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All Journal International Journal of Electrical and Computer Engineering Seminar Nasional Aplikasi Teknologi Informasi (SNATI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Seminar Nasional Informatika (SEMNASIF) Journal of Information Systems Engineering and Business Intelligence Indonesia Symposium on Computing IJoICT (International Journal on Information and Communication Technology) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Seminar Nasional Teknologi Informasi Komunikasi dan Industri Journal of Computer Science and Informatics Engineering (J-Cosine) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) Madani : Indonesian Journal of