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ASEP SAEFUDDIN
Departemen Statistika, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Institut Pertanian Bogorm, Kampus IPB Darmaga, Bogor 16688
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Journal : Scientific Journal of Informatics

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Evaluating Ensemble Learning Techniques for Class Imbalance in Machine Learning: A Comparative Analysis of Balanced Random Forest, SMOTE-RF, SMOTEBoost, and RUSBoost Fulazzaky, Tahira; Saefuddin, Asep; Soleh, Agus Mohamad
Scientific Journal of Informatics Vol. 11 No. 4: November 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i4.15937

Abstract

Purpose: This research aims to identify the optimal ensemble learning method for mitigating class imbalance in datasets utilizing various advanced techniques which include balanced random forest (BRF), SMOTE-random forest (SMOTE-RF), RUSBoost, and SMOTEBoost. The methods were systematically evaluated against conventional algorithms, including random forest and AdaBoost, across heterogeneous datasets with varying class imbalance ratios. Methods: This study utilized 13 secondary datasets from diverse sources, each with binary class outputs. The datasets exhibited varying degrees of class imbalance, offering scenarios to assess the effectiveness of ensemble learning techniques and traditional machine learning approaches in managing class imbalance issues. Study data were split into training (80%) and testing (20%), with stratified sampling applied to maintain consistent class proportions across both sets. Each method underwent hyperparameter optimization with distinct settings with repetition over 10 iterations. The optimal method was evaluated based on balanced accuracy, recall, and computation time. Result: Based on the evaluation, the BRF method exhibited the highest performance in balanced accuracy and recall when compared to SMOTE-RF, RUSBoost, SMOTEBoost, random forest, and AdaBoost. Conversely, the classical random forest method outperformed other techniques in terms of computational efficiency. Novelty: This study presents an innovative analysis of advanced ensemble learning techniques, including BRF, SMOTE-random forest, SMOTEBoost, and RUSBoost, which demonstrate significant effectiveness in addressing class imbalance across various datasets. By systematically optimizing hyperparameters and applying stratified sampling, this research produces findings that redefine the benchmarks of balanced accuracy, recall and computational efficiency in machine learning.
Comparison of Ensemble Forest-Based Methods Performance for Imbalanced Data Classification Hasnataeni, Yunia; Saefuddin, Asep; Soleh, Agus Mohamad
Scientific Journal of Informatics Vol. 12 No. 2: May 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i2.24269

