Journal of Applied Engineering and Technological Science (JAETS)

Analysis and Identification of Non-Impact Factors on Smart City Readiness Using Technology Acceptance Analysis: A Case Study in Kampar District, Indonesia

Sun, 10 Dec 2023 00:00:00 +0700

Most countries start to implement Smart Cities as an innovation for urban strategy. However, not all Smart Cities implementations worked and were implemented well, because the community still not ready for the implementation of Smart City. The aim of this research is to investigate community readiness and finding low impact factors for implementing smart cities based on 5 factors, namely AU, PEOU, ATU, BIU, and PU. This research was using a qualitative study with the Technology Acceptance Model approach (TAM) to investigate the relationship between 5 factors. Based on the results of data distribution, there are 2 clusters, namely people who know about public service applications and people who are not aware of any public service applications. Furthermore, there are 3 tests conducted in this research namely T-test, F-test and Coefficient Determination Test to determine the impact and influence of the relationship between each factor. However, from the results of the t-test it was found that there were 2 relationships that had no impact because the t-count was negative and the 2 relationships between these factors were between PU - AU and AU - PU.

Comparison Between Face and Gait Human Recognition Using Enhanced Convolutional Neural Network

Fatima Esmail Sadeq, Ziyad Tariq Mustafa Al-Ta'i — Sun, 10 Dec 2023 00:00:00 +0700

Identifying people at distance is an important task in daily life Because of the increase in terrorism. Biometrics is a better solution to overcome personal identity problems, and this applies to soft biometrics also. Soft biometric are features that can be extracted remotely and do not require cooperation with people. This paper introduces a comparison between human face recognition and human gait recognition using soft biometric features. Nine face attributes and nine gait attributes are taken from a dataset built by researchers. The constructed dataset is composed from (66) videos for (33) persons. Features are extracted using Haar and MediaPipe methods. The extracted features are classified using enhanced convolutional neural network. This work achieves an accuracy of 95.832% in human face recognition and an accuracy of 89.583% in human gait recognition. From the above results it turns out that the proposed method achieved promising results with regard to Recognize people remotely

Phytoarchitecture Design Requires a Plant Selection Framework to Combat Air Contaminants in Building Areas Sustainably

Harida Samudro, Ganjar Samudro, Sarwoko Mangkoedihardjo — Sun, 10 Dec 2023 00:00:00 +0700

Empowerment of plants to maintain the indoor and outdoor air quality of a building area promises occupant health and sustainable use of the building. In supporting plants' functional role, this study proposes a novel approach for a general framework for selecting plants. The method to achieve the objectives of this study was based on previous empirical studies conducted in various places under different environmental quality conditions. The essential findings of the selected literature became part of the technical feasibility process in selecting plants. Significant results indicate the mechanism of controlling airborne contaminants by plants through aerial parts and growth media. Gaseous pollutants can be absorbed along with carbon dioxide absorption, while particulate matter is deposited on the leaf surface. Some other contaminants enter the plant growth medium, which plants can process with microbes in the root zone. The use of plants for indoor and outdoor phytoremediation is various plant species, sourced and selected from a retrospective study, locally available and standard plants, and popular plants. These findings were developed to include assessments of contaminant-plant interactions and plant-specific experiments. The implications of the plant selection framework can be one of the promising methods in designing sustainable building phytoarchitectures.

Smart Home System With Battery Backup and Internet of Things

Sun, 10 Dec 2023 00:00:00 +0700

This research enhances Smart Home Systems by integrating an Automatic Transfer Switch (ATS) for seamless power source switching between the grid and a backup battery, ensuring uninterrupted operation during power disruptions. An Automatic Battery Charging (ABC) system optimizes battery charging based on its condition, improving energy storage and efficiency. The system provides on-site electrical equipment control and sensor data access via a Human Machine Interface (HMI). Remote monitoring and control through the Blynk app offer convenience. Additionally, an energy consumption estimation feature allows users to estimate billing costs, with the Battery State of Charge (SoC) indicating the remaining battery capacity. Hardware testing showed the system's reliability with a 2-4 second ATS response and ±2-second ABC response. This research offers homeowners reliable power continuity and energy optimization. It contributes to IoT-based smart home systems, demonstrating ATS and ABC effectiveness, advancing both theory and practice for modern smart living.

Trends in E-Commerce And Social Media Research in Asia: Five Years of Scientometric and Content Analysis

Hilmi Aulawi , Novie Susanti Suseno, Khairul Hafezad Abdullah — Sun, 10 Dec 2023 00:00:00 +0700

This paper aims to provide scientometric and content analysis towards e-commerce and social media research in Asia. The Web of Science (WoS) and Scopus databases were used in searching for articles. There were 884 (433 publications from the Web of Science and 451 articles from Scopus) papers analysed. Based on the analysis of two databases, the number of publications from the Web of Science database showed a significant increase yearly. In comparison, the Scopus database showed fluctuating growth every year. One of the countries that enormously contributed to the research was China, which can be seen from the author’s and country’s analyses. The ACM International Conference Proceeding Series was the most contributing conference proceedings. Based on the keyword results, there are five keywords that appear most often. Referring to the data from the last two years (2021–2022), the keywords “machine learning” and “social media marketing” are the most frequently used. These two keywords are most often associated with e-commerce and social media keywords. These findings are expected to provide a substantial understanding towards e-commerce and social media research, particularly in the Asian region. This paper will assist researchers in understanding new topics, collaborating with other researchers, and determining relevant sources and countries. Analysed keywords can inspire new research. Consequently, researchers can learn about new technology, societal changes, and impending challenges and opportunities by tracking keyword trends.

Moving Object Activator in Background Subtraction Algorithm for Automatic Passenger Counter System in Public Transportation

Sun, 10 Dec 2023 00:00:00 +0700

Buses are the most used transportation by people in Indonesia when traveling between cities. However, to improve passenger comfort, a tool is required to determine the number of passengers on the bus. This research presents an automated system to count the number of passengers based on a background subtraction algorithm and moving object activator. This research aims to provide the number of passengers based on video images taken from the entranceway. The system is built with a camera, Raspberry Pi, and LCD. The APC system starts the counting process by removing the video background image from the captured object image. The entry and exit direction of the object is determined using the concept of moving object activator. The experiments were applied in several scenarios to determine the robustness of the system. The best APC performance was achieved when the system is positioned perpendicularly above the entranceway at a height of 230 cm and a light intensity of 800-1000 lux. Meanwhile, the moving object activator is effective in supporting the system's performance to determine the passenger's direction. In this scenario, the results stated that the accuracy of APC system performance reached 93.8%.

The Impulse Buying of Gen Z When Using E-Wallet In Indonesia

Lim Sanny, Gilang Rafi Chandra, Kitsy Chelles, Laurent Angelica Santoso — Sun, 10 Dec 2023 00:00:00 +0700

This research aims to analyze the impulse buying of Gen Z when using e-wallets in Indonesia. This type of research is quantitative research using the Partial Least Squares Structural Equation Modeling (SEM) framework. The sampling technique involved convenience sampling with a total of 393 Gen Z e-wallet users in Indonesia who had been surveyed online. Theoretical implications in this study are implementing the S-O-R framework on e-wallet enriches. This research provides a new perspective using Generation Z as a research subject. The results found in this study revealed that the model could explain 60.2% of variance satisfaction and 5.9% of impulse buying. In addition, the factors that encourage satisfaction include perceived interactivity, perceived risk, and subjective norms that significantly affect satisfaction with a small effect. Perceived usefulness is the most significant factor with a substantial impact that positively influences satisfaction. This satisfaction is proven to control impulse buying positively, but it can only be explained in a small part. The research's practical implication is that these results can provide input for e-wallet development companies to satisfy Gen Z using e-wallets in impulse buying.

