TY  - JOUR
PB  - PROCEEDING Al Ghazali Internasional Conference  The Future is Now: Adaptation to the World?s Emerging Technologies
A1  - Mula Agung Barata, Barata
A1  - Dwi Irnawati, Irnawati
A1  - Ifnu Wisma Dwi Prastya, Prastya
A1  - Dwi Issadari Hastuti, Hastuti
JF  - PROCEEDING Al Ghazali Internasional Conference  The Future is Now: Adaptation to the World?s Emerging Technologies
SN  - 3032-5587
N2  - Hydrogen sulfide (H?S) 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 H?S 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 H?S 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 H?S 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.
EP  - 358
KW  - C4.5
KW  -  features extraction
KW  -  gas leak
KW  -  hydrogen sulfide
AV  - public
ID  - repository4859
SP  - 348
Y1  - 2026/01/01/
VL  - 1
UR  - https://repository.unigoro.ac.id/id/eprint/4859/
TI  - Hydrogen Sulfide Leak Detection Using The C4.5 Algorithm: Optimizing Feature Extraction For Enhanced Accuracy
ER  -