Abstract

Purpose: Classification of imbalanced data presents a major challenge in meteorological studies, particularly in rainfall classification where extreme events occur infrequently. This research addresses the issue by evaluating ensemble learning models in handling imbalanced rainfall data in Bogor Regency, aiming to improve classification performance and model reliability for hydrometeorological risk mitigation. Methods: Four ensemble methods: RF, RoF, DRF, and RoDRF were applied to rainfall classification using three resampling techniques: SMOTE, RUS, and SMOTE-RUS-NC. The data underwent preprocessing, stratified splitting, resampling, and 5-fold cross-validation. Performance was evaluated over 100 iterations using accuracy, precision, recall, and F1-score. Result: The combination of DRF with SMOTE-RUS-NC yielded the most balanced results between accuracy (0.989) and computation time (107.28 seconds), while RoDRF with SMOTE achieved the highest overall performance with an accuracy of 0.991 but required a longer computation time (149.30 seconds). Feature importance analysis identified average humidity, maximum temperature, and minimum temperature as the most influential predictors of extreme rainfall. Novelty: This research contributes a comprehensive comparison of ensemble forest-based methods for imbalanced rainfall data, revealing DRF-SMOTE as an optimal trade-off between performance and efficiency. The findings contribute to improved rainfall classification models and offer practical insight for disaster mitigation planning and resource management in tropical regions.
Co-Authors . Marzuki . Sutriyati Achmad ACHMAD . Achmad Ramzy Tadjoedin adwendi, satria june Agus M Soleh Agus Mohamad Soleh Agustifa Zea Tazliqoh Ahmad A. Mattjik Ahmad Ansori Mattjik Aji H. Wigena Aji Hamim Wigena Aldi, Muhammad Nur Alif Supandi Alinda F. M. Zain Alkahfi, Cahya Ananda Shafira Anang Kurnia Andres Purmalino Ani Suryani Anik Djuraidah Arief Daryanto Arista Marlince Tamonob Arman Arman Arman Arman Arman Arman Arnita Arnita Azagi, Ilham Alifa Bagus Sartono Bambang Indriyanto Basita Ginting Budhi Purwandaya, Budhi Budi Marwoto Budi Susetyo Bunasor Sanim Cece Sumantri Chalid Talib Citra Jaya Daowen Zhang Dede Dirgahayu Domiri Dede Dirgahayu Domiri, Dede Dirgahayu Dewi Juliah Ratnaningsih Diah Krisnatuti Dian Handayani Dian Kusumaningrum Dian Kusumaningrum, Doni Suhartono Dudung Darusman Eka Intan Kumala Putri Embay Rohaeti Eminita, Viarti Enny Kristiani Enny Kristiani Erfiani Erfiani Erfiani Eri Purnomohadi Etih Sudarnika Etty Riani Euis Sunarti Eva Z Yusuf Fatah Sulaiman Fitrah Ernawati Frisca Rizki Ananda Fulazzaky, Tahira H. R. Eddie Gurnadi HAJRIAL ASWIDINNOOR Hanny Nurlatifah Harapin Hafid H. Hardiansyah . Hardinsyah Hari Wijayanto Hartoyo, harry Hasnataeni, Yunia Hendra Prasetya Hengki Muradi Heny Suwarsinah Hermanto Siregar Hidayat Syarief Hilman Dwi Anggana Husaini . I Made Sumertajaya I Wayan Mangku Ida Mariati Hutabarat Indahwati Itasia Dina Sulvianti Jajang Jajang Jodi Vanden Eng Joko Affandi Joko Affandi Joko Sutrisno JOKO SUTRISNO Khairil Anwar Notodiputro Kristiani, Enny Kusman Sadik Lia Budimulyati Salman Lia Ratih Kusuma Dewi Lilik Noor Yuliati Lismayani Usman Lukmanul Hakim Lukmanul Hakim M. Yunus M. Yunus Maghfiroh, Firda Aulia Mangara Tambunan Margono Slamet Marimin , Marimin Marimin Marizsa Herlina Marliati . Marliati Marliati Mirnawati Sudarwanto Muggy David Cristian Ginzel Muhammad Nur Aidi Muradi, Hengki Musa Hubeis mutiah, siti Ni Nyoman Sawitri Nimmi Zulbainarni Ningsih, Wiwik Andriyani Lestari Ninuk Purnaningsih Nirawita Untari Nunung Nuryartono Nuramaliyah, Nuramaliyah Nurlatifah, Hanny Nurul Hidayati Nusar Hajarisman Pang S. Asngari Pien Budiyanto Prabowo Tjitropranoto Pradina, Fathia Anggriani Priyadi Kardono Purnomohadi, Eri R. Ruswandi Rahmadi Sunoko Rahmadi Sunoko Ratna Megawangi Rimun Wibowo Ristu Haiban Hirzi, Ristu Rita Kusriastuti Rizal Syarief Rizal Syarief Rizka Rahmaida Ronny Rachman Noor Rudy Priyanto S. Damanhur, Didin Santun R.P. Sitorus SANTUN R.P. SITORUS Sarah Putri Sarsidi Sastrosumarjo Sausan Nisrina Setiadi Djohar Setiawan Setiawan Siti Sundari Sitti Nurhaliza Sjafri Mangkuprawira Sjafri Mangkuprawira Soedijanto Padmowihardjo Soekirman Soekirman Soetrisno Hadi Sony Sunaryo Sri Yusnita Burhan Suhartono . Suhartono . Sumardjo Sumarjo Gatot Irianto Sumartono Sumartono Sutarman Sutarman . Suwarsinah, Heny Syafri Mangkuprawira Syafri Mangkuprawira Syarifah Iis Aisyah TADJOEDIN, ACHMAD RAMZY Tagor Alamsyah Harahap Talib, Chalid Tati Rajati Tati Suprapti Tiyas Yulita triguna, gunadi Ujang Sumarwan Umi Cahyaningsih Upik Kesumawati Hadi Utami Dyah Syafitri Wahida Ainun Mumtaza William A. Hawley Wiwik Andriyani Lestari Ningsih Yani Nurhadryani Yekti Widyaningsih Yenni Angraini Yudhistira Arie Wijaya Yuni Ros Bangun Yusuf, Eva Z Zinggara hidayat