Performance Evaluation of Logistics Service Provider (LSP) in FMCG Companies Using Physical Distribution Service Quality (PDSQ) Dimension: Case Study

Evi Yuliawati, Clora Brilliana, Nur Rahmawati, Dian Trihastuti — Sun, 10 Dec 2023 00:00:00 +0700

Increasingly fierce business competition requires companies to be able to meet customer desires on time, in the right amount and in the right quality. FMCG XXX Company is a skin care product manufacturing company that focuses on production activities. Therefore, the company transfers product distribution activities to customers (distributors) to Logistics Service Providers (LSP). FMCG XXX hold consumer’s satisfaction in the highest regard. Along with the increasing number of activities being diverted, FMCG XXX companies aim to evaluate LSP performance through appropriate indicators. The indicators evaluated in this study adopt dimensions in Physical Distribution Service Quality (PDSQ) which consist of three dimensions, namely timeliness, availability and condition. Based on the results of the study, 13 indicators were obtained that FMCG XXX Company used to evaluate LSP performance. In general, around 69% of LSP's actual performance has been able to meet the targets set by the company, where two indicators are able to exceed the target and seven indicators are in accordance with the target. However, the company still has to focus on four indicators that have negative deviations because the actual performance value is less than the target set by the company. Negative indicators come from the availability and condition dimensions. The company coordinates with LSP to design improvement projects in order to improve actual performance according to the company's targets.

Analysis of a Balanced Short Circuit on a Sulbagsel Electrical System by Using The FFA-ABC Method Approach

Haripuddin Haripuddin, Al Imran, Zulhajji Zulhajji, Muliaty Yantahin — Sun, 10 Dec 2023 00:00:00 +0700

Fault studies are an important part of electrical power system analysis. The purpose of this study was to determine the amount of line current at the point of disturbance when a three-phase balanced fault occurs in the real Sulbagsel electrical system. In this paper, a new hybrid FFA-ABC (Fruit Fly Algorithm-Artificial Bee Colony) method is proposed, which is one of the new methods used to calculate balanced three-phase short circuit currents in electric power systems, especially in the real Sulbagsel electrical system of South Sulawesi, Indonesia. The real electricity system of Sulbagsel was chosen as the research object because this system consists of 15 generators, 44 buses, 52 transmission lines, and 29 load buses with system voltages varying from 30 kV, 70 kV, 150 kV, and 275 kV so that this system is included in the complex system category. To test the effectiveness of the proposed FFA-ABC method, it was implemented on a real electric power system, namely the Sulbagsel System. In addition, it can also be applied to IEEE electrical systems or other real electric power systems. The results of the new hybrid FFA-ABC method of the balanced short circuit analysis of the Sulbagsel electrical system are then compared using the FFA method, the ABC method, and the deterministic method (in this case, the bus impedance matrix (BIM) method). The simulation results obtained show that the FFA-ABC hybrid method is able to solve the problem of balanced short circuit analysis in the Sulbagsel electrical system in South Sulawesi, Indonesia which the largest fault current occurs when the fault is close to the slack bus generator (bus 2=Sidrap) of 18.9697 p.u, and the smallest fault current occurs when the fault is farthest from the slack bus generator (bus 44=Poso) of 0.8457 p.u.

Soft Skills and Hard Skills Needed In Industry 4.0 For Electrical Engineering Students

Sun, 10 Dec 2023 00:00:00 +0700

This paper investigates industry perceptions regarding the relevance of the courses in the curriculum provided by universities and whether they are in accordance with the demands that exist in the industrial world. This research uses a descriptive survey method. The sample taken from electrical engineering alumni who worked in industry was 242 people. This research uses a stratified random sampling technique. The instrument used is a 4-point scale. The assessment was rated Strongly Agree, Agree, Disagree, and Strongly Disagree with weights of 4, 3, 2, and 1, respectively. Three vocational education experts validated the instrument. Reliability was performed with Aiken-V. The questionnaire contains 34 questions. Research data was analysed by percentage, mean, and standard deviation. The findings show that the soft skills and hard skills achieved in each course in the curriculum are arranged in accordance with industry needs. Soft skills and hard skills in the curriculum can be provided to students so that they can look for related jobs.

Fuzzy Genetic Particle Swarm Optimization Convolution Neural Network Based On Oral Cancer Identification System

R Dharani, S. Revathy, K. Danesh — Sun, 10 Dec 2023 00:00:00 +0700

Oral cancer is the eighth most common type of cancer in the world. Every year, 130,000 people in India die from mouth cancer. Getting a diagnosis from a clinical exam by skilled doctors and a biopsy takes time. When a problem is found early, it is always easier to treat. The primary goal of this work is to recognise disease-affected oral regions in a given oral image and classify the oral cancer disorder. This study employs unique Deep Learning algorithms to detect the location of disease-affected oral areas. This work employs the most effective feature extraction techniques, including appearance and patter-based features. Following feature extraction, the Bee Pulse Couple Neural Network (BeePCNN) algorithm is used to choose the best feature. Finally, Deep Learning is used to classify these attributes. An innovative FGPSOCNN reduces the computational complexity of CNN. On an additional real-time data set from Arthi Scan Hospital, a secondary evaluation is conducted. The experimental results indicate that the innovative FGPSOCNN performs better than existing methods.

Data Governance Model For Nation-Wide Non-Profit Organization

Adi Suryaputra Paramita, Harjanto Prabowo, Arief Ramadhan, Dana Indra Sensuse — Sun, 10 Dec 2023 00:00:00 +0700

According to Connolly's (2017) research, the context of nonprofit organizations exhibits variations when compared to commercial organizations or businesses, as supported by Zhang's (2010) study. Hence, it is imperative for both theoretical and empirical studies to contribute towards enhancing our comprehension of the strategy, implementation, and utilization of information systems in the specific context of nonprofit organizations. The investigation of information systems within the context of non-profit organizations offers a promising avenue for advancing the field of information systems research. This study focuses on the development of an information systems framework using the soft systems methodology, which has already been established. One opportunity for the advancement of information systems in non-profit organizations lies in the establishment of a comprehensive framework that facilitates adoption and is accompanied by robust data governance. This framework enables the analysis of data and the generation of valuable insights, thereby contributing to the development of information systems in the non-profit sector. The choice of data governance was informed by Zhang's (2010) research, which demonstrated that non-profit organizations face significant obstacles in the form of privacy and data security concerns. Furthermore, it is apparent that the preservation of data privacy plays a crucial role in the acceptance and utilization of information systems within non-profit entities. This research aims to contribute to the resolution of the issue by establishing a governance framework for information systems that effectively communicates to users the absence of data privacy risks associated with the systems employed by organizations. The objective of this study is to create a data governance model that will fill the research gap mentioned earlier and make a valuable contribution to the field of information systems research. The formation of the data governance model will involve the integration of soft systems methodology and the DAMA framework. The outcome of this study will be a data governance model specifically designed for a nationwide non-profit organization that utilizes microservices as its cutting-edge technology.

