eprintid: 4859
rev_number: 12
eprint_status: archive
userid: 273
dir: disk0/00/00/48/59
datestamp: 2026-05-06 01:49:55
lastmod: 2026-05-06 01:49:55
status_changed: 2026-05-06 01:49:55
type: article
metadata_visibility: show
creators_name: Mula Agung Barata, Barata
creators_name: Dwi Irnawati, Irnawati
creators_name: Ifnu Wisma Dwi Prastya, Prastya
creators_name: Dwi Issadari Hastuti, Hastuti
corp_creators: Universitas Bojonegoro
title: Hydrogen Sulfide Leak Detection Using The C4.5 Algorithm: Optimizing Feature Extraction For Enhanced Accuracy
ispublished: pub
subjects: QD
divisions: mr
keywords: C4.5, features extraction, gas leak, hydrogen sulfide
abstract: Hydrogen sulfide (H₂S) is a toxic and potentially hazardous gas commonly found in industrial environments, where leaks can lead to serious health and safety risks. Effective detection of H₂S leaks is essential for preventing accidents and ensuring workplace safety. This study explores the implementation of the C4.5 algorithm combined with optimized feature extraction techniques to improve the accuracy of H₂S leak detection. By utilizing feature extraction, significant attributes of gas leak indicators are identified and analyzed, enhancing the classification accuracy of the C4.5 algorithm. The experimental results demonstrate that optimized feature extraction can significantly improve the algorithm’s ability to detect H₂S leaks promptly and accurately. The proposed method not only offers a reliable solution for gas leak detection but also contributes to safer industrial monitoring practices. This study highlights the potential of machine learning techniques, particularly decision tree-based methods, to advance environmental safety through intelligent monitoring systems.
date: 2026-01-01
publisher: PROCEEDING Al Ghazali Internasional Conference  The Future is Now: Adaptation to the World’s Emerging Technologies
full_text_status: public
publication: PROCEEDING Al Ghazali Internasional Conference  The Future is Now: Adaptation to the World’s Emerging Technologies
volume: 1
pagerange: 348-358
refereed: TRUE
issn: 3032-5587
citation:   Mula Agung Barata, Barata and Dwi Irnawati, Irnawati and Ifnu Wisma Dwi Prastya, Prastya and Dwi Issadari Hastuti, Hastuti  (2026) Hydrogen Sulfide Leak Detection Using The C4.5 Algorithm: Optimizing Feature Extraction For Enhanced Accuracy.  PROCEEDING Al Ghazali Internasional Conference The Future is Now: Adaptation to the World’s Emerging Technologies, 1.  pp. 348-358.  ISSN 3032-5587     
document_url: https://repository.unigoro.ac.id/id/eprint/4859/1/LIT%202024%20Desember%20Mula%20Agung%20Barata%2C%20Dwi%20Irnawati%2C%20Ifnu%20Wisma%20Dwi%20Prastya%2C%20Dwi%20Issadari%20Hastuti.pdf