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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 134 Documents
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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.
Smart Parking System Based on Internet of Things Technology for Realtime Parking Slot Monitoring Yera Nafisy; Satria Gunawan Zain; Kurnia Prima Putra
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.v6i3.9716

Abstract

This study aims to develop a parking system based on Internet of Things (IoT) technology to address the issues of parking space limitations and efficiency. The system is designed using an HC-SR04 sensor and an ESP32 microcontroller, with data displayed in real-time through an Android application based on MIT App Inventor. The research employed applied research methods, with data collection techniques involving observation and questionnaires. Data analysis was conducted using descriptive quantitative methods. The results showed that the system has a functionality value of 1 or 100%, and a usability level of 81%–100%, indicating that the system is highly suitable for use. Additionally, this system is energy-efficient, with a power consumption of only 0.9105 watts (5.607 mWh for 6 hours of use), and the Wi-Fi connection remains stable up to a distance of 20 meters. Sensor testing revealed that at distances of 50–100 cm, objects can still be detected up to an angle of ±45°, while at a distance of 150 cm, effective detection only occurs within the angle range of -30° to 30°. At a distance of 200 cm, objects are not detected at an oblique angle. These results demonstrate that an IoT-based smart parking system can function effectively and efficiently if the sensor placement is adjusted to its optimal range.
Digital Forensics in Open Journal Systems: Case Study on Security Breach and Data Recovery Andi M. Yusuf; Dian Megah Sari; Musawwir
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.v6i3.9778

Abstract

Open Journal Systems (OJS) has become the dominant platform for scholarly journal publication in Indonesia, with more than 8,500 active journals in 2024. However, the growing cyber threats targeting academic infrastructures demand the development of digital forensic methodologies specifically tailored to the OJS ecosystem. This research develops a comprehensive framework for digital forensic investigation on the OJS platform through the analysis of 45 security incidents that occurred in Indonesian scholarly journals during the 2022–2024 period. The proposed Digital Forensic for Academic Publishing (DFAP) methodology covers preservation, acquisition, examination, analysis, and presentation, specifically designed for the OJS architecture. The implementation of this framework in 12 real-world cases demonstrated a success rate of 89.3% in data recovery, 76.2% in perpetrator identification, and 94.4% in operational system restoration, with an average resolution time of 72 hours. The study also identified 15 common vulnerability patterns in Indonesian OJS installations and produced 28 security recommendations that can reduce incident risks by up to 67%. The main contributions of this research include the development of OJS-specific forensic tools, the standardization of investigation procedures for academic institutions, and the establishment of the Indonesian Academic Digital Forensic Database (IADFD) as a knowledge-sharing repository.
Digital Distraction Analysis Using Machine Learning Models to Understand the Impact of Social Media and Risky Use on College Students as Gen Z Mudarris, Mudarris; Anshari, Ahmad; Basirung, Muhammad Romario
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.v6i4.9830

Abstract

Research on digital distraction among Generation Z students shows that excessive social media use has significant impacts on academic, psychological, and social aspects. Gen Z, who on average own a smartphone before the age of 18 and spend 6–8 hours per day on digital platforms, are susceptible to impaired concentration and decreased academic achievement due to multitasking while studying. Analysis using the Extreme Gradient Boosting (XGBoost) machine learning model identified that the dominant factors influencing digital distraction are negative perceptions of mental health due to social media, feelings of guilt after excessive scrolling, and a tendency to lose time due to short content that offers instant gratification. The study also found that the 18–21 age group with a usage duration of more than six hours per day, especially before bed, is most at risk of experiencing sleep disorders, stress, and a decreased GPA. From a social aspect, the habit of spending time online reduces real interactions and weakens students' social skills. Thus, digital distraction is not only an individual problem, but also a collective one, necessitating interventions in the form of digital literacy education, strengthening study time management, limiting device use before bed, and providing alternative positive activities. This research confirms that the use of machine learning is able to provide an accurate predictive picture of risk patterns, so that the results are useful for academics, technology developers, policy makers, and educational institutions to design more targeted mitigation strategies for the most affected generations.
Smart Skincare: Expert System Based on Certainty Factors for Skin Type Identification and Product Selection Raja Gunung, Tar Muhammad; Ningtyas, Alyiza Dwi; Sitepu, Sengli Egani; Rolanda, Vicky; Jinan, Abwabul
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 4 (2025): Desember 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i4.10015

Abstract

This study aims to develop an expert system for selecting skincare products based on skin type using the Certainty Factor method. This method is used to represent the expert's level of confidence in the symptoms that appear on the user's skin, so that the system can provide realistic diagnostic results that are close to human thinking. The research data was obtained through consultations with beauty experts and a review of dermatology literature. The test results show that the system is able to identify the user's skin type with varying degrees of certainty. For example, for patients with combination skin types, the system recommends appropriate skincare products such as Salicylic Acid Serum, Elshe Skin Acne Cleansing Wash, and Azarine Acne Gentle Cleansing Foam. Thus, this Certainty Factor-based expert system is expected to help individuals recognize their skin type and choose facial care products more accurately and effectively. Going forward, this system has the potential to be further developed with the integration of artificial intelligence technology and a broader product database to improve the accuracy and personalization of recommendations.
Optimized Automated Virtual Private Network Management System Utilizing WireGuard and Redis with Delta Processing on MikroTik RouterOS Ardiansyah; Hartinah; Wahyuddin Saputra
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.v6i3.10044

