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M. Miftach Fakhri
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Journal of Embedded Systems, Security and Intelligent Systems
Published by Universitas Negeri Makassar
ISSN : 2745925X     EISSN : 2722273X     DOI : -
Core Subject : Science,
Computer Science & IT
The Journal of Embedded System Security and Intelligent System (JESSI), ISSN/e-ISSN 2745-925X/2722-273X covers all topics of technology in the field of embedded system, computer and network security, and intelligence system as well as innovative and productive ideas related to emerging technology and computer engineering, including but not limited to : Network Security System Security Information Security Social Network & Digital Security Cyber Crime Machine Learning Decision Support System Intelligent System Fuzzy System Evolutionary Computating Internet of Thing Micro & Nano Technology Sensor Network Renewable Energy Wearable Devices Embedded Robotics Microcontroller
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 157 Documents
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Prediction of Learning Outcomes of Programming Courses Using Random Forest and Feature Selection Andi Muh Wira Gunawan; Emalia Fatma Dianti; Erfina Fitri Adnur; Fathul Umam; Fatmawati; Fitri Rahmadhani
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 2 (2025): June 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/1ctsxz88

Abstract

The achievement of learning outcomes in programming courses remains a challenge in higher education due to variations in students’ logical thinking skills, problem-solving abilities, and practical competencies. Conventional evaluation methods are generally retrospective and do not provide early identification of students at risk of not achieving course learning outcomes. Therefore, predictive modeling based on educational data can support data-driven academic decision-making. This study aims to develop a predictive model of learning outcomes in a programming course using the Random Forest algorithm combined with feature selection to improve model performance and interpretability. This research employed a computational experimental method with a quantitative approach. The dataset consisted of 180 student academic records, including assignment scores, quizzes, practicum, project, attendance, midterm exam, and final exam scores. The experiment compared a baseline Random Forest model using all features with a model applying feature selection based on feature importance. Data were divided into 80% training and 20% testing sets and evaluated using accuracy, precision, recall, and F1-score. The results showed that the baseline model achieved 83.33% accuracy, while the model with feature selection improved accuracy to 88.89% and increased recall performance. Final exam and practicum scores were identified as the most influential predictors. The findings indicate that integrating Random Forest and feature selection enhances prediction accuracy and provides meaningful insights for early intervention strategies in programming education.
Comparison Of Automated Machine Learning and Manual Modeling In Data Science Education Toward Pipeline Understanding and Model Interpretability: A Qualitative Experimental Study Abdi Anugrah; Wahyullah; Yusri Yusuf; Zulham Abidin; Dian Kumala Azis; Fandi Armawan
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 2 (2025): June 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i2.11528

Abstract

The rapid development of Automated Machine Learning (AutoML) has transformed modeling practices in data science by automating preprocessing, feature selection, and hyperparameter tuning. However, its pedagogical implications in higher education remain underexplored. This study aims to compare the impact of AutoML and manual modeling approaches on students’ understanding of machine learning pipelines and model interpretability. A qualitative quasi-experimental design was employed involving final-year undergraduate students enrolled in a Data Science course. Participants were divided into two groups: one using AutoML tools and the other applying manual modeling procedures. Data were collected through in-depth interviews, learning observations, reflective reports, and artifact analysis of coding assignments. Thematic analysis was used to identify differences in conceptual understanding and learning experiences. The findings indicate that manual modeling fosters deeper structural comprehension of pipeline stages, including preprocessing, feature engineering, and evaluation mechanisms. In contrast, AutoML enhances efficiency and reduces technical barriers but tends to obscure internal modeling processes, potentially limiting interpretative insight. These results highlight important implications for curriculum design in data science education, suggesting the need for balanced integration between automation tools and foundational modeling practices.
SVM Parameter Optimization with PSO for Sensor-Based Water Quality Classification and Monitoring Dashboard Andi Muharram; Hasmiah Husayn; Ibnu Farhan Hasrul; Ibnu Hajar; Ibnu Mundzir Hasanuddin; Ikram Anas
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 1 (2025): March 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/te7wgc92

Abstract

Water quality monitoring is essential for environmental sustainability and public health protection. Conventional laboratory-based testing is often time-consuming and unsuitable for real-time monitoring systems. The development of sensor-based Internet of Things (IoT) technology enables continuous acquisition of water quality parameters such as pH, temperature, turbidity, and Total Dissolved Solids (TDS). However, accurate classification of water quality from multi-parameter sensor data remains a challenge due to non-linear data characteristics and the sensitivity of machine learning models to parameter selection. This study aims to optimize the parameters of Support Vector Machine (SVM) using Particle Swarm Optimization (PSO) for sensor-based water quality classification and to integrate the optimized model into a real-time monitoring dashboard. A quantitative experimental approach was employed by comparing the performance of standard SVM and PSO-optimized SVM models. The dataset consisted of sensor measurements collected over 30 days and was evaluated using accuracy, precision, recall, and F1-score metrics. The results show that parameter optimization significantly improves classification performance and enhances the model’s ability to detect critical water quality conditions. The optimized SVM model was successfully integrated into a web-based dashboard capable of real-time monitoring and classification. This study demonstrates that combining metaheuristic optimization with machine learning provides an effective and practical solution for intelligent water quality monitoring systems
Development of an Expert System for Crisis Risk Assessment in Bullying Cases to Support School Counselor Service Protocols: Expert Validation and System Feasibility Testing Andi Tenri Aunia Sabir; Imam Nur Ihsan Mustafa; Ince St Aisyah Tri Utami; Irda Baenaha; Isbahuddin; Jihan Aprilia Patata
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 2 (2025): June 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i2.11530

