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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

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.

Occupational and Operational Risk Assessment in Trans Jogja Public Transportation Using HIRADC and FTA Adhe Yuliawan; Rifano Rifano; Dwi Wahyu Hidayat; Mokhammad Rifqi Tsani
JTI: Jurnal Teknik Industri Vol 12 No 1 (2026): June 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/jti.v12i1.39669

Urban public transportation operations are exposed to a range of operational, technical, and human-factor risks that may lead to traffic accidents, occupational injuries, and service disruptions. However, previous transportation safety studies have rarely integrated workshop hazards, operational routes, and driver-related risks within a unified risk-management framework. Therefore, this study aims to develop an integrated operational risk-control framework for urban public transportation systems using the Hazard Identification, Risk Assessment, and Determining Control (HIRADC) and Fault Tree Analysis (FTA) methods. This study employed a mixed-method descriptive approach involving workshop activities, operational routes, and driver-related operational factors in Trans Jogja operations. Data were collected through observations, interviews, and questionnaires involving mechanics and drivers. The HIRADC analysis identified several high-risk activities related to workshop operations, traffic conditions, and driver performance, particularly welding activities, manual handling, congested intersections, aggressive road-user behavior, and driver fatigue. Furthermore, the FTA results revealed that accident risks were influenced by interactions among human, managerial, technical, and environmental factors. Recommended control measures include stricter implementation of standard operating procedures, defensive driving training, ergonomic improvements, optimization of driver work-rest schedules, and traffic engineering improvements. The findings demonstrate that safety risks in urban public transportation systems are multidimensional and interconnected across operational domains. This study contributes by integrating HIRADC-based risk assessment with FTA-based root cause analysis to support comprehensive transportation safety risk management. Keywords: HIRADC, FTA, Risk Control, Trans Jogja, Transportation Safety.

ARTIFICIAL INTELLIGENCE PREDIKSI DIAGNOSA KERUSAKAN MOBIL DENGAN METODE NAIVE BAYES BERBASIS WEBSITE Mokhammad Rifqi Tsani; Brasie Pradana; Langgeng Asmoro
The Indonesian Journal of Computer Science Research Vol. 3 No. 1 (2024): Januari
Publisher : Hemispheres Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59095/ijcsr.v3i1.97

Banyak pakar di bidang ilmu komputer berfokus dalam pengembangan kecerdasan buatan (AI), juga dikenal sebagai artificial intelligence (AI). AI adalah bidang studi yang bertujuan untuk membuat komputer bertindak dan berpikir seperti manusia. Banyak implementasi AI dalam bidang komputer, misalnya Decision Support System (Sistem Penunjang Keputusan), Robotic, Natural Language (Bahasa Alami), Neural Network (Jaringan Saraf), dan lain-lain. Seperti bidang otomotif yang sangat membutuhkan konsultasi perbaikan mobil yang cepat untuk mendeteksi dan menangani kerusakan, Salah satu bidang AI yang paling menarik adalah sistem pakar. Karena masalah ini, diperlukan suatu sistem yang dapat mengatasi masalah di atas. Dengan menggunakan Algoritma Naive Bayes pada sistem AI, hasil diagnose AI ini diharapkan akan sangat membantu pemilik mobil. Penelitian ini menerapkan metode penelitian waterfall SDLC (Software Development Life Cycle). Bahasa pemrograman Website dengan database MySQL digunakan untuk membuat aplikasinya. Aplikasi ini berfungsi untuk membantu sopir dan pengguna mobil mengidentifikasi gejala kerusakan dini pada kendaraan mereka. Selain itu, aplikasi ini dapat digunakan sebagai alat untuk mengajar taruna PKTJ Tegal tentang keahlian otomotif

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 | DOI: 10.59562/jessi.v7i1.2616

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.

The Smart Battery Safety and Anti-Theft Monitoring System for Electric Bicycles with Automatic Cut-Off and Dual-Channel Notification Andhika Putra Perdana; Mokhammad Rifqi Tsani; Helmi Wibowo; Nanang Okta Widiandaru
Journal of Renewable Energy and Smart Device Vol. 3 No. 2 April 2026
Publisher : PT. Global Research Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66314/joresd.v3i2.685

The rapid growth of electric bicycle usage in Indonesia has been accompanied by rising safety incidents, particularly those related to battery thermal runaway and theft. This research presents the design and implementation of an integrated monitoring and security system for electric bicycles using the ESP32 microcontroller, PZEM-017, DS18B20, and Neo-6M GPS module, combined with a web-based dashboard and Telegram bot notification. The system was developed using the Research and Development (R&D) method with a four-parameter monitoring scheme covering voltage, current, temperature, and geospatial coordinates. Experimental results from twenty data points per sensor demonstrated excellent accuracy: DS18B20 achieved an average error of 1.133%, PZEM-017 achieved 1.224% for voltage and 1.787% for current, while the Neo-6M module achieved 0.000575% and 0.000042% for latitude and longitude respectively. The automatic cut-off mechanism successfully operated in all six tested scenarios, and the Telegram-website integration delivered notifications with an average delay of two seconds. These findings confirm that the proposed system improves safety and security of electric bicycles through real-time multi-parameter monitoring and remote intervention capability. Unlike prior systems that address monitoring or security in isolation, this work is the first to unify real-time multi-parameter battery protection, automatic cut-off, geofencing, and dual-channel notification within a single low-cost ESP32-based platform tailored for urban electric bicycle users in Indonesia. The practical relevance of this integration is particularly significant given the accelerating adoption of electric bicycles as primary short-distance transportation in densely populated Indonesian cities, where charging-related fire incidents and theft cases have reached critical levels.