Abstract

In the contemporary network infrastructures, the pursuit of secure, efficient, and easily manageable connectivity has become a fundamental design requirement. WireGuard, a next-generation Virtual Private Network (VPN) protocol, delivers superior cryptographic efficiency and operational performance compared to traditional protocols such as OpenVPN and IPsec. This study presents the development of an optimized automated VPN management framework built upon WireGuard and integrated into MikroTik RouterOS via a web-based control interface. The proposed system automates user registration, account approval, and configuration generation processes, thereby reducing administrative overhead and minimizing human-induced configuration errors. To further enhance system performance, an optimized traffic logging mechanism was implemented using the Redis in-memory database combined with a Delta Processing algorithm, which records only incremental traffic variations instead of cumulative totals. Experimental validation demonstrates that this integration reduces router CPU utilization by 30% and decreases logging latency by 40% relative to conventional polling-based methods. The results confirm that the proposed solution not only achieves full automation of VPN management but also significantly improves the responsiveness and scalability of real-time traffic monitoring. To the best of our knowledge, this research introduces the first Redis-based Delta Processing integration for VPN optimization on MikroTik platforms, offering a lightweight, scalable, and high-throughput solution for multiuser network environments.
AI-Powered Botox Dosage Classification: A Comparative Study of CNN Architectures on Facial Wrinkle Analysis Ayutri Wahyuni; Resky Ayu Dewi Talasari
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.v6i3.10050

Abstract

Botulinum Toxin (BOTOX) injections are widely used as a non-invasive cosmetic treatment to enhance facial appearance. However, determining the optimal dosage still relies on subjective visual assessment by medical professionals, which can lead to inconsistency. This study proposes a model deep learning–based classification framework using convolutional neural networks (CNNs) to automate BOTOX dosage prediction from forehead wrinkle images. Four CNN architectures Inception-V3, ResNet-34, ResNet101-V2, and EfficientNetB2 were evaluated on an augmented dataset of 168 cropped images, equally divided between 2-unit and 4-unit dosages. The dataset was generated through flipping and rotation augmentation to address class imbalance and enhance model generalization. Among the models, EfficientNetB2 achieved the highest accuracy of 92.8%, surpassing Inception-V3 85.7%, ResNet-34 82.1%, and ResNet101-V2 80.3%. The superior performance of EfficientNetB2 reflects its capability to extract fine-grained wrinkle patterns efficiently while maintaining computational efficiency. The novelty of this work lies in integrating CNN-based visual feature extraction with expert-labeled clinical image data for objective BOTOX dosage determination. Although limited by dataset size, this study highlights the potential clinical application of deep learning in supporting accurate, consistent, and data-driven facial aesthetic treatments.
Analyzing the Impact of Data Filtering on Anomaly Detection under Distribution Shift Conditions Talasari, Resky Ayu Dewi; Ayutri Wahyuni
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.v6i3.10051

Abstract

One of the main challenges in anomaly detection for Windows Event Logs and Sysmon is distribution shift, where changes in data distribution invalidate the model's learned normality reference. This study evaluates how data filtering setting value boundaries classified as normal affects the model's ability to handle distribution shifts across three experimental scenarios. This research is among the first to systematically quantify the trade-off between filtering efficiency and model adaptability across varying magnitudes of distribution shifts in anomaly detection systems. The experimental design employs three scenarios: Scenario 1 evaluates filtering under complete cross-environment shift using Dataset A for training and Dataset B for testing, Scenario 2 examines filtering with partial Dataset B training data, and Scenario 3 validates model adaptability without filtering constraints. The goal is to determine whether filtering improves performance under small, adaptable shifts and to measure its impact under large shifts that push the distribution far from the initial training data. Shift magnitude is measured using Jensen Shannon Divergence and Hellinger Distance, followed by evaluation of model performance through precision, recall and F1-score. Results show that filtering can help for minor shifts but substantially impairs adaptation under substantial distributional changes: filtered models remain constrained by prior baseline behavior and fail to learn new patterns, while unfiltered models adapt successfully and maintain accurate detection. These findings suggest critical implications for designing adaptive anomaly detection systems in dynamic operational environments where changes frequently alter normal behavior patterns. Future approaches should incorporate adaptive filtering mechanisms that dynamically adjust baseline boundaries rather than relying solely on static training data distributions.
Transfer Learning-Based CNN for Guava Fruit Disease Detection and Classification Azir Zuldani Pratama; Mustari Lamada; Surianto, Dewi Fatmarani
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.v6i3.10153

Abstract

Guava (Psidium guajava L.) is a tropical plant from the Myrtaceae family and the Psidium genus that is susceptible to diseases such as anthracnose and scab, especially in humid environmental conditions. To accurately detect and classify these diseases, digital image-based technology is needed. However, previous studies still have limitations in dataset size, method variation, and model optimization. Therefore, a study was conducted with the title Guava Fruit Disease Detection and Classification System Using a Convolutional Neural Network (CNN) Based Transfer Learning Model. This study tested four Transfer Learning models, namely MobileNetV2, DenseNet169, VGG16, and EfficientNetV2B5. Based on the test results, the MobileNetV2 model with a combination of activation functions and optimizers (Swish, Swish, Adam) showed the best performance, having the fastest computation time, namely 10 minutes 17 seconds. This proves that the model built is not only superior in accuracy, but also efficient in execution time and can be applied to guava fruit disease detection and classification systems. These findings provide valuable insights into the MobileNetV2 method, combined with Swish, Swish, and Adam, as the best choice for classifying or detecting guava fruit disease levels compared to other methods. This approach can also lead to the development of a widely applicable web-based system for plant disease identification. This offers several benefits for farmers, including faster and more accurate disease detection, efficiency, and cost savings.

Page 12 of 14 | Total Record : 134

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