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All Journal Seruni - Seminar Riset Unggulan Nasional Inoformatika dan Komputer Jurnal Penelitian dan Pengabdian Kepada Masyarakat UNSIQ Jurnal Qua Teknika Jurnal Daya Mas : Media Komunikasi dan Informasi Hasil Pengabdian dan Pemberdayaan Masyarakat Pilar Teknologi : Jurnal Penelitian Ilmu-ilmu Teknik Journal of Computer and Information Systems Ampera SPEED - Sentra Penelitian Engineering dan Edukasi Jurnal Ekonomi dan Teknik Informatika International Journal of Artificial Intelligence and Science
Candra Budi Susila
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Public Health Social Sciences Other

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Journal : International Journal of Artificial Intelligence and Science

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Web-Based Electric Bicycle Fault Diagnosis Using the Backward Chaining Method Nugroho, Satrio Wicaksono; Dwi Nor Amadi; Pradityo Utomo; Candra Budi Susila
International Journal of Artificial Intelligence and Science Vol. 2 No. 2 (2025): September
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/IJAIS.v2i2.35

Abstract

This study aims to develop a web-based expert system for diagnosing electric bicycle faults using the backward chaining method. It addresses the limitation of previous systems that did not support user input of fault hypotheses. The research stages include literature review, data collection (31 faults and 5 symptoms), implementation of web-based inference, and black box testing. The results demonstrate that the system successfully accommodates user-input hypotheses and related symptoms, then matches them with rules to generate diagnoses. Functional testing confirms all features operate as intended. The research novelty lies in: (1) the first comprehensive knowledge base for electric bicycles (31 faults), (2) an interactive web interface supporting hypothesis input, and (3) dynamic database storage for rule updates.
Co-Authors Amadi, Dwi Nor Arif - Diana Rendrarini Dwi Nor Amadi Erlina Idola Ganis Fatmawati, Laily Meifani, Nely Indra Muhammad Syifa'ul Aqhyar Nanang Junaedi Nugroho, Satrio Wicaksono Pradityo Utomo Ramadian Agus Triyono - Universitas Surakarta Rendrarini, Diana Rohmad, Nur Sukadi Sukadi Sukadi Susilo, Hendro Tomi Tristono Tomi Tristono