Pengembangan Sistem Pendeteksi Tekanan Ban Berbasis Internet of Things untuk Otomatisasi Inspeksi Kendaraan Benita Aryani; Mokhammad Rifqi Tsani
Infotekmesin Vol 16 No 2 (2025): Infotekmesin: Juli 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i2.2763

Tire pressure inspection is a crucial procedure at PT HMMI based on the Part Inspection Standard (PIS), with a recommended pressure of 105-125 Psi for buses. Currently, inspections are still performed manually, deemed inefficient and prone to human error. To address this and meet inspection standards, this research aims to develop an Internet of Things (IoT)-based bus tire pressure detection system. The Research and Development (R&D) method was applied to design and build this system. The developed system utilizes a pressure transmitter sensor integrated with an ESP32 microcontroller, equipped with LCD, LED, and buzzer outputs. Pressure measurement data is transmitted in real-time and stored in Google Spreadsheet for paperless documentation. Functional testing of the system on buses demonstrated the sensor's detection capability within 2-3 seconds with all outputs functioning optimally. Accuracy test results showed excellent performance, reaching 99.44% with an average error of only 0.56% after calibration with 30 pressure parameters. This system successfully proved its capability as an effective solution for automatically and accurately monitoring bus tire pressure, supporting the achievement of PIS standards and enhancing the efficiency of inspection processes in the automotive industry.

Object Detection of Motor Vehicle Suspension Systems Based on the YOLOv8 Algorithm: Supporting Sustainable Industry and Innovation (SDG 9) Helmi Wibowo; Nurul Muzakki Rihhadatul ‘Aisy; Muhammad Iman Nur Hakim; Mokhammad Rifqi Tsani; Setya Wijayanta
Journal of Current Studies in SDGs Vol. 3 No. 2 (2027): June
Publisher : Sekolah Tinggi Agama Islam Sabilul Muttaqin Mojokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63230/jocsis.3.2.192

Objective: To develop an object detection system for identifying motor vehicle suspension system components using the YOLOv8 algorithm. Specifically, this study focused on improving the efficiency and accuracy of undercarriage inspection processes, which are commonly conducted manually and require technical knowledge to recognize suspension components and detect potential damage. This research contributes to automotive inspection innovation and supports the development of sustainable industrial technology in accordance with Sustainable Development Goal 9 (Industry, Innovation, and Infrastructure). Method: The study employed a computer vision-based approach using the YOLOv8 object detection algorithm for recognizing suspension system components in motor vehicles. The dataset consisted of 1000 suspension system images collected from mandatory vehicle inspection activities at motor vehicle testing facilities. Results: The results showed that the YOLOv8-based detection model could identify suspension system components with an accuracy of up to 95%. Furthermore, the trained model successfully detected oil leakage damage on shock absorber components with an accuracy of 92%. The evaluation results indicate that the proposed system can effectively recognize suspension components under different inspection conditions and provide reliable assistance for vehicle undercarriage inspection processes. Novelty: The study provides a novel implementation of the YOLOv8 deep learning algorithm for automated suspension system inspection in motor vehicles by integrating computer vision technology into the vehicle testing process. The developed system contributes to automotive technology innovation and supports the advancement of smart inspection infrastructure in line with SDG 9 (Industry, Innovation, and Infrastructure).

Artificial Intelligence Based Detection of Over Dimension and Overload (ODOL) Vehicles Using the YOLO Algorithm for Sustainable Transportation Infrastructure (SDG 9) Mokhammad Rifqi Tsani; Humam Eka Saputra; Muh Irhas Rafiqi; Hafid Yusuf Ramadhan
Journal of Current Studies in SDGs Vol. 3 No. 2 (2027): June
Publisher : Sekolah Tinggi Agama Islam Sabilul Muttaqin Mojokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63230/jocsis.3.2.193

