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All Journal Journal of Mathematical and Fundamental Sciences Communication in Biomathematical Sciences Jurnal Teknik Informatika (JUTIF) Jambura Journal of Biomathematics (JJBM) Makara Journal of Science Journal of Engineering and Technological Sciences
Bevina D Handari
Department Of Mathematics, Universitas Indonesia, Depok 16424, Indonesia
Aerospace Engineering Astronomy Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Mathematics Physics Social Sciences

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Journal : Journal of Engineering and Technological Sciences

 1 
Classifying Coal Mine Pillar Stability Areas with Multiclass SVM on Ensemble Learning Models Hertono, Gatot Fatwanto; Wattimena, Ridho Kresna; Mendrofa, Gabriella Aileen; Handari, Bevina Desjwiandra
Journal of Engineering and Technological Sciences Vol. 56 No. 1 (2024)
Publisher : Directorate for Research and Community Services, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.eng.technol.sci.2024.56.1.8

Abstract

Pillars are key structural components in coal mining. The safety requirements of underground coal mines are non-negotiable. Accurately classifying the areas of pillar stability helps ensure safety in coal mines. This study aimed to classify new pillar stability categories and their stability areas. The multiclass support vector machine (SVM) method was implemented with two types of kernel functions (polynomial and radial basis function (RBF) kernels) on pillar stability data with four new categories: failed or intact, either with or without an appropriate safety factor. This classification uses three basic ensemble learning models: Artificial Neural Network-Backpropagation Rectified Linear Unit, Artificial Neural Network-Backpropagation Exponential Linear Unit, and Artificial Neural Network-Backpropagation Gaussian Error Linear Unit. The results with four data proportions and ten experiments had an average accuracy and standard deviation of 92.98% and 0.56%-1.64% respectively. The accuracies of the multiclass SVM method using the polynomial kernel and the RBF kernel with Bayesian parameter optimization to classify the areas of pillar stability were 91% and 92%, respectively. The multiclass SVM method with the RBF kernel captured 96.6% of potentially dangerous pillars. The visualization of classification areas showed that areas with intact pillars may also have failed pillars.
Classifying Rockburst Events and Intensity in Underground Mines using Grey Wolf Optimization–Support Vector Machine and Extreme Gradient Boosting Nugraha, Adhitya Dwi; Wattimena, Ridho Kresna; Handari, Bevina Desjwiandra; Hertono, Gatot
Journal of Engineering and Technological Sciences Vol. 57 No. 6 (2025): Vol. 57 No. 6 (2025): December
Publisher : Directorate for Research and Community Services, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.eng.technol.sci.2025.57.6.6

Abstract

Rockbursts are destructive accidents that often occur in underground mines. With the advancement of technology, machine learning has emerged as an alternative solution that can be used for rockburst mitigation. In this research, we classify rockburst events and their intensities in underground mines using two machine learning models: grey wolf optimization–support vector machine (GWO–SVM) and extreme gradient boosting (XGBoost). Rockburst events are classified into two categories: Existent and None. Meanwhile, the intensities are classified into three categories: weak, moderate, and strong. The implementation used 476 cases of rockbursts with six variables: maximum tangential stress, uniaxial compressive strength, uniaxial tensile strength, stress coefficient, rock brittleness coefficient, and elastic strain index. Both models can better predict the “Existent” rockburst class with a “Weak” intensity compared with the other intensity classes. The performances of the models are evaluated using different proportions of training data, ranging from 50% to 90%. Both models have the same performance for rockburst event classification with 97.53% accuracy, 0.9444 precision, 0.9846 recall, and 0.9628 F1-score. Meanwhile, for intensity classification, XGBoost outperforms GWO-SVM with its 88.24% accuracy, 0.8413 precision, 0.9137 recall, and 0.8651 F1-score.
Co-Authors Arthana Islamilova Chukwu, Chidozie W. Cindy Cynthia Denny Riama Silaban Devvi Sarwinda Dipo Aldila Farhana, Nadhilah Hati, Wanda Puspita Hengki Tasman Hertono, Gatot Hertono, Gatot Fatwanto Joshita Djajadisastra Joshita Djajadisastra, Joshita Khoshnaw, Sarbaz H. A. Mendrofa, Gabriella Aileen Musyaffa, Muhammad Majid Rafi Nugraha, Adhitya Dwi Putri Zahra Kamalia Ramadhan, Derio A. Ridho Kresna Wattimena, Ridho Kresna Sarbaz H.A. Khosnaw Sarwinda, Devvi Shahzad, Muhammad Tamalia, Evllyn Valentino Vito