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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 20 Documents
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Search results for , issue "Vol 6, No 3 (2025): September 2025" : 20 Documents clear
Static Analysis of Android Malware Based on Opcode and Permission Features Using Random Forest Azis, Muhammad Amran; Irianti, Arnita; Firgiawan, Wawan; M. Yusuf, Andi
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 3 (2025): September 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i3.9113

Abstract

This study conducts a static analysis of Android applications to detect malware based on opcode and application permission features. A total of 1,000 applications were used, consisting of 500 benign and 500 malware samples. Opcode features were extracted from the classes.dex file and represented as numerical vectors using the Term Frequency–Inverse Document Frequency (TF-IDF) method. A total of 147 unique opcodes were successfully identified. In addition, application permission features were extracted from the AndroidManifest.xml file, resulting in 65 features. These two types of features were then combined to form a dataset used as input for the classification process. The classification algorithms used in this study are Random Forest and Support Vector Machine as a comparison. The model performance was evaluated using accuracy, precision, recall, and F1-score metrics. Based on the test results on the test data, the Random Forest model achieved the best accuracy of 99%, followed by SVM at 98%. These results indicate that the combination of opcode and application permission features using Random Forest is quite effective in distinguishing between benign and malware applications through static analysis. Therefore, the TF-IDF-based classification system utilizing opcode and permission features developed in this study can serve as an initial approach for Android malware detection using static analysis.
Automated Student Activity Monitoring Based on Spatiotemporal Modeling Using MediaPipe and Long Short-Term Memory Andi Syarwani; Hartinah; Maya Itasari; Nurul Amalia Amri; Annisa Nurfadhilah; Muhdalifah Muhtar
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 3 (2025): September 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i3.9371

Abstract

Computer vision-based Human Activity Recognition (HAR) systems hold significant potential for applications in educational settings, particularly for monitoring student activities in laboratories or classrooms. Activities such as typing, smartphone usage, and resting are often visually indistinguishable due to their highly similar seated postures. This study proposes a spatiotemporal modeling approach to automatically and non-invasively recognize such activities. Body poses are extracted from video streams using MediaPipe Pose and represented as sequential feature vectors, which are then analyzed using a Long Short-Term Memory (LSTM) network to capture temporal dynamics. The model is trained on video data of students performing three primary activity classes. Evaluation on validation data demonstrates a classification accuracy of 98.48%, with average precision, recall, and F1-score values of approximately 98%. However, testing on unseen videos shows a decrease in accuracy to around 65%, primarily due to misclassification in segments with minimal movement. These findings suggest that the model is sensitive to subtle pose transitions, which are common in seated activity contexts. Overall, the proposed approach demonstrates promising potential for automated student activity monitoring and provides a foundation for developing pose-based behavioral analysis systems in contextual learning environments.
Vehicle Detection Counting using YOLO and DeepSORT on Edge Device Rafli; Wardoyo, Siswo; Alfanz, Rocky; Fahrizal, Rian; Muhammad, Fadil; Muttakin, Imamul
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 3 (2025): September 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i3.9482

Abstract

Vehicle counting is a crucial method used in traffic management. Computer vision can be employed for efficient detection and classification techniques for vehicle objects. This paper reports on a simultaneous process of vehicle classification and counting implemented on NVIDIA Jetson Nano. The use of YOLOv5 overcomes computational load issues in edge computing deployments, whereas its combination with the DeepSORT tracker algorithm enhances the accuracy of vehicle detection and counting in various directions. A total of 18200 images are used to train the detectors that are designed to target local vehicles. The average accuracy of the model for detecting cars, motorcycles, buses, and trucks is 72.1%, 21.56%, 70%, and 25.63%, respectively. Real-time tests obtained an overall average vehicle counting accuracy of 49.95%.
Improving Accuracy of Software Development Effort Estimation Using Use Case Points and Fuzzy Logic Victor Eric Pattiradjawane; Emanuella M. C. Wattimena; Devi Valentino Waas
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 3 (2025): September 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i3.9488

