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M. Miftach Fakhri
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Program Studi Teknik Komputer, UNM Parangtambung, Daeng Tata Raya, Makassar, South Sulawesi, Indonesia
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INDONESIA
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 198 Documents
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Improving the Efficiency of X-ray Data Management through Database-Based Web Application at RSUD I Lagaligo Musdy Yusuf; M. Hasanuddin; Muh. Hajar Harike; Abdul Malik; Suharsono Bantun
Journal of Embedded Systems, Security and Intelligent Systems Vol 5, No 2 (2024): July 2024
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v5i2.3327

Abstract

The development of information technology in the health sector encourages BLUD RSUD I Lagaligo East Luwu Regency to improve the efficiency of X-ray data management. This research aims to develop a web-based x-ray result database application to speed up access and increase user satisfaction. The methods used include requirements analysis, system design, application development with Laravel and MySQL, as well as functionality testing and User Acceptance Testing (UAT). The results showed that data access time was reduced from 7 minutes to 1 minute and user satisfaction increased from 3.4 to 4.8. This application is proven effective in improving the efficiency and quality of radiology services. Suggestions for further research are integration with broader hospital information systems and development of advanced analytic features.
Digital Transformation of Libraries: Web-based Information System Development with Laravel Ira Widyastuti; Muh. Hajar Harike; Muh. Nasir Takbir; Abdul Malik; Jayanti Yusmah Sari
Journal of Embedded Systems, Security and Intelligent Systems Vol 5, No 2 (2024): July 2024
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v5i2.3330

Abstract

This research addresses the problem of data and information management efficiency in the Regional Library of North Luwu Regency, which is currently still done manually, causing the process of borrowing and returning books to be slow and error-prone. The purpose of this research is to develop a web-based library information system using the Laravel framework to improve efficiency and user satisfaction. The methods used include requirements analysis, system design, development, testing, and evaluation. The results show that the system is able to provide a fast response with an average time of 120-180 ms and has high scalability. Significant improvements were seen in ease of use, access speed, data security, and operational efficiency. Thus, this Laravel-based information system is effective in improving efficiency and user experience and is able to handle future growth.
PENGEMBANGAN PERANGKAT MULTI SENSOR UNTUK PRECISION FARMING MONITORING MEDIA TANAM Muliaty Yantahin; Sulis Marshanda Basri; Satria Gunawan Zain; Ruslan
Journal of Embedded Systems, Security and Intelligent Systems Vol 5, No 2 (2024): July 2024
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v5i2.3928

Abstract

The development of a multi-sensor device and the implementation of a data transmission system to a database for predicting plant suitability parameters are the focus of this paper. This technology enables accurate and continuous data collection for agricultural condition monitoring. The study utilizes a DHT22 sensor for air temperature and humidity, a pH sensor for soil pH, a soil moisture sensor for soil moisture, a rain gauge sensor for rainfall, and a raindrop sensor for rain detection. The data from these sensors are managed by an ESP32 microcontroller and transmitted to a Firebase database. The multi-sensor device was functionally tested and compared with valid reference sensor readings. The results show that all the multi-sensor devices and the recording system in the Firebase database functioned well. The average measurement error for the DHT22 temperature sensor was 1.15°C, with 2.9% for humidity, 0.72% for the pH sensor, and the soil moisture sensor showed 100% accuracy. This system can be applied as a parameter for predicting the suitability of plant types with soil and environmental conditions.
MITIGASI KEDIP TEGANGAN AKIBAT BEBAN TANUR BUSUR LISTRIK MELALUI KOMPENSATOR SERI: STUDI KASUS PT. KRAKATAU STEEL Muliaty Yantahin
Journal of Embedded Systems, Security and Intelligent Systems Vol 5, No 2 (2024): July 2024
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v5i2.4300

Abstract

The goal of this research project is to show how the series capacitor compensator can reduce the effects of voltage-dip problem in an electrical arc furnace operation. The case of PT. Krakatau Steel (a major steel company in Indonesia) is taken into consideration. A 195 KVA/220 arc furnace operating in the company is known to cause votage-dip problem in the surrounding distribution system supplied by a 400 MW electrical power plant. The dip is recorded as ranging from 8% to 17%.
Experimental Assessment of Mining Waste Utilization in Cementitious Construction Materials: Strength, Water Absorption, and Durability Performance Ranggu, Ruth Bunga; Sani, Hendra; Sumbung, Dwi Yolanda
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 1 (2026): March 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i1.2610

Abstract

Purpose – This study aims to comparatively evaluate the potential of three Indonesian mining and industrial waste streams gold-mine tailings, ferronickel slag, and coal fly ash for utilization in cementitious materials within a unified experimental framework, supporting sustainable construction and circular economy strategies. Design/methods/approach – A screening-level experimental design was applied by incorporating tailings as a 10% fine-aggregate replacement, and ferronickel slag and fly ash as 10% cement replacements. Performance was assessed using compressive strength, water absorption, and durability-related behavior under 28 days of 5% NaCl immersion, with conventional concrete as a control. Statistical analysis included one-way ANOVA and Tukey HSD, alongside pooled correlation analysis. Findings – Results indicate that waste type significantly influences performance. The slag mixture (S-10) achieved the best balance, with a 28-day compressive strength of 28.93 MPa (90.32% of control), low water absorption (4.50%), and a “Good” durability rating. Tailings-based concrete showed moderate performance, while the fly-ash mixture exhibited the weakest chloride resistance. Statistical tests confirmed significant differences across all groups (p < 0.05), and a strong inverse correlation between compressive strength and water absorption (r = −0.798, p = 0.0019). Research implications/limitations – The study provides initial comparative insights but is limited by its exploratory NaCl immersion method and absence of advanced characterization (XRF, XRD, SEM) and leaching analysis, restricting mechanistic and environmental interpretation. Originality/value – This research contributes a novel comparative framework by evaluating multiple waste streams and utilization pathways under a standardized protocol, offering practical screening evidence to guide material selection in sustainable construction
Hybrid Machine Learning Models Based on MobileNetV2 Feature Extraction for Robusta Coffee Leaf Disease Classification Rahmatia; Rasyid, Muh. Rafli; Arifin, Nurhikma
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 1 (2026): March 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i1.11896