Objective: To develop an artificial intelligence-based detection system for identifying Over-Dimension and Overload (ODOL) vehicles using the YOLO algorithm. Specifically, this study focused on improving the efficiency of ODOL vehicle monitoring, which is important for maintaining road safety, reducing infrastructure damage, and supporting effective transportation regulation. This research contributes to the development of smart transportation systems and sustainable infrastructure innovation in accordance with Sustainable Development Goal 9 (Industry, Innovation, and Infrastructure). Method: The study employed a computer vision-based approach using YOLOv8m and YOLOv10n algorithms for ODOL vehicle detection. The dataset consisted of vehicle images containing over-dimension vehicles, normal vehicles, and trucks collected from digital image sources. Results: The experimental results showed that YOLOv8m achieved better performance compared with YOLOv10n in detecting ODOL vehicles. YOLOv8m obtained a confusion matrix value of 78%, a precision-recall curve value of 81.7%, precision of 91.6%, and recall of 90%. Although YOLOv10n achieved a higher recall value of 93%, its overall detection performance was lower, with a confusion matrix value of 59%, precision-recall curve value of 72.3%, and precision of 89.9%. The implementation of YOLOv8m into an IoT-based detection system successfully enabled real-time data transmission and storage in a database, demonstrating its capability for practical ODOL vehicle monitoring applications. Novelty: The study provides a novel implementation of YOLO-based artificial intelligence technology for real-time ODOL vehicle detection by integrating deep learning, computer vision, and IoT infrastructure. The developed system supports the advancement of intelligent transportation infrastructure and contributes to sustainable innovation in transportation management in line with SDG 9 (Industry, Innovation, and Infrastructure).

Object Detection of Motor Vehicle Suspension Systems Based on the YOLOv8 Algorithm: Supporting Sustainable Industry and Innovation (SDG 9) Helmi Wibowo; Nurul Muzakki Rihhadatul ‘Aisy; Muhammad Iman Nur Hakim; Mokhammad Rifqi Tsani; Setya Wijayanta
Journal of Current Studies in SDGs Vol. 3 No. 2 (2027): June
Publisher : Sekolah Tinggi Agama Islam Sabilul Muttaqin Mojokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63230/jocsis.3.2.192

Objective: To develop an object detection system for identifying motor vehicle suspension system components using the YOLOv8 algorithm. Specifically, this study focused on improving the efficiency and accuracy of undercarriage inspection processes, which are commonly conducted manually and require technical knowledge to recognize suspension components and detect potential damage. This research contributes to automotive inspection innovation and supports the development of sustainable industrial technology in accordance with Sustainable Development Goal 9 (Industry, Innovation, and Infrastructure). Method: The study employed a computer vision-based approach using the YOLOv8 object detection algorithm for recognizing suspension system components in motor vehicles. The dataset consisted of 1000 suspension system images collected from mandatory vehicle inspection activities at motor vehicle testing facilities. Results: The results showed that the YOLOv8-based detection model could identify suspension system components with an accuracy of up to 95%. Furthermore, the trained model successfully detected oil leakage damage on shock absorber components with an accuracy of 92%. The evaluation results indicate that the proposed system can effectively recognize suspension components under different inspection conditions and provide reliable assistance for vehicle undercarriage inspection processes. Novelty: The study provides a novel implementation of the YOLOv8 deep learning algorithm for automated suspension system inspection in motor vehicles by integrating computer vision technology into the vehicle testing process. The developed system contributes to automotive technology innovation and supports the advancement of smart inspection infrastructure in line with SDG 9 (Industry, Innovation, and Infrastructure).

Artificial Intelligence Based Detection of Over Dimension and Overload (ODOL) Vehicles Using the YOLO Algorithm for Sustainable Transportation Infrastructure (SDG 9) Mokhammad Rifqi Tsani; Humam Eka Saputra; Muh Irhas Rafiqi; Hafid Yusuf Ramadhan
Journal of Current Studies in SDGs Vol. 3 No. 2 (2027): June
Publisher : Sekolah Tinggi Agama Islam Sabilul Muttaqin Mojokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63230/jocsis.3.2.193

Objective: To develop an artificial intelligence-based detection system for identifying Over-Dimension and Overload (ODOL) vehicles using the YOLO algorithm. Specifically, this study focused on improving the efficiency of ODOL vehicle monitoring, which is important for maintaining road safety, reducing infrastructure damage, and supporting effective transportation regulation. This research contributes to the development of smart transportation systems and sustainable infrastructure innovation in accordance with Sustainable Development Goal 9 (Industry, Innovation, and Infrastructure). Method: The study employed a computer vision-based approach using YOLOv8m and YOLOv10n algorithms for ODOL vehicle detection. The dataset consisted of vehicle images containing over-dimension vehicles, normal vehicles, and trucks collected from digital image sources. Results: The experimental results showed that YOLOv8m achieved better performance compared with YOLOv10n in detecting ODOL vehicles. YOLOv8m obtained a confusion matrix value of 78%, a precision-recall curve value of 81.7%, precision of 91.6%, and recall of 90%. Although YOLOv10n achieved a higher recall value of 93%, its overall detection performance was lower, with a confusion matrix value of 59%, precision-recall curve value of 72.3%, and precision of 89.9%. The implementation of YOLOv8m into an IoT-based detection system successfully enabled real-time data transmission and storage in a database, demonstrating its capability for practical ODOL vehicle monitoring applications. Novelty: The study provides a novel implementation of YOLO-based artificial intelligence technology for real-time ODOL vehicle detection by integrating deep learning, computer vision, and IoT infrastructure. The developed system supports the advancement of intelligent transportation infrastructure and contributes to sustainable innovation in transportation management in line with SDG 9 (Industry, Innovation, and Infrastructure).

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