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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) CommIT (Communication & Information Technology) Journal of ICT Research and Applications International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics Journal of Information Systems Engineering and Business Intelligence Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Journal of Information Technology and Computer Science (JOINTECS) JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JURIKOM (Jurnal Riset Komputer) Building of Informatics, Technology and Science Journal of Information Systems and Informatics RADIAL: JuRnal PerADaban SaIns RekAyasan dan TeknoLogi Indonesian Journal of Electrical Engineering and Computer Science Journal of Computer System and Informatics (JoSYC) Madani : Indonesian Journal of Civil Society Teknika Journal of Applied Data Sciences KLIK: Kajian Ilmiah Informatika dan Komputer Journal of Dinda : Data Science, Information Technology, and Data Analytics Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi SisInfo : Jurnal Sistem Informasi dan Informatika Jurnal INFOTEL RADIAL: Jurnal Peradaban Sains, Rekayasa dan Teknologi
Nur Ghaniaviyanto Ramadhan
Institut Teknologi Telkom Purwokerto
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering

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Journal : Journal of Applied Data Sciences

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Enhancing SMOTE Using Euclidean Weighting for Imbalanced Classification Dataset Ramadhan, Nur Ghaniaviyanto; Maharani, Warih; Gozali, Alfian Akbar; Adiwijaya, Adiwijaya
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.798

Abstract

Class imbalance is a significant challenge in machine learning classification tasks because it often causes models to be biased toward the majority class, resulting in poor detection of minority classes. This study proposes a novel enhancement to the Synthetic Minority Over-sampling Technique (SMOTE) by incorporating Euclidean distance-based feature weighting, called Weighted SMOTE. The key idea is to improve the quality of synthetic minority samples by calculating feature importance using a Random Forest model and assigning higher weights to the most relevant features. The objective of this research is to generate more representative synthetic data, reduce model bias, and increase predictive accuracy on highly imbalanced datasets. Experiments were conducted on four benchmark datasets from the KEEL Repository with imbalance ratios ranging from 0.013 to 0.081. The proposed Weighted SMOTE combined with an ensemble voting classifier (Random Forest, AdaBoost, and XGBoost) demonstrated significant improvements compared to standard SMOTE and models without resampling. For example, on the Zoo-3 dataset, the Balanced Accuracy Score (BAS) increased from 75% to 90%, while the F1-score improved from 48% to 94%. On the Cleveland-0_vs_4 dataset, precision improved from 83% to 91% and recall remained high at 99%. Statistical testing using the Wilcoxon signed-rank test confirmed these improvements with p-values 0.05 for key metrics. The findings show that the proposed method effectively balances sensitivity and precision, generates more meaningful synthetic samples, and reduces the risk of overfitting compared to conventional oversampling. The novelty of this work lies in integrating Euclidean-based feature weighting into the SMOTE process and validating its performance on multiple domains with varying feature types and imbalance ratios. These results indicate that the proposed Weighted SMOTE approach contributes a practical solution for improving classification performance and model stability on severely imbalanced data.
Co-Authors Abdurrahman Ibnul Rasidi Adil El-Faruqi Adiwijaya Agung Toto Wibowo Agus Supriatman Aji Gautama Putrada Alfian Akbar Gozali Alon Jala Tirta Segara Ananda Aulia Rizky Arief Rais Bahtiar Arif Amrulloh Azka Khoirunnisa Christoph Quix Diovianto Putra Rakhmadani Diva Angelica Enjeli Cistia Sukmawati Faisal Dharma Adhinata Fauzi, Muhammad Dzulfikar Hashimoto, Takako Hilda Fahlena Imelda Atastina Kridabayu, Adam Nur Kurnianingsih Kurnianingsih, Kurnianingsih Lintang Suryaningrum Maman Abdurohman Merlinda Wibowo Mohammad Fikri Nur Syahbani Nabilah Sharfina Nia Annisa Ferani Tanjung Nicolaus Euclides Wahyu Nugroho Novi Rahmawati Novi Rahmawati Nugraha, Narantyo Maulana Adhi Nur Fatin Liyana Mohd Rosely Purnama Dileon Yamora Nainggolan Purwitasari, Rachma Wukir Rachma Wukir Purwitasari Rahardian, Reva Ramadhan, Teguh Ikhlas Rifqi Akmal Saputra Rifqi Alfinnur Charisma Salsabila, Nisrinia Eka Sayyid Yakan Khomsi Pane Sigit Pramono Sofiyudin Pamungkas Warih Maharani Wibowo, Merlinda Yohani Setiya Rafika Nur