@article{repository4859,
            year = {2026},
          volume = {1},
       publisher = {PROCEEDING Al Ghazali Internasional Conference  The Future is Now: Adaptation to the World's Emerging Technologies},
           pages = {348--358},
         journal = {PROCEEDING Al Ghazali Internasional Conference  The Future is Now: Adaptation to the World's Emerging Technologies},
           title = {Hydrogen Sulfide Leak Detection Using The C4.5 Algorithm: Optimizing Feature Extraction For Enhanced Accuracy},
           month = {January},
        keywords = {C4.5, features extraction, gas leak, hydrogen sulfide},
        abstract = {Hydrogen sulfide (H{$_2$}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{$_2$}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{$_2$}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{$_2$}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.},
            issn = {3032-5587},
             url = {https://repository.unigoro.ac.id/id/eprint/4859/},
          author = {Mula Agung Barata, Barata and Dwi Irnawati, Irnawati and Ifnu Wisma Dwi Prastya, Prastya and Dwi Issadari Hastuti, Hastuti}
}