HYDROGEN SULFIDE LEAK DETECTION USING THE C4.5 ALGORITHM: OPTIMIZING FEATURE EXTRACTION FOR ENHANCED ACCURACY

  • Mula Agung Barata Universitas Nahdlatul Ulama Sunan Giri
  • Dwi Irnawati Universitas Bojonegoro
  • Ifnu Wisma Dwi Prastya Universitas Nahdlatul Ulama Sunan Giri
  • Dwi Issadari Hastuti Universitas Nahdlatul Ulama Sunan Giri
DOI: https://doi.org/10.52802/aicp.v1i1.1352
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.

References

A. Semary, N., Ahmed, W., Amin, K., PÅ‚awiak, P., & Hammad, M. (2024). Enhancing machine learning-based sentiment analysis through feature extraction techniques. PLOS ONE, 19(2), e0294968. https://doi.org/10.1371/journal.pone.0294968
AR, H., & Palini, R. A. (2022). Analisis Alat Pendeteksi Gas Hidrogen Sulfida Menggunakan Hazard and Operability Study Di Perusahaan Minyak Dan Gas. Jurnal Tekno, 19(1), 36–48. https://doi.org/10.33557/jtekno.v19i1.1661
Bahassine, S., Madani, A., Al-Sarem, M., & Kissi, M. (2020). Feature selection using an improved Chi-square for Arabic text classification. Journal of King Saud University - Computer and Information Sciences, 32(2), 225–231. https://doi.org/10.1016/j.jksuci.2018.05.010
Barata, M. A., Edi Noersasongko, Purwanto, & Moch Arief Soeleman. (2023). Improving the Accuracy of C4.5 Algorithm with Chi-Square Method on Pure Tea Classification Using Electronic Nose. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 7(2), 226–235. https://doi.org/10.29207/resti.v7i2.4687
Barata, M., Ayuni, I. S., Kartini, A. Y., & Alawi, Z. (2024). Algorima K-Means dalam Clustering Produk Skincare untuk Menentukan Strategi Pemasaran. Jurnal Informatika Polinema, 10(3), 421–428. https://doi.org/10.33795/jip.v10i3.5167
Datasets, D., Silfana, F. I., & Barata, M. A. (2024). Using K-NN Algorithm for Evaluating Feature Selection on High. 17(2).
Deni, D. R., Agung Barata, M., & Sahri. (2023). Forecasting Metode Single Exponential Smoothing Dalam Meramalkan Penjualan Barang. Jurnal Informatika Polinema, 9(4), 435–444. https://doi.org/10.33795/jip.v9i4.1405
Guidotti, T. L. (2015). Chapter 8 - Hydrogen sulfide intoxication. In M. Lotti & M. L. B. T.-H. of C. N. Bleecker (Eds.), Occupational Neurology (Vol. 131, pp. 111–133). Elsevier. https://doi.org/https://doi.org/10.1016/B978-0-444-62627-1.00008-1
Harsono, W., Sarno, R., & Sabilla, S. I. (2020). Recognition of original arabica civet coffee based on odor using electronic nose and machine learning. Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, ISemantic 2020, 333–339. https://doi.org/10.1109/iSemantic50169.2020.9234234
Nose, B. E. (n.d.). A False Alarm Reduction Method for a Gas Sensor Based Electronic Nose. 1–19. https://doi.org/10.3390/s17092089
Peker, N., & Kubat, C. (2021). Application of Chi-square discretization algorithms to ensemble classification methods. Expert Systems with Applications, 185(July), 115540. https://doi.org/10.1016/j.eswa.2021.115540
Purnomo, A., Barata, M. A., Soeleman, M. A., & Alzami, F. (2020). Adding feature selection on Naïve Bayes to increase accuracy on classification heart attack disease. Journal of Physics: Conference Series, 1511(1). https://doi.org/10.1088/1742-6596/1511/1/012001
Ross, J., Morgan, Q., & Publishers, K. (1994). Book Review : C4 . 5 : Programs for Machine Learning. 240, 235–240.
Rubright, S. L. M., Pearce, L. L., & Peterson, J. (2018). Environmental Toxicology of Hydrogen Sulfide. 412, 1–13. https://doi.org/10.1016/j.niox.2017.09.011.Environmental
Tambunan, S., & Stefanie, A. (2023). Monitoring Kebocoran Gas Lpg Menggunakan Sensor Mq-2 Pada Rumah Dengan Notifikasi Bot Telegram. JATI (Jurnal Mahasiswa Teknik Informatika), 7(2), 1423–1228. https://doi.org/10.36040/jati.v7i2.6815
Wakhid, S., Sarno, R., Sabilla, S. I., & Maghfira, D. B. (2020). Detection and classification of indonesian civet and non-civet coffee based on statistical analysis comparison using E-Nose. International Journal of Intelligent Engineering and Systems, 13(4), 56–65. https://doi.org/10.22266/IJIES2020.0831.06
Yan, J., Zhang, Z., Lin, K., Yang, F., & Luo, X. (2020). A hybrid scheme-based one-vs-all decision trees for multi-class classification tasks. Knowledge-Based Systems, 198, 105922. https://doi.org/10.1016/j.knosys.2020.105922
Published
2025-01-20
How to Cite
Barata, M., Dwi Irnawati, Ifnu Wisma Dwi Prastya, & Dwi Issadari Hastuti. (2025). HYDROGEN SULFIDE LEAK DETECTION USING THE C4.5 ALGORITHM: OPTIMIZING FEATURE EXTRACTION FOR ENHANCED ACCURACY. PROCEEDING AL GHAZALI International Conference, 2, 348-358. https://doi.org/10.52802/aicp.v1i1.1352
Issue
Vol. 2 (2024): The Future is Now: Adaptation to the World’s Emerging Technologies
Section
Articles

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