Abstract

Effort estimation in software development is essential for effective project planning and resource management. The Use Case Points (UCP) method is one of the most recognized estimation techniques; however, its accuracy is often constrained by the subjectivity involved in determining the Environmental Complexity Factor (ECF). This study introduces an enhanced estimation model that integrates Fuzzy Logic into the UCP framework to reduce subjectivity and improve precision. Six software project datasets were analyzed—one institutional project and five publicly available datasets—using Python-based simulations. The proposed Fuzzy-UCP model redefines ECF through fuzzy membership functions and rule-based inference, transforming linguistic assessments into quantitative outputs. Evaluation metrics, including Mean Magnitude of Relative Error (MMRE) and Estimation of Mean Magnitude of Error (EMMER), were employed to assess prediction accuracy. The results demonstrate that the Fuzzy-UCP model improves estimation accuracy by 4% to 12% compared to the standard UCP method, with lower standard deviation values. These findings confirm that incorporating fuzzy reasoning enhances reliability in handling uncertainty during effort estimation. Consequently, the Fuzzy-UCP approach provides a practical, adaptive, and computationally efficient alternative for software engineering practitioners seeking consistent and data-driven estimation results.
Improving Air Quality Forecasts with LSTM and SHAP Explainability: A Case Study in Jakarta Radjabaycolle, Jefri E. T.; Wattimena, Emanuella M C; Pattiradjawane, Victor Eric
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 3 (2025): September 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i3.9512

Abstract

Accurate air-quality forecasting is essential for public-health advisories in large tropical megacities such as Jakarta. This study develops an explainable deep-learning pipeline to predict Indonesia’s Air Pollution Standard Index (ISPU) at the DKI-5 station using daily data from 2017–2021. After handling missing values and integrating meteorological variables, all features were min–max normalized and framed with a lag window of five days. A stacked LSTM (128 and 64 units, dropout 0.2, Adam optimizer, MSE loss) was trained with an 80/20 train–test split. Model performance was assessed using MAE, RMSE, and R2R^2R2. To open the “black box,” SHAP was applied to quantify each feature’s contribution to the predictions. Results show stable convergence of training and validation losses and good generalization. The best configuration achieved MAE ≈ 7.96, RMSE ≈ 10.26, and R2≈ 0.52 on the test set. SHAP analysis indicates that PM10_lag1 is the most influential predictor, followed by wind speed (ff_avg_lag1), relative humidity (RH_avg_lag1), and average temperature (Tavg_lag1), confirming the joint role of recent pollutant levels and meteorology in driving ISPU variability. Compared with a previous LSTM configuration on the same site, the proposed model lowers RMSE by ≈25%, evidencing a more accurate and reliable ISPU forecast while providing transparent feature attributions. The proposed LSTM–SHAP framework offers an interpretable decision-support tool for air-quality management in Jakarta.
Implementatiton of the Random Forest Regression Algorithm for Predicting Maize Yields Pyrda Monica; Muliadi; Abdul Rahman Patta
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 3 (2025): September 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i3.9540

Abstract

This study aims to implement the Random Forest Regression algorithm to predict maize yields in North Luwu Regency, South Sulawesi. Historical data from 2020–2024 were used, with variables including planting area, harvested area, productivity, and production. The study follows the CRISP-DM methodology, which includes the stages of business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Modeling was carried out using Google Colab with GPU/TPU support and Google Drive integration. The dataset was divided into 80% training data and 20% testing data. The model was developed with parameters such as max_depth = 10 and n_estimators = 200. Evaluation results indicated excellent performance, with an R² value of 0.9974, RMSE of 42.98 (4.02%), MAE of 26.188 (2.45%), and MAPE of 7.12%, all of which fall under the “excellent” category. The trained model was then integrated into a website to facilitate users in predicting maize production based on input variables. Questionnaire results from 10 respondents showed a very high satisfaction level, with an average score of 96.6%, classified as excellent.
Cloud Governance Frameworks: CIA-Based Security and Compliance Rahmika, Afiyah Rifkha; Muhammad Akbar; Deni Luvi Jayanto; Joshua Reska Bu'tu
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 3 (2025): September 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i3.9541