Application of E-Glass Jute Hybrid Laminate Composite with Curved Shape on Compressive Strength of Cylindrical Column Concrete

Achmad Jusuf Zulfikar, Mohd Yuhazri Yaakob, Rahmad Bayu Syah — Sun, 10 Dec 2023 00:00:00 +0700

This study provides a better understanding of reinforcing cylindrical concrete columns (CCC) using a hybrid laminated composite material (HLC) composed of jute and e-glass fibers, including the influence of layer quantity on strength and a comparison with previous research. The utilization of these alternative materials may lead to the development of novel and efficient solutions for constructing durable and robust structures. The primary objectives of this research are to assess the effects of employing HLC as a reinforcing layer on CCC compressive strength, optimize the reinforcement process by selecting appropriate layer sequences and types, and analyze the type of fiber damage in relation to the strength of HLC composite material. The materials utilized in this study encompass woven jute fabric sheets, e-glass fiber sheets, and epoxy resin. Compressive strength testing was conducted following ASTM C39 standards. Specimen variations were based on the number and type of reinforcing layers. The results revealed that CCC compressive strength increased by up to 100% with the application of up to three layers of jute compared to an unlayered specimen. Furthermore, CCC compressive strength experienced a remarkable enhancement of up to 150% with the incorporation of HLC composite. Hence, the implementation of HLC demonstrates significant potential for augmenting the strength of concrete structures.

The Business Digitalization Model to Enhance Family Business Performance

Ricky Adrian Gunawan, Andika Pratama, Arta Moro Sundjaja — Sun, 10 Dec 2023 00:00:00 +0700

Digital transformation is changing how an organization uses technology, enterprise, processes, and people to improve performance and adopt a new business model. Digital transformation drives value through shared industry information and insights. However, several limitations, such as differences in culture and perspective between owners and management, impede the implementation of this digitalization. Harmonization is needed to keep the changes from getting out of hand because the digitization process is affected not only by sales, marketing, and finance processes but also by the culture of digitization. This study investigates the impact of digital organizational culture, digital capabilities, digital technology, and digital transformation strategy on the performance of family businesses. The research design is a quantitative study with structural equation modeling. The population in this study cannot be determined due to limited access to the association. The sample size was calculated using the G-Power calculator with an effect size of 0.15, an alpha level of 0.05, and a power of 0.80. According to the calculation, there are 43 respondents in the study. The sampling technique is convenient sampling. The survey was distributed online using questionnaires sent via email. The data analysis software is Smart PLS 4.0.8.5. The result shows that the organization's culture plays a big part in improving performance. The organization's culture strongly affects business digitization and technology value development. The value of developed technology affects business performance. Therefore, the top management needs to prioritize the IT development that transforms their business and improves business performance

A Conceptual Aquila Merged Arithmetic Optimization (AIAO) Integrated Auto-Encoder Based Long Short Term Memory (AUE-LSTM) For Sentiment Analysis

Sangeetha J, Maria Anu V — Sun, 10 Dec 2023 00:00:00 +0700

Sentiment analysis is a branch of analysis that uses disorganized written language to infer the opinions and emotions of people's critiques and attitudes toward entities and its features. In order to produce acceptable results, the majority of sentiment analysis models that employ supervised learning algorithms require a large amount of labeled information during the training stage. This is typically costly and results in significant labor expenses when used in practical applications. In this study, an intelligent and unique sentiment prediction system is developed for accurately classifying the positive, negative, and neutral comments from the social media dataset. Data preprocessing, which entails noise reduction, tokenization, standardization, normalization, stop word removal, and stemming, is done to ensure that the data is of a high enough quality for efficient sentiment prediction and analysis. The preprocessed data is then used to extract a mix of features, including hash tagging, Bag of Words (BoW), and Parts of Speech (PoS). Consequently, in order to choose the best features and speed up the classifier, a new hybrid optimization method called Aquila merged Arithmetic Optimization (AIAO) is used. Furthermore, an Auto-Encoder based Long Short Term Memory (AuE-LSTM), an innovative and clever ensemble learning technique, is used to precisely anticipate and classify user feelings based on the chosen data. This study uses a variety of open source social media datasets to evaluate the performance of the suggested AIAO integrated AuE-LSTM model.

Social Consumer Relation Management Using Social Media as a Marketing Scheme in University

Sun, 10 Dec 2023 00:00:00 +0700

Social media or called as ‘socmed’ is an online media that is frequently used by humans to improve in activities, which include promoting study programs of university. Most universities require a marketing scheme to promote certain study programs by using socmed. This study aims to investigate marketing strategies of universities using social costumer relation management (SCRM) and socmed. This is a qualitative study with a narrative model design. This study included about 2000 students from universities in South Sumatra, Lampung, Bengkulu, and Bangka Belitung, Indonesia. The samples were collected by proportional random sampling approach to attain the number of informants and data were collected through online interview questionnaire. Furthermore, the data were analyzed using coding approaches (open, axial, and selective coding) and value stream analysis. The findings revealed that SCRM can be an alternative marketing scheme for universities that utilize socmed to disseminate information owing to access easiness, complete and updated information, and attractive appearance.

A Smart Kumbung For Monitoring and Controlling Environment in Oyster Mushroom Cultivation Based on Internet of Things Framework

Sun, 10 Dec 2023 00:00:00 +0700

Oyster mushroom (Pleurotus Ostreatus) is a fungus-like plant that is often cultivated in Indonesian agriculture. Oyster mushroom is raised by manipulating environmental parameters, so that it can grow in the provided Kumbung. Oyster mushroom requires a temperature that is used, which ranges from 23°-28°C, for humidity used between 70% -90% and for light intensity it requires light of ±300 lux. In this study, a system was designed to carry out automatic monitoring and control in real time based on the Internet of Things (IoT) which integrates DHT22 humidity and temperature sensors, BH1750 light intensity sensors and NodeMCU as a microcontroller with measurement results sent to the Firebase database. In addition, a water pump connected to the sprayer nozzle is installed in this system to maintain the humidity of the oyster mushroom curd. From the test results, the system can work automatically to stabilize temperature, humidity, and light intensity according to the ideal parameters.

Heart Disease Prediction based on Physiological Parameters Using Ensemble Classifier and Parameter Optimization

Agung Muliawan, Achmad Rizal, Sugondo Hadiyoso — Sun, 10 Dec 2023 00:00:00 +0700

This study describes the prediction of heart disease using ensemble classifiers with parameter optimization. As input, a public dataset was taken from UCI machine learning repository, which refers to the dataset at UCI Machine learning. The dataset consists of 13 variables that are considered to influence heart disease. Particle swarm optimization (PSO) was used for feature selection and principal component analysis (PCA) for feature extraction to reduce the features' dimensions. The application of parameter optimization on several machine learning methods such as SVM (Radial Basis Function), Deep learning, and Ensemble Classifier (bagging and boosting) to get the highest accuracy comparison. The results of this study using PSO dimensionality reduction in the public dataset of heart disease resulted in the slightest accuracy compared to PCA. In contrast, the highest accuracy was obtained from optimizing Deep Learning parameters with an accuracy of 84.47% and optimization of SVM RBF parameters with an accuracy of 83.56%. The highest accuracy in the ensemble classifier using bagging on SVM of 83.51%, with a difference of 0.5% from SVM without using bagging.