Abstract

Bullying in school environments has increasingly led to psychological crises among students, requiring early detection and structured risk assessment by school counselors. However, risk assessment practices are often subjective and lack standardized digital support systems. This study aims to develop an expert system for crisis risk assessment in bullying cases to support school counseling service protocols through expert validation and system feasibility testing. The research employed a Research and Development (R&D) approach using the ADDIE (Analysis, Design, Development, Implementation, Evaluation) model. During the analysis phase, crisis risk indicators were identified through literature review and needs assessment with school counselors. The design and development phases produced a rule-based expert system utilizing weighted scoring to classify risk levels into low, moderate, and high categories. Expert validation involving counseling, psychology, and information systems specialists indicated a feasibility score of 88% (very feasible). User feasibility testing with school counselors resulted in an acceptance rate of 84% (feasible to highly feasible). System accuracy testing showed an accuracy rate above 80% compared to manual assessments. The findings demonstrate that the developed expert system is valid, feasible, and effective as a decision support tool for assessing crisis risk in bullying cases. This study contributes to the integration of counseling science and artificial intelligence by providing a structured, objective, and protocol-based digital assessment system to enhance school crisis management services
Design And Development of a Web-Based Practicum Management Information System with QR Code Integration and Automated Notifications Abdul Syakur; Clara Falisha Suardi; Devianty Lukman; Dimas Ari Wahyudi; Erma Hajrah; Erwin
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 1 (2025): March 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/4fngb442

Abstract

This study aims to design and develop a web-based Practicum Management Information System integrated with QR Code technology and automated notifications. The conventional practicum management process still relies on manual attendance recording and delayed information dissemination, resulting in inefficiency and administrative inaccuracies. This research applies the Research and Development (R&D) method using the Waterfall software development model. The stages include requirement analysis, system design, implementation, and testing. The system integrates QR Code for attendance validation and automated notification features via email to enhance communication efficiency. Black Box Testing and User Acceptance Testing (UAT) were conducted to evaluate system functionality and usability. The results show that the developed system improves attendance accuracy, accelerates administrative processes, and enhances real-time information delivery. The system contributes to increasing efficiency, transparency, and effectiveness in practicum management in higher education institutions.
Development of an Online Supervision Dashboard for School Counselor Burnout Monitoring in Bullying Cases: Data Analysis and Use Evaluation Andi Zulfahmi; Jumaria Manda; Khairul Alam; Kristian Rande Salu; M. Taufik; Miftah Kurniawan
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 1 (2025): March 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/1px9fh71

Abstract

This study aims to develop and evaluate an online supervision dashboard as a school counselor burnout monitoring system in handling bullying cases. The research uses a Research and Development (R&D) approach with the ADDIE model which includes the stages of needs analysis, design, development, implementation, and evaluation. The research subjects consisted of 30 school counselors and 5 guidance and counseling supervisors. Data was collected through burnout questionnaires (adaptation of the Maslach Burnout Inventory), system activity logs, usability questionnaires (System Usability Scale), observations, and interviews. The results of the study show that the dashboard developed has the main features in the form of periodic burnout assessment, trend visualization, risk notification, and supervision reporting integrated with bullying case management. Quantitative analysis showed a significant decrease in burnout scores, especially in the emotional fatigue dimension (p < 0.05), after eight weeks of using the system. The usability score is in the good category, indicating that the system is easy to use and accepted by users. Qualitative findings indicate that dashboards increase counselors' self-awareness and strengthen the quality of data-driven supervision. Overall, the online supervision dashboard has the potential to be an innovation in the transformation of education supervision that supports the prevention of burnout and increases the effectiveness of handling bullying cases in schools
Implementation Of an Academic Chatbot Based on Dialogflow for Study Program Information Services: A Qualitative Study on Usability and User Satisfaction Ahmad Fakih; Fahmi Yuliady; Fatahillah Paweloi; Firanda; Gabriella Pasalli; Haerunnisa
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 1 (2025): March 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/3y5s4f23

