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All Journal The Indonesian Journal of Computer Science Research Information Technology Education Journal Journal of Embedded Systems, Security and Intelligent Systems Journal of Renewable Energy and Smart Device
Mokhammad Rifqi Tsani
Politeknik Keselamatan Transportasi Jalan
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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IoT-Based Road Blackspot Detection via GPS and Web Integration: Design, EAN-Based Risk Classification, and Field Evaluation Ghani Ridho Rahmatullah; Mokhammad Rifqi Tsani; Raka Pratindy; Siti Shofiah
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.v5i2.267

Abstract

Purpose – Road safety on high-traffic inter-city corridors in Indonesia remains a pressing concern, as drivers receive no real-time hazard notification when approaching zones with statistically elevated crash history. This study develops and evaluates an ESP32-based early warning system that couples GPS-derived positioning with the Equivalent Accident Number (EAN) method to issue graduated audio-visual alerts at road blackspots along the Palur–Semarang bus corridor. Design –  EAN quantifies accident severity by weighting fatalities (12), serious injuries (3), minor injuries (1), and property-damage incidents (0.5); segments exceeding the Upper Control Limit (UCL = 170,52) are designated blackspots, with coordinates stored in onboard flash memory. A SIM800L GPRS module transmits positioning data to a web-based fleet monitoring dashboard. Findings – Field evaluation across 10 GPS sampling points yielded mean errors of 0.00033% for latitude (3.7 m) and 0.00005% for longitude (5.0 m), with maximum deviations of 8.9 m and 17.8 m—both within the 800 m geofencing radius. All 10 from 64 validated corridor zones returned EAN values of 199,5–668,5, each exceeding the UCL, with web-platform outputs matching manual calculations exactly. Eight integrated test scenarios confirmed three-tier audio-visual alert delivery at 800 m, 400 m, and 100 m thresholds with zero missed triggers and zero spurious activations. Research implications – These findings provide preliminary evidence for the technical feasibility of EAN-based blackspot intelligence as a driver vigilance aid; however, full-route longitudinal testing across diverse vehicles and network conditions is required before generalised deployment can be recommended. Originality – This study integrates EAN-based crash severity analysis with real-time GPS tracking in an ESP32 system to deliver tiered early warnings for road blackspots.  
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.
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

Abstract

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

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.
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

Abstract

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.  
Co-Authors Andhika Putra Perdana Brasie Pradana Fahriza Hafidz Agya Ananda Faris Humami Ghani Ridho Rahmatullah Gunawan Helmi Wibowo Langgeng Asmoro Langgeng Asmoro Raga Nur Iman Pribadi Raka Pratindy Rupaka, Anggun Prima Gilang Siti Shofiah Widiandaru, Nanang Okta