A Modified KLEIN Encryption-based Knight Tour for Image Encryption

Emaan Oudha Oraby, Rafeef Mohammed Hamza — Sun, 10 Dec 2023 00:00:00 +0700

The security considerations should be balanced with the specific use case and potential risks associated with using lightweight encryption. The security offered by lighter encryption algorithms could not be as high as that offered by heavier encryption techniques. In this paper, a Modified KLEIN Algorithm is proposed for image encryption based on the Knight Tour movement in Chessboard. The required key generation is represented by inputting an image as a key image and then applying a specific operation based on knight tour movement to produce a key scheduled in the proposed encryption algorithm. The movement of Knight Tour applied in modifying the proposed algorithm for increasing security. The experimental results explain the efficiency of a modified algorithm when comparing the histogram of the input image with the encrypted image also the correlation is tested before and after encryption in three directions horizontal, vertical, and diagonal which explains there are very low values of them in all directions. The similarity test also explains there are high differences between the plain and encrypted images. The chessboard movement might be useful when used with another encryption algorithm which increases the confidentiality of transferring data. The contribution of this work is the use of an image as a key for encryption with a specific planning method which helps in key management.

Student Acceptance Study of PhET Simulation with an Expanded Technology Acceptance Model Approach

Sun, 10 Dec 2023 00:00:00 +0700

Phet simulation is a computer-based practicum simulation medium that helps to increase students’ engagement and understanding through concept visualization. Even though there have been numerous studies on the Technology Acceptance Model (TAM) related to Phet simulation, the TAM study of Phet simulation combined with certain learning methods is rarely observed. We propose an advanced version of TAM by including the external variables of system quality, user characteristics, and instructor. This study is a quantitative design with a descriptive, explanatory type. This model was tested using an online questionnaire disseminated to 49 students that finished taking basic natural sciences subjects using Phet simulation based on problem-based learning. The result showed that system quality has an effect on perceived usefulness, perceived ease of use has an effect on behavioral intention of use, behavioral intention of use has an effect on actual usage, and there is no relationship between instructor quality on perceived usefulness. Grit on perceived ease of use, learner anxiety, and perceived usefulness on the behavioral intention of use. These findings have implications regarding user acceptance of Phet simulation combined with certain learning methods, specifically problem-based learning.

The Construction of Affordable Housing in Developing Countries: a Scientometric Review

Deddy Purnomo Retno, Harmiyati Harmiyati — Sun, 10 Dec 2023 00:00:00 +0700

The presence of affordable housing in developing countries is a crucial issue in order to fulfill the primary need for housing in a large market segment, especially people whose income is below the average household income. In contrast to developed countries, the development of studies on affordable housing construction (AHC) in developing countries has not been well mapped. This certainly creates many gaps in determining the direction of future developments, especially related to the studies that will be carried out. This study tries to map the development of scientific publications related to AHC in developing countries, from 1983 - 2021. Using scientometric techniques and VosViewers as a data processing tool, 116 publications that meet the given criteria have been identified. The findings of this study reveal a mapping of publications organized by country, organization, research outlet, author, document citation, and main research area. During the observation period, most research focused on developing nations, affordability, sustainable development, the construction industry, and the developing globe. In addition, the results of this study also successfully mapped opportunities for future research focuses related to building materials, affordable housing, low-income populations, decision-making, and structural design. In conclusion, this study highlights the need for further research on affordable housing development in developing countries to guide policy makers and researchers in developing affordable housing solutions that meet the housing needs of low-income households.

Multi-Object Detection Using YOLOv7 Object Detection Algorithm on Mobile Device

Patricia Citranegara Kusuma, Benfano Soewito — Sun, 10 Dec 2023 00:00:00 +0700

This research discusses the importance of enhancing real-time object detection on mobile devices by introducing a new multi-object detection system that uses the quantified YOLOv7 model. Focusing on the complexities of food item detection, particularly in diverse and intricate contexts, our study uses a dataset that includes five food classes. By investigating the influence of data quantity on the detection model, we demonstrate the superiority of larger datasets in both YOLOv5 and YOLOv7. In addition, our comparison shows that YOLOv7 has better precision, recall, and F1-score values compared to YOLOv5. The crucial methodological contribution lies in the successful quantification of the YOLOv7 model, reducing the model size from 28.6 KB to 14.3 KB and enabling seamless mobile application development. This high-performance mobile application displays a real-time interface response time of 235ms, with precision, recall, and F1-score values of 0.923, 0.9, and 0.911, respectively. Beyond the practical implications for informed dietary choices and improved health outcomes, our study develops object detection techniques theoretically, offering valuable insights that can be applied across various domains and emphasizing the potential impact of our approach on both theory and practice

The Role of Artificial Intelligence in Diagnosing Heart Disease in Humans: A Review

Tamara Hameed Yousiaf, Mohammed S. H. Al-Tamimi — Sun, 10 Dec 2023 00:00:00 +0700

The electrical activity of the heart and the electrocardiogram (ECG) signal are fundamentally related. In the study that has been published, the ECG signal has been examined and used for a number of applications. The monitoring of heart rate and the analysis of heart rhythm patterns, the detection and diagnosis of cardiac diseases, the identification of emotional states, and the use of biometric identification methods are a few examples of applications in the field. Several various phases may be involved in the analysis of electrocardiogram (ECG) data, depending on the type of study being done. Preprocessing, feature extraction, feature selection, feature modification, and classification are frequently included in these stages. Every stage must be finished in order for the analysis to go smoothly. Additionally, accurate success measures and the creation of an acceptable ECG signal database are prerequisites for the analysis of electrocardiogram (ECG) signals. Identification and diagnosis of various cardiac illnesses depend heavily on the ECG segmentation and feature extraction procedure. Electrocardiogram (ECG) signals are frequently obtained for a variety of purposes, including the diagnosis of cardiovascular conditions, the identification of arrhythmias, the provision of physiological feedback, the detection of sleep apnea, routine patient monitoring, the prediction of sudden cardiac arrest, and the creation of systems for identifying vital signs, emotional states, and physical activities. The ECG has been widely used for the diagnosis and prognosis of a variety of heart diseases. Currently, a range of cardiac diseases can be accurately identified by computerized automated reports, which can then generate an automated report. This academic paper aims to provide an overview of the most important problems associated with using deep learning and machine learning to diagnose diseases based on electrocardiography, as well as a review of research on these techniques and methods and a discussion of the major data sets used by researchers.

Depend Ability Analysis on CPS Using Machine Learning Techniques

Krishna Narayanan S, Dhanasekaran S, Vasudevan V — Sun, 10 Dec 2023 00:00:00 +0700

A Cyber-Physical System (CPS) is an entity that effortlessly monitors and controls physical operations by integrating computational and physical elements. Dependability on the CPS application program and also proposes a real-time analysis approach to CPS application dependability based upon Artificial Intelligence and Machine Learning (ML). For starters, pick complicated networks to determine tips within the system topology on the CPS application process. Unsupervised mastering category by a quick density clustering algorithm to classify the value of nodes could be successfully put on the crucial analysis of nodes in CPS application program as well as help support the setting up of CPS software phone Secondly, a real-time CPS dependability automated internet analysis technique is suggested. Unreliable methods are able to imply big losses, each monetarily in addition to within man's life. On a good mention, CPS has information like the main component of the operation of theirs. The prevalence and availability of information demonstrate a brand-new chance to change the methods within what dependability evaluation continues to be usually performed. This process utilizes printer mastering tips to create an analysis framework, design, and style an internet queuing algorithm, as well as put into action real-time internet evaluation and analysis of CPS dependability. Preventive steps make sure that the device works ordinarily as well as with no interruption, which significantly betters method dependability. Last but not least, simulation final results verify the usefulness of the analysis technique and the broad application prospects of its.