Abstract

The rapid development of artificial intelligence technology has encouraged the utilization of chatbot systems in higher education information services. However, the effectiveness of chatbot implementation in supporting academic information services requires evaluation, particularly in terms of usability and user satisfaction. This study aims to analyze the implementation of a Dialogflow-based academic chatbot and evaluate its usability and user satisfaction using a qualitative approach. This research employed a descriptive qualitative case study design. Data were collected through observation, in-depth interviews, and documentation involving 12 active students who had used the chatbot. Data analysis was conducted using the interactive model of Miles and Huberman, consisting of data reduction, data display, and conclusion drawing. The results indicate that the chatbot provides ease of access, fast response time, and relevant academic information. From a usability perspective, the system meets effectiveness, efficiency, and satisfaction aspects. However, several limitations were identified, including limited contextual understanding and restricted response variations. Overall, users expressed positive satisfaction and perceived the chatbot as helpful in supporting academic information services. The study contributes to the development of intelligent information systems in higher education by providing qualitative insights into user experience and system improvement strategies.
Predictive Modeling of Suicide Ideation Risk of Bullying Victims Using Machine Learning Based on Questionnaire Data Andika Saputra; Mirnawati; Mispasari; Muh Nasrun; Muh. Arya Kusuma Wardana; Muhalis
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 1 (2025): March 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/nxxppw67

Abstract

Bullying is one of the main risk factors for mental health disorders in adolescents and is significantly correlated with increased suicidal ideation. This study aims to develop a predictive model of suicidal ideation risk in bullying victims using a questionnaire data-based machine learning approach as the basis for the development of an early warning system. The study used a predictive quantitative design involving 350 respondents who had experienced bullying. The variables analyzed included demographic factors, experiences of bullying, as well as psychological indicators such as depression, anxiety, stress, self-esteem, and social support. Four classification algorithms were compared, namely Logistic Regression, Random Forest, Support Vector Machine, and XGBoost. The results show that XGBoost has the best performance with an accuracy of 91% and a ROC-AUC of 0.94. The most influential variables on risk prediction were depression scores, social support, anxiety, and bullying frequency. These findings show that the machine learning approach is effective in supporting early detection of the risk of suicidal ideation and has the potential to be implemented as an early warning system in the educational environment.
Development of a Bullying Behavior Prediction Model Using Machine Learning Based on Psychosocial Factors: Accuracy Modeling and Evaluation Study Annisa Diah Mutiara; Muhammad Alif Karyamula; Muhammad Reza Shiraj; Musakkir Nompo; Irma Mawarni; Ismi Afni
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 1 (2025): March 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/tkmzgn94

Abstract

Bullying behavior is a serious problem in the educational environment that has an impact on students' psychological health and social development. Psychosocial factors such as empathy, emotion regulation, peer pressure, and school climate are known to contribute to the emergence of these behaviors. This study aims to develop a model of predicting bullying behavior using a Machine Learning approach based on psychosocial factors and evaluate the accuracy level of the resulting model. This study used a predictive quantitative design with a cross-sectional approach on 412 junior high and high school students. Data was collected through a standardized questionnaire and analyzed using four classification algorithms, namely Logistic Regression, Support Vector Machine (SVM), Random Forest, and XGBoost. Model validation was carried out using a 5-fold cross validation technique with evaluation parameters of Accuracy, Precision, Recall, F1-score, and ROC-AUC. The results showed that the XGBoost algorithm had the best performance with an Accuracy value of 90%, F1-score of 0.85, and a ROC-AUC of 0.93. The variables of peer pressure, empathy, and emotion regulation were the most influential predictors in the model. These findings suggest that the integration of psychosocial factors and Machine Learning techniques is effective in building accurate models of predictive bullying behavior. This model has the potential to be used as a basis for the development of early detection systems and preventive interventions in the school environment
Design and Build an Embedded System-Based Computer Damage Diagnosis Expert System for School Laboratories Aqsha Anugrah Abu Putra; Ismira Amusaputri; Karmila; Marwan Kamaruddin; Mubtadin Ali Mulia; Feija Heirani
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 2 (2025): June 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/xr60c564

Abstract

Computer laboratories are an important facility in supporting the technology-based learning process in schools, but they often face the problem of hardware and software malfunctions that can hinder academic activities. The limited number of technicians in the school environment causes the damage diagnosis process to often be slow and less systematic. This research aims to design and build an embedded system-based computer damage diagnosis expert system that can be used as a troubleshooting tool in school laboratories. The method used is a system engineering approach (Research and Development) with the stages of needs analysis, design, implementation, and testing. The expert system's inference mechanism uses the rule-based Forward Chaining method implemented on the ESP32 microcontroller platform. The test results of 30 crashes showed a system accuracy rate of 86.67% with an average response time of less than 2 seconds. The system is able to provide early diagnosis and solution recommendations quickly and consistently. Thus, the system developed is effective as a decision support tool in the process of identifying computer damage and contributes to improving the efficiency of school laboratory management.

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