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All Journal Jurnal Transportasi Jurnal Informatika dan Teknik Elektro Terapan SISFOTENIKA CogITo Smart Journal JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal Restikom : Riset Teknik Informatika dan Komputer Jurnal Teknologi Transportasi dan Logistik Jurnal Manajemen Informatika & Teknologi Information Technology Education Journal Journal of Embedded Systems, Security and Intelligent Systems Power Elektronik : Jurnal Orang Elektro
Mokhammad Rifqi Tsani
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Transportation Other

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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.
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 - Nurhadianto Annisa, Shafirza Awaludin Febrianto Budi Mardikawati Dendy Dimas Dwi Saputra Djarot Suradji Dwi Wahyu Hidayat Edi Purwanto Edo Panda Mesakh Silaban Fahriza Hafidz Agya Ananda Faris Humami Fauzi, Muhammad Bagas Fu’ad Febriyana, Bagus Gunawan Hilyatul Jannah Humami, Faris Ibnu Adib Adika LENTERA GINARIS AL DINSQI Mochammad Hanafi Saputra Muhammad Heru Dwi Nugroho Muhammad Iman Nur Hakim Nurul Fitriani Raga Nur Iman Pribadi Ramadhani, Ilham Wijaya, Yogie