Examining the Practicality of Mobile-Based Gamification Assessment in Electrical Machine Course: A Study in Industrial Electrical Engineering

Sun, 10 Dec 2023 00:00:00 +0700

Mobile-based gamification learning is increasingly popular for enhancing student interest and motivation in the learning process, including its application in evaluating learning outcomes. However, the practicality of its use, from the perspective of students as users, needs further evaluation. This study aims to assess the practicality of mobile-based gamification assessment (M-BGA) in evaluating student learning outcomes in the Electrical Machine Course (EMC). M-BGA was developed using Kahoot! application. A survey-based quantitative research design was employed, using the Practicality Assessment Instrument (PAI) as the data collection tool. The practicality of M-BGA was evaluated based on student assessments after its implementation in an EMC. This research involved 83 second-year students from the Industrial Electrical Engineering Study Program, Faculty of Engineering, Universitas Negeri Padang, Indonesia. The results indicate a high level of practicality in several aspects. The Ease of Use aspect scored 92.23% (highly practical), the Reliability aspect scored 89.82% (highly practical), the Student Engagement aspect scored 88.55% (highly practical), and the Learning Impact aspect scored 90.19% (highly useful). Overall, based on student responses, the M-BGA proved to be highly practical in evaluating student learning outcomes in the EMC. M-BGA can serve as an alternative approach for assessing student learning outcomes with an innovative approach.

The Readiness of Minang Weaving Towards Halal Fashion Adoption: A Clustering Analysis of Toe Framework

Ratni Prima Lita, Nilda Tri Putri, Meuthia Meuthia, Devi Yulia Rahmi — Sun, 10 Dec 2023 00:00:00 +0700

This research focuses on clustering the readiness of halal practice implementation in Minang weaving businesses, then based on the clusters that are very ready, it is then carried out the identification of the determinant factors of the readiness to implement the Halal Assurance System in Minang weaving businesses. Sampling was conducted using the non-probability sampling technique through purposive sampling from 103MSMEs of weaving in West Sumatra. The results of this research through K-means cluster analysis show that the grouping of Technology, Organization, Environment (TOE ) halal adoption in weaving MSMEs consists of 6 readiness groups, namely termination (15 MSMEs), maintenance (43 MSMEs), action (23 MSMEs), preparation (13 MSMEs), contemplation ( 2 MSMEs), pre-contemplation (7 MSMEs) spread across five cities/regencies in West Sumatra (Sawahlunto City and 50 Cities Regency which consists of 34 MSMEs respectively, 30 MSMEs from Tanah Datar Regency, 3 MSMEs from Sijunjung Regency, and 2 MSMEs from Payakumbuh City). Of the three determinants of TOE adoption tested, MSMEs' perceptions of technology readiness through the dimensions of compatibility and perceived benefits are higher than organizational and environmental factors. This research has theoretical and practical implications through exploration of the TOE model so that the results of this study can become a further research agenda in encouraging halal certification through developing a halal adoption strategy based on HAS 23000 in weaving MSMEs in West Sumatra.

Extraction of Green Grass Jelly Leaves as An Alternative Biopolymer in Polymer Flooding

Dita Putri Purnama, Anas Hidayat, Muhammad Khairul Afdhol, Fiki Hidayat — Sun, 10 Dec 2023 00:00:00 +0700

Biopolymer from Green Grass Jelly Leaves attracts attention due to its friendlier environmental profile and cost-effectiveness in providing raw materials. This research aims to explore the potential of biopolymers from Green Grass Jelly Leaves as an alternative to synthetic polymers in an effort to increase oil recovery involving sequential pretreatment, extraction, and characterization stages to obtain essential pectin compounds. This experiment centers on a biopolymer sourced from Green Grass Jelly Leaves, involving sequential steps of pretreatment, extraction, and characterization to obtain essential pectin compounds. Characterization employed scanning electron microscopy (SEM) and Fourier-transform infrared spectroscopy (FTIR). The recorded peak viscosity for Green Grass Jelly Leaves biopolymer was 2.04 cp at 3000 ppm concentration, contrasting with pectin's 1.98 cp viscosity. In comparison, industrial biopolymer Xanthan Gum displayed significantly higher viscosity at 95.01 cp for 3000 ppm concentration. Thermal stability assessment under reservoir conditions (30°C and 60°C) demonstrated that Green Grass Jelly Leaves biopolymer pectin exhibited peak viscosities of 55.29 cP and 51.77 cP at 3000 ppm concentration, respectively. These results show that the comparison between biopolymer and synthetic polymer is not too far and there is an increase in viscosity as the concentration increases, which can increase sweep efficiency.

Standardscaler's Potential in Enhancing Breast Cancer Accuracy Using Machine Learning

Febri Aldi, Febri Hadi, Nadya Alinda Rahmi, Sarjon Defit — Sun, 10 Dec 2023 00:00:00 +0700

The major consequence of breast cancer is death. It has been proven in many studies that machine learning techniques are more efficient in diagnosing breast cancer. These algorithms have also been used to estimate a person's likelihood of surviving breast cancer. In this study, we employed machine learning algorithms to predict breast cancer. A total of 569 breast cancer datasets were obtained from kaggle sites. Some of the machine learning algorithms that we use are K-Nearest Neighbor (KNN), besides Random Forest (RF), there is also Gradient Boosting (GB), then Gaussian Naive Bayes (GNB), Vector Support Machine (SVM), and then Logistic Regression (LR). Before algorithms were used to train and test breast cancer datasets, StandardScaler was leveraged to transform training datasets and test datasets for improved algorithm performance. As a result of this utilization, the performance measurement carried out succeeded in producing high accuracy. The highest results were obtained from the Logistic Regression algorithm with an accuracy value of 99%. The value of precison is 99% benign, and 100% malignant. The recall results are 100% benign, and 98% malignant. The F1-Score results show 99% benign, and 99% malignant. It is hoped that this research can help the medical party to determine the next step in dealing with breast cancer.

Intensive Malware Detection Approach based on Data Mining

Israa Ezzat Salem, Karim Hashim Al-Saedi — Sun, 10 Dec 2023 00:00:00 +0700

Malicious software, sometimes known as malware, is software designed to harm a computer, network, or any of the connected resources. Without the user's knowledge, malware can spread throughout their computer system. Malware is typically disseminated via online connections and mobile devices. While malware has always been a problem in the digital age, its effects have gotten increasingly serious. Traditional malware detection methods seek to locate specific malware samples and families to recognize harmful codes and can be located using traditional signature- and rule-based detection methods. The research focuses on developing malware detectors using data mining techniques. The proposed method outlined below sets itself apart by emphasizing the processing of malware behaviors significantly dependent on aspects. Finding more dependable intelligent detecting techniques is a crucial component of this paper. In order to identify the cluster of the most essential malware features and use decision tree classifiers for malware detection, the study, a common methodology for creating malware detectors based on data mining, is implemented and investigated. Our approach can identify the most significant features of malware that can significantly determine and detect a malware code.