Abstract

Purpose – This study aims to evaluate the effectiveness of a hybrid machine learning approach for classifying robusta coffee (Coffea canephora) leaves into healthy and diseased categories, addressing challenges in manual field inspection and limited comparative analyses across classifiers. Design/methods/approach – A hybrid framework was implemented by combining MobileNetV2 as a feature extractor with four machine learning classifiers: Random Forest, K-Nearest Neighbor, Linear Support Vector Machine, and Gaussian Naive Bayes. The dataset comprised 1,560 images (791 healthy and 769 diseased), split into 70% training, 10% validation, and 20% testing using a hash-based grouped strategy to prevent data leakage from duplicate images. Model performance was evaluated using accuracy, F1-score, ROC-AUC, and McNemar’s statistical test. Findings – Gaussian Naive Bayes achieved the highest accuracy (93.89%) and F1-score (93.85%), while Random Forest obtained the highest ROC-AUC (96.94%). However, McNemar’s test showed no statistically significant differences among the models (p > 0.05), indicating comparable classification performance. The results demonstrate that lightweight hybrid approaches can achieve strong performance even with relatively small datasets. Research implications/limitations – The study is limited to binary classification and a relatively small dataset, which may restrict generalizability to more complex, multi-class disease scenarios. Further research with larger and more diverse datasets is recommended. Originality/value – This study provides a systematic comparison of multiple machine learning classifiers using a unified MobileNetV2 feature representation, offering practical insights into efficient and reliable approaches for early-stage coffee leaf disease screening in resource-constrained environments.
IoT Multi-Gas Monitoring for Bus Cabin Air Quality Fahriza Hafidz Agya Ananda; Mokhammad Rifqi Tsani; Gunawan; Faris Humami
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 1 (2026): March 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Purpose – This study aims to develop an Internet of Things (IoT)-based multi-gas monitoring system to detect hazardous gas accumulation inside bus cabins and enhance passenger safety through early warning and automated response mechanisms. Design/methods/approach – An experimental and system development approach was employed to design and implement the proposed system using an ESP32 microcontroller integrated with MiCS-5524 and MQ-series sensors. The system monitors carbon monoxide (CO), hydrocarbons (HC), nitrogen oxide (NO), and carbon dioxide (CO₂), with data transmitted in real time to a cloud platform and mobile application developed using MIT App Inventor. Calibration was conducted using real vehicle exhaust emissions, and system performance was evaluated based on measurement error, response time, and communication delay. Findings – The system achieved average measurement errors ranging from 3.38% to 4.68% across all sensors, with response times between 4.9 s and 6.5 s and data transmission delays between 1.1 s and 1.5 s. The system successfully detected hazardous gas conditions and automatically activated alarms and ventilation when predefined thresholds were exceeded. Multi-node deployment revealed non-uniform gas distribution inside the cabin, confirming the necessity of distributed sensing. Research implications/limitations – The system demonstrates reliable indicative performance as an early warning prototype; however, the use of MOS sensors introduces cross-sensitivity, limiting selective gas quantification. The study is also limited to controlled testing conditions and requires further validation under real driving environments. Originality/value – This study contributes by integrating multi-gas monitoring, IoT-based real-time communication, and automated ventilation control within a single embedded system for bus cabins, providing a practical early warning solution not addressed in prior single-gas or non-IoT-based approaches.
Design and Build an Intelligent Vehicle Access System Using Face Recognition and RFID-Based E-SIM Viky Dwi Nugraha; M Iman Nur Hakim; Ethys Pranoto; Faris Humami
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 1 (2026): March 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Purpose – This study aims to design and develop an intelligent vehicle access system that enhances security through a two-factor authentication mechanism integrating face recognition and RFID-based electronic driver identification (E-SIM). Design/methods/approach – The research adopts a Research and Development (R&D) approach, including system design, implementation, and evaluation. The system is built on a Raspberry Pi 4 platform and integrates face recognition using the Histogram of Oriented Gradients (HOG) method with RFID UID verification. Additional features include GPS-based tracking and Telegram-based real-time notifications. Performance evaluation is conducted using confusion matrix metrics and experimental testing under varying environmental conditions. Findings – The proposed system achieves 95% accuracy, 95.92% precision, 94% recall, and an F1-score of 94.95%. The system demonstrates good performance in preventing unauthorized access, with only two false acceptance cases. Performance remains stable under moderate lighting and short distances but decreases under low illumination and longer distances. The GPS module provides reliable tracking with an average positioning error of approximately 5.06 meters. In terms of real-time performance, the system exhibits an average latency of approximately 6.84 seconds per authentication cycle, which remains acceptable for practical vehicle access applications. Research implications/limitations – The system demonstrates strong performance as a functional prototype; however, it remains vulnerable to face spoofing and RFID cloning due to the absence of liveness detection and encrypted communication. Environmental factors such as lighting and distance also affect recognition accuracy. Originality/value – This study contributes by integrating biometric and possession-based authentication within a standalone embedded system, enhanced with IoT features for real-time monitoring. Unlike prior single-factor approaches, the proposed system improves security robustness while maintaining practical usability.

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