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All Journal Journal of Student Research Information Technology Education Journal Journal of Embedded Systems, Security and Intelligent Systems
Faris Humami
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Humanities Computer Science & IT Economics, Econometrics & Finance Education Public Health Social Sciences

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Pendekatan Machine Learning untuk Analisis dan Visualisasi Data Jembatan Timbang Siti Shofiah; Faris Humami; M. Iman Nur Hakim; Azimatun Lissyifa; Agus Siswono
Journal of Student Research Vol. 2 No. 1 (2024): Januari: Journal of Student Research
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jsr.v2i1.2666

Abstract

In this research, a machine learning approach, especially a decision tree model, is implemented to improve the analysis and visualization of weighbridge data in Indonesia. The evaluation results show that the decision tree model provides better insight in predicting the carrying capacity, dimensions and loading procedures of vehicles. The advantage of this model lies in its combination of low Mean Squared Error (MSE) and high R-squared, indicating its effectiveness in capturing data variance and providing accurate predictions. The use of decision tree models can be a valuable tool in improving the visualization of bridge weighing data, allowing users to gain additional insights and understand the complex dynamics within the data. In addition, the model's adaptability to various types of data makes it a versatile analysis tool. The positive implications of using this model open up opportunities to understand more deeply the logic of predictions and make more informed decisions. As a suggestion, increasing the number and quality of weighing equipment, wider application of information and communication technology, human resource training, and cross-sector collaboration can further strengthen weighbridge management in Indonesia.
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

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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

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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.
Expert System for Bus Vehicle Failure Diagnosis Using the Decision Tree Method: A Web-Based Approach for Operational Fleet Management Raga Nur Iman Pribadi; Mokhammad Rifqi Tsani; Gunawan; Faris Humami
Information Technology Education Journal Vol. 5, No. 2, May (2026)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v5i2.270

Abstract

Purpose – This study aims to develop a web-based expert system to support initial fault identification in bus fleets, addressing the limitations of manual, experience-based diagnostics that are often subjective and time-consuming in operational environments. Design/methods/approach – The system was developed using a rule-based approach with a Decision Tree framework, where entropy and information gain were used to structure expert knowledge into an interpretable diagnostic hierarchy. The development followed the SDLC Waterfall model and incorporated 30 fault categories across six subsystems. Validation included entropy-based computation on the AC subsystem and expert-scenario testing across all subsystems (90 cases). System usability was evaluated using the System Usability Scale (SUS), and functional testing was conducted using Black Box Testing. Findings – The system achieved an accuracy of 97.78% under expert-defined diagnostic scenarios. However, this result reflects rule-consistency performance within structured scenarios and should not be interpreted as real-world diagnostic accuracy. The SUS evaluation yielded a score of 82.07, categorized as “excellent,” and all functional modules operated correctly based on Black Box Testing.Research limitations/implications – The validation is based on expert-defined scenarios rather than independently observed operational failure data, limiting generalizability. In addition, overlapping symptoms may introduce ambiguity in certain diagnostic conditions. Originality/value – This study contributes an interpretable expert system that integrates entropy-based attribute prioritization within a web-based fleet management context, providing structured diagnostic support for non-technical operational personnel.
Co-Authors Agus Siswono Azimatun Lissyifa Ethys Pranoto Fahriza Hafidz Agya Ananda Gunawan M Iman Nur Hakim M. Iman Nur Hakim Mokhammad Rifqi Tsani Raga Nur Iman Pribadi Siti Shofiah Viky Dwi Nugraha