Priority RPL for IOT Based Smart Manufacturing Industries

Krishna Priya M, Angeline Prasanna G — Sun, 10 Dec 2023 00:00:00 +0700

A routing protocol used in heterogeneous transport networks for low-power, lossy networks. This is a routing protocol for wireless networks. This protocol follows the same specifications as Zigbee, 6 lopan is IEEE 802.15. 4 Enables both many-to-one and one-to-one communication. To address the need for enhancing in this study proposes a novel methodology called RPL-PG (Routing Protocol for Low-Power and Lossy Networks Priority Generation). Initially sensors like Temperature, Humidity, Vibration, Proximity, Gas and Current Monitoring Sensors are used for smart manufacturing. Consequently, Destination Oriented Directed Acyclic Graph (DODAG) is used for RPL configuration. Based on selected RPL configuration the priority is generated using assign priority count and priority-based queuing. Finally, Fuzzy rules are used to select the RPL path and then update the DODAG finally reached the destination. The study involves setting up a simulated environment using appropriate tools, such as MATLAB. Experimental findings evaluate and compares performance measures, such as Energy Consumption, Network Life Time, Packet Loss Ratio, Packet Delivery Ratio (PDR), E2E Delay, and Network Throughput. The Energy Consumption of the proposed RPL-PG method achieves 43.6 % lower than 38 % and 35.8 % in terms of OMC-RPL and RMA-RP respectively.

Fetal Heart Disease Detection Via Deep Reg Network Based on Ultrasound Images

S. Magesh, P.S. RajaKumar — Sun, 10 Dec 2023 00:00:00 +0700

Congenital heart disease (CHD) is the most prevalent congenital ailment. One in every four newborns born with serious coronary artery disease will require surgery or other early therapy. Early identification of CHD in the fetal heart, on the other hand, is more critical for diagnosis. Extracting information from ultrasound (US) images is a difficult and time-consuming job. Deep learning (Dl) CNNs have been frequently utilized in fetal echocardiography for CAD identification to overcome this difficulty. In this work, a DL based neural network is proposed for classifying the normal and abnormal fetal heart based on US images. A total of 363 pregnant women between the ages of 18 and 34 weeks who had coronary artery disease or fetal good hearts were included. These US images are pre-processed using SCRAB (scalable range based adaptive bilateral filter) for eliminating the noise artifacts. The relevant features are extracted from the US images and classify them into normal and CHD by using the deep Reg net network. The proposed model integrates the Reg net -module with the CNN architecture to diminish the computational complexity and, simultaneously, attains an effectual classification accuracy. The proposed network attains higher accuracy of 98.4% for the normal and 97.2% for CHD. To confirm the efficiency of the proposed Reg net is compared to the various deep learning networks.

Improving Text Summarization Quality by Combining T5-Based Models and Convolutional Seq2Seq Models

Sun, 10 Dec 2023 00:00:00 +0700

In the natural language processing field, there are several sub-fields that are very closely related to information retrieval, such as the automatic text summarization sub-field. obtained from the convolutional T5 and Seq2Seq models in summarizing text on hugging faces found features that can affect text summary such as upper- and lower-case letters which have an impact on changing the understanding of the text of the document. This study uses a combination of parameters such as layer dimensions, learning rate, batch size, and the use of Dropout to avoid model overfitting. The results can be seen by evaluating metrics using ROUGE. This study produces a value of ROUGE-1 on 4 documents that are tested which produces an average of 0.8 which is the optimal value, for ROUGE-2 on 4 documents that are tested which results in an average of 0.83 which is an optimal value while ROUGE-L on 4 documents conducted tests that produce an average of 0.8 which is the optimal value for the summary model.

Economic Analysis of Rooftop Based On-Grid and Off-Grid Photovoltaic Systems in Equatorial Area

Sun, 10 Dec 2023 00:00:00 +0700

Through a thorough analysis using Net Present Cost (NPC) over a 20-year period, this research presents a comprehensive and economically optimized solar panel design methodology. The study examines two different PV system configurations: On-Grid PV and Off-Grid PV, using sophisticated simulation and analytical techniques with the aid of HOMER Pro software. The simulation results offer compelling new information about these systems' economic viability. The simulation results in an NPC value of IDR 31,386,360,- for the Off-Grid PV configuration. The On-Grid PV system, in contrast, exhibits a significantly lower NPC value of IDR 8,903,329,- emphasizing its superior economic performance. This On-Grid PV system boasts a significant energy generation capacity of 5,012 kWh/year in addition to favorable cost efficiency. Notably, this is greater than the National Power Company's 1,186 kWh/year energy output. These results highlight the financial benefits of the On-Grid PV system and demonstrate its capability to provide affordable and sustainable energy solutions over a long period. The thorough analysis carried out in this study aids in the optimization of solar panel designs, offers insightful information for future sustainable energy projects, and emphasizes the crucial part that economic factors play in influencing the adoption of renewable energy technologies.

Effectiveness Analysis of Hydraulic Torque Wrench Machine Using Failure Mode and Effect Analysis (FMEA) and Logic Tree Analysis Study Case on Heavy Equipment Manufacturing

Dhadung Prihananto, Taufik Roni Sahroni — Sun, 10 Dec 2023 00:00:00 +0700

Production quality in terms of process efficiency and quality in the manufacturing industry must always be improved in order to maintain customer confidence. PT XYZ as a heavy equipment assembly company is one of the companies that depends on process reliability for a smooth production process. Based on this, the problems raised in this study focus on increasing the efficiency of the process of installing bolts on slew bearings. By using Overall Equipment Efficiency (OEE) and Process Capability (CpK) as the main benchmarks for measuring the quality of the production process where the latest data shows the average OEE value is at 18.53%, while the CpK value for the bolt tightening process is at 0 ,67. The OEE and CpK figures obtained show that the process quality is still not optimal and needs to be improved. The purpose of this research is to identify and prevent as many factors as possible that can lead to process failure. The methods used to evaluate processes and to identify where and how a process might fail are Failure Mode and Effect Analysis (FMEA) and Logic Tree Analysis (LTA). Both methods are used to identify and prevent as many factors as possible that can lead to a process failure. The results of research using the FMEA and LTA methods show that in the process of installing slew bearing bolts there is a process that needs to be improved because the RPN value is quite high, namely above 125. Some suggestions for improvement such as the use of a manipulator arm on a torque tool and the implementation of a manufacturing execution system (MES) can reduce the RPN value from above 125 to 28, where the process obtained is better than before.

Optimized Deep Convolutional Neural Network for the Prediction of Breast Cancer Recurrence

Arathi Chandran R I, V Mary Amala Bai — Sun, 10 Dec 2023 00:00:00 +0700

With more than 2.1 million new cases of diagnosis each year, breast cancer is considered to be the most prevalent women disease. Within 10 years, nearly 30% patients who got cured at early-stages experienced cancer recurrence. Recurrence is a crucial aspect of breast cancer behaviour that is inseparably linked to mortality. Despite its importance, the significant proportion of breast cancer datasets rarely include it, which makes research into its prediction more challenging. It is still difficult to predict who will experience a recurrence and who won't, which has implications for the treatment that goes along with it. Clinicians treating breast cancer may be able to avoid ineffective overtreatment if Artificial Intelligence (AI) methods are developed that can forecast the likelihood of breast cancer recurrence. This work proposes a novel automatic breast cancer recurrence classification and prediction system incorporating novel Deep Convolutional Neural Network (DCNN) algorithm. The proposed DCNN model is deployed on Wisconsin Breast Cancer dataset for further evaluation. The role of AI in forecasting recurrence is examined in this work. The experimental results were analysed for various combination of train and validation dataset. The accuracy, precision, recall and F1-score for the proposed DCNN was calculated as 97.63 %, 98.57 %, 96.84 %, 97.89 % respectively.