Abstract

Digital transformation has driven organizations to adopt cloud computing as a flexible and efficient IT infrastructure solution. However, differences between public and private cloud models create challenges in maintaining information security and compliance. This study employs a descriptive–comparative approach through an extensive literature review of journals, conference papers, and standards such as ISO/IEC 27001 and the NIST Cybersecurity Framework (CSF). Findings show that public clouds, while offering cost efficiency and scalability, are more vulnerable to external threats due to limited control and multi-tenancy, whereas private clouds provide stronger governance and customization but demand higher internal resources. The hybrid model emerges as a strategic alternative balancing flexibility and control. Integrating the Confidentiality, Integrity, and Availability (CIA) framework enables a structured evaluation of security risks and governance mechanisms across cloud models. The study highlights that effective governance depends on risk-based policies, compliance alignment, and adaptive controls. It concludes that combining ISO/IEC 27001’s prescriptive management system with NIST CSF’s flexible structure can optimize resilience, compliance, and operational sustainability. This integrated governance approach ensures that cloud security aligns with organizational goals and regulatory requirements while addressing evolving digital risks
Analysis of Naive Bayes and Support Vector Machine Algorithms in Classification of Diabetes Cases Based on Lifestyle Factors Awalia, Andi Dio Nurul; Muhammad Fadhil Hani; Dewi Fatmarani Surianto
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 3 (2025): September 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i3.9783

Abstract

The increase in diabetes mellitus cases globally, including in Indonesia, demands a more adaptive lifestyle-based risk prediction strategy. This study aims to evaluate and compare the efficiency of Support Vector Machine (SVM) and Naive Bayes in the diabetes risk classification process using a Hybrid clustering-classification approach . The data analyzed in this study were obtained from the Kaggle platform , with 8,500 data of diabetes patients analyzed based on the attributes of age, gender, and smoking history. The Clustering process was carried out using K-Means (k=3), resulting in three main groups with different lifestyle characteristics. The classification results showed that Naive Bayes provided stable performance with an F1-score of 0.975. Meanwhile, SVM excelled in terms of F1-score 98.3% and perfect AUC (1,000), and was able to classify all data in cluster C3 without error. However, SVM recorded a higher classification error in the majority cluster . This study concluded that SVM was superior by 0.8% over Naive Bayes . Naive Bayes is more suitable for balanced data, while SVM is effective for detecting patterns in minority groups. These findings support the use of a hybrid approach in lifestyle data-based diabetes early detection systems. Future research directions include integrating additional variables and ensemble techniques to improve model generalization.
Literature Review: IT Management and Information Systems Analysis Using COBIT 5 Yusuf, Nur Azizah; Andi Hutami Endang
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 3 (2025): September 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i3.9339

Abstract

This study is a literature review that examines how the COBIT 5 framework is used to assess information technology (IT) management and information systems across various businesses. The study examined 100 scholarly papers to determine how COBIT 5 domains (EDM, APO, BAI, DSS, MEA) are distributed, what combinations of domains are used, what each study aims to assess, and how well IT processes are implemented. The study results indicate that the DSS and APO domains are the most commonly used. This is due to their strong focus on IT service operations and strategic planning. Furthermore, many studies integrate two to five areas to obtain more comprehensive evaluation results. These studies examine several aspects during the evaluation, such as process capability, strategic alignment, risk management, and information systems development. However, most studies indicate that IT process capability remains at levels 1 to 3. This study can serve as a foundation for organizations and researchers to design IT management improvement strategies that are more focused and aligned with organizational goals.
Sustainable Biogas Development in Argosari Village: A Spatial, Environmental, and Economic Feasibility Analysis Ghefra Rizkan Gaffara; Arfilian Permana Putra; Debby Syafriyandi; Raafi Widyaputra Yulianyahya; Ansadilla Niar Sitanggang
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 3 (2025): September 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i3.9589

Abstract

Energy transition at the village level plays a crucial role in Indonesia’s strategy toward sustainable energy independence. This research examines the biogas-based renewable energy development in Argosari Village as a model of community-driven sustainability. The study integrates qualitative and quantitative analyses—spatial, environmental, and economic—using IPCC guidelines, Location Quotient (LQ), Internal Rate of Return (IRR), and Net Present Value (NPV). The research involved interviews with 45 households, three focus group discussions with farmer associations, and secondary data from the Malang Regency Bureau of Statistics. Results show that 57% of households have installed biogas units, reducing GHG emissions by approximately 60% (1.2 tons CO₂ per household annually), and achieving a B/C ratio of 1.75. Sensitivity analysis indicates project feasibility remains positive even under ±15% variation in maintenance and subsidy levels. Comparative analysis with rural biogas programs in India and China highlights Argosari’s unique integration of social, economic, and environmental benefits. This study contributes a replicable framework for community-scale renewable energy implementation in rural Indonesia.

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