Strengthening the University-Maritime Industry Collaborations (UMICs): Technology Issues

Sun, 10 Dec 2023 00:00:00 +0700

In management practise and research, the university-maritime industry collaborations (UMICs) have grown in significance. This trend is reinforced by the necessity for innovation in the current industry environment and the desire of policymakers to commercialise knowledge from academia. Much less is known about these collaborations, although significant research efforts have been made to identify the success factors for these collaborations. Therefore, the aim of this study is to identify and explore the key factors that strengthen UMICs and propose a framework to enhance collaboration, so that a research agenda for the future will be developed based on an assessment of the existing literature. This study adopted a method of systematic literature review using published and unpublished theoretical literature to conduct analysis using five research databases in order to propose a framework aimed at identifying the key factors to strengthen UMICs. The findings of this study concluded that effective communication, trust, and adequate fund resources are essential for UMICs to succeed. Open communication channels, mutual trust, and shared vision can help build strong partnerships, while adequate funding can support research and development of new technologies, practices, and solutions. Based on previous research, none of them treated combined fund resources, effective communication, and trust as an independent variables towards UMICs relationship specifically. Hence, this study fills the gap by proposing a framework to test the relationship between fund resources, effective communication, and trust towards UMICs. Thus, the proposed framework can be used as a benchmark to strengthen UMICs in the future. This study also will encourage the managers in the maritime industry to drive innovation, establish strategic collaborations, actively involve stakeholders, and foster innovation and economic growth in the maritime industry to strengthen UMICs. The existing limited body of knowledge and literature will also benefit from this study.

SQL Injection Detection Using RNN Deep Learning Model

Abdulbasit ALAzzawi — Sun, 10 Dec 2023 00:00:00 +0700

SQL injection attacks are a common type of cyber-attack that exploit vulnerabilities in web applications to access databases through malicious SQL queries. These attacks pose a serious threat to the security and integrity of web applications and their data. The existing methods for detecting SQL injection attacks are based on predefined rules that can be easily circumvented by sophisticated attackers. Therefore, there is a need for a more robust and effective method for detecting SQL injection attacks. In this research, we propose a novel method for detecting SQL injection attacks using recurrent neural networks (RNN), which are a type of deep learning model that can capture the syntax and semantic features of SQL queries. We train an RNN model on a dataset of benign and malicious SQL queries, and use it to classify queries as either benign or malicious. We evaluate our method on a benchmark dataset and compare it with the existing rule-based methods. Our experimental results show that our method achieved high accuracy and outperformed the rule-based methods for detecting SQL injection attacks. Our research contributes to the field of web application security by providing a new and effective solution for protecting web applications from SQL injection attacks using deep learning. Our method has both practical and theoretical implications, as it can be easily integrated into existing web application security frameworks to provide an additional layer of protection against SQL injection attacks, and it can also advance the understanding of how deep learning models can be applied to natural language processing tasks such as SQL query analysis.

A Machine Learning Model for Determination of Gender Utilizing Hybrid Classifiers

Sun, 10 Dec 2023 00:00:00 +0700

One part of forensic anthropology involves investigating skeletal remains to identify corpses, and many of these remains were found incomplete, burned, broken, or destroyed, making investigation challenging. This study aims to use the pelvis and femur to identify the gender of skeletal remains. The pelvis and femur have previously been proven to be accurate indicators of a corpse's gender. The identification process is done through the measurement of the subpubic angle of the pelvis and the angle taken straight down from the top of the femur to the patella and then straight up. The two measurements were combined using the principal component analysis (PCA) method into two attributes on the x and y axes. These attributes were later used as data for the machine learning model design. The design process consisted of an Artificial Neutral Network (ANN) design model and Support Vector Machine (SVM) design model combined into a hybrid machine learning system. The ANN and SVM hybrid machine learning were tested with acquired data. The result of the test using the confusion matrix showed 83.33% accuracy, which is categorized as "good classification" based on Area Under the Curve (AUC).

Triglycerides of Crude Palm Oil to Biokerosene: Studies on Electrolysis and Electromagnetic Effect

Sun, 10 Dec 2023 00:00:00 +0700

Crude Palm Oil (CPO) is a potential feedstock for biokerosene. However, it is problematic when used directly because it is gummy, has a high viscosity and is degradable. Various conversion processes have been conducted that directly convert CPO into biokerosene, but it requires high temperature and pressure. Therefore, as a novelty, this study aims to develop the technology for converting triglycerides into biokerosene under relatively low operating conditions and producing similar petroleum kerosene by electrolysis-assisted and electromagnetic induction. In this study, the conversion technology process was conducted in three steps (i) converting triglycerides to Free Fatty Acids (FFA), (ii) converting FFA to alkanes, and (iii) converting alkanes to biokerosene. Step (ii) is assisted by the electrolysis process, meanwhile, step (iii) is assisted by electromagnetic irradiation. The finding showed that electrolysis obtained 73.47% yield of alkanes and electromagnetic irradiation obtained 78.02% yield of biokerosene. Biokerosene is almost close to kerosene-based petroleum in terms of colour Saybolt, flash point and Net Heating Value. The findings of this study may provide an alternate technology approach for biokerosene synthesis and solution kerosene scarcity.

Optimizing Cloud-Fog-Edge Job Scheduling Using Catastrophic Genetic Algorithm and Block Chain-Based Trust: A Collaborative Approach

Nibras A. Mohammed Ali, Firas A. Mohammed Ali — Sun, 10 Dec 2023 00:00:00 +0700

Collaborative edge-cloud features improve job scheduling. Cloud job scheduling is crucial. Pending delay completion. A cloud-edge mixed system replaced centralized cloud computing. Combining resource levels reduces terminal user service call latency. Decentralization, regionalization, and node dispersal autonomy increase ambiguity, unreliability, and instability. This paper will plan cloud-migrating tasks on edge devices or the cloud to achieve a global optimum. The objective of this research is to enhance the efficiency of job scheduling in cloud-fog edge environments through the integration of the Catastrophic Genetic Algorithm (CGA), a genetic algorithm inspired by natural evolution. Additionally, Berger's theory will be employed to develop a trust-enabled interaction framework based on blockchain technology. The CGA fitness function incorporates load balancing and reasonability in the coordination of services and scheduling of tasks, with the goal of maximizing performance. This article presents proposed improvements to the CGA, which involve the incorporation of mutation and crossover operators, roulette selection, and cataclysm. These changes aim to expand the search area and potentially discover schedules that are more optimal. The approach also effectively deals with the problem of premature convergence, guaranteeing ample time for the algorithm to comprehensively explore the solution space prior to reaching a final solution. The experimental findings indicate that the strategy put forward in this study yields a substantial reduction in task completion time, surpassing 97%. Furthermore, it effectively addresses the best local problem, hence showcasing competing options.

Enhancing Support For Senior Citizens: Development And Evaluation of The OSCA Information Management System With Agile Methodology and ISO/IEC 25010 Compliance

Johannah Mae C. Velasquez, Alexander S. Cochanco, Ruth G. Luciano — Sun, 10 Dec 2023 00:00:00 +0700

The elderly were a member of the vulnerable group which the government supported and assisted in every way possible. To contribute to the support and assistance needed by the elderly who are called senior citizens, this study was conducted. This aims to develop and design a usable, performance-efficient, and functional suitable to the operation and service of the Office of the Senior Citizen Affairs (OSCA), Cabanatuan City office. The System Application Office of the Senior Citizen Affairs Information Management System with Analytics (OSCA-IMSA) was developed using the Agile System Development Life Cycle (SDLC) Model. The agile model has different phases and sub-phases that guarantee the quality and efficient development of the system. Every phase and sub-phases lead to a well-organized, and systematic process of system development. The proponents adopted the ISO/IEC 25010 criteria as an evaluation tool to assess the system's usability, reliability, performance efficiency, functional suitability, security, portability, maintainability, and compatibility. The self-made survey questionnaire was used as the main tool to collect the data from the respondents. Purposive sampling was used to determine the right respondents for the study. The study was composed of two different sets of respondents, the first set was the System Users and the second set was composed of IT Experts. The Office of the Senior Citizen Affairs Information Management System with Analytics (OSCA-IMSA) was evaluated and assessed by the respondents with a result of being highly functional, highly efficient, highly portable, highly maintainable, highly compatible, highly secured, highly reliable, and highly usable. This result indicates that the system passed and conformed to the ISO/IEC 25010 Software Product Quality Standard, hence, is recommended to be deployed at the research locale.

Is It Practical Digital Learning Application For Learning 3D Graphic Design Based on Augmented Reality?

Fitri Ayu, Ganefri Ganefri, Dedy Irfan, Asrul Huda, Des Suryani — Sun, 10 Dec 2023 00:00:00 +0700

Learning Graphic Design requires a high level of visualization, especially for design concepts or 3D interior design. If this material is only taught through guided practice in class, the learning outcomes will certainly not be optimal, especially for vocational graduates in the current digital era. The research aims to test the practicality of digital learning applications based on augmented reality technology as a learning medium for 3D interior graphic design. The type of research is Research and Development (R&D) with the Borg and Gall Method, data collection techniques using Likert scale questionnaires, applications built using Blender for modelling, Unity 3D for Augmented Reality Implementation, Android Studio for designing applications, Firebase for database storage, and Fiqma to design the interface design. This research produces a digital learning application that is used for learning 3D interior graphic design which is equipped with learning needs such as classrooms, and communication rooms and is based on Augmented Reality technology so that the resulting interior design objects can be displayed in real-time. Aiken's V formula is used to test the practicality of digital learning applications. The research results showed that the average Aiken's V score from lecturers was 85.22% in the practical category, and students in the small group test was 82.96% and students in the large group test was 83.04% in the practical category. So, it can be concluded that the use of the DiGi.AR application based on Augmented Reality is good and practical for learning 3D graphic design.

Understanding the Perspectives and Usability of Digital games for Children with Intellectual Disabilities

Dhiyaneshwari RP, Renuga Devi C — Sun, 10 Dec 2023 00:00:00 +0700

Typically, the digital games are used as a medium for teaching students having intellectual disabilities, and it helps the student to enhance their learning skills and to understand their surroundings. Intellectual disability is a neurological disease that manifests as a deficit in an individual's mental and adaptive functioning during childhood. Moreover, the computer-assisted training has been shown to be the most effective method of instruction for children with disabilities in terms of conceptual learning, academic accomplishment, and skill-based development. Traditionally, some existing research works are done in this field for analyzing the effectiveness of digital games. Accordingly, the main contribution of this research work is to determine the perception of special educators and usability of digital games in educational settings for children with intellectual disabilities. By identifying the needs for their design and use in those children's classes, this study intends to further illuminate how to employ digital games in education as a contribution to improving educable intellectually impaired children's teaching and learning practices. In addition, a case study is conducted in this work using a closed-ended questionnaire on a sample of 60 special educators, handling Children with Intellectual disabilities. According to this case study analysis, the quantitative analysis suggest that special educators have a strong need to use digital games to optimize learning for children with intellectual disabilities and to promote digital inclusion. Based on the outcomes, it is inferred that the digital game based learning could be more helpful and beneficial for the student with intellectual disabilities in real time.

Enhancing Onion Supply Chain Using The Smart Contract Platform: A Meta-Analysis

Kenneth L. Armas, Bren C. Bondoc, Rhea Lyn La Penia — Tue, 12 Dec 2023 00:00:00 +0700

In the ever-evolving landscape of global agricultural supply chains, ensuring traceability, transparency, and sustainability is paramount to guaranteeing food safety, combating fraud, and meeting consumer demands. The Philippine onion industry, a vital component of the nation's horticultural sector, grapples with challenges related to traceability and transparency that impact customer trust and economic sustainability. While the adoption of smart contract platforms has revolutionized traceability and transparency in various agricultural sectors worldwide, their potential in the Philippine onion market remains underutilized. This study employs a comprehensive meta-analysis approach to evaluate the existing traceability and transparency mechanisms within the Philippine onion industry, drawing insights from a diverse set of studies. The meta-analysis reveals a consistently positive impact of these mechanisms on traceability and transparency. The findings, supported by a range of studies, underscore the value of these mechanisms in improving product quality, supply chain efficiency, and transparency. The study further investigates the potential impact of smart contract platforms in enhancing traceability and transparency throughout the onion industry's supply chain. Meta-analysis results suggest that the adoption of smart contract platforms holds promise in furthering these objectives. Through automated record-keeping and real-time data sharing, smart contracts have the potential to address existing challenges related to data fragmentation and limited technological integration. Identifying barriers to smart contract platform adoption in the context of traceability and transparency, the study proposes a set of strategic initiatives and recommendations. These recommendations cater to various stakeholders, including government bodies, academic institutions, local authorities, onion farmers, and industry players, aiming to promote the widespread adoption of smart contract platforms. This study extends beyond the confines of the Philippine onion industry, offering valuable insights into the role of smart contract platforms in enhancing traceability, transparency, and sustainability within agricultural supply chains. As the world works towards achieving the Sustainable Development Goals of the United Nations, this research contributes to the realization of "Zero Hunger" and "Responsible Consumption and Production" by promoting transparent and sustainable supply chains. By bridging the gap in understanding the potential of smart contract platforms in enhancing traceability and transparency, this study paves the way for innovative solutions, inspiring trust, and fostering sustainable farming practices within the onion industry and, potentially, in similar sectors worldwide.

Musculoskeletal Disorders Risk Levels in Tofu Workers in North Aceh: An Ergonomic Assessment

Cut Ita Erliana, Iskandar Hasanuddin, Yuwaldi Away, Raja Ariffin Raja Ghazilla — Thu, 14 Dec 2023 00:00:00 +0700

Tofu is a soy-based food that is popular in Indonesia. One of the supporting factors is that it has high nutritional value and low price. Tofu is produced by micro, small, and medium enterprises (MSMEs) in all provinces in Indonesia, including in the region of Aceh. Based on the results of observations, the tofu production process still predominantly uses human labor, starting from the washing process to the cutting process. This activity is carried out every day with a high repetition rate. If this is allowed to happen for a long time, workers will be exposed to the risk of musculoskeletal disorders. This study uses the Cornell Musculoskeletal Disorders (CMDQ) questionnaire to assess the workers' work posture and determine the level of occupational hazard risk using the Rapid Entire Body Assessment (REBA) method. The results of the distribution of questionnaires show that the body parts that experienced pain complaints were the shoulder (right dan left), upper back, upper arm (right and left), lower back, forearm (right and left), also wrist (right and left). It happens in the filtering process. The results of the assessment of work posture with the REBA method show the score of work posture in the filtering process is 13, which means that the filtering posture of workers has a very high risk and must be improved immediately. The present research recommended additional intervention that includes engineering and administrative control methods for reducing those workers' complaints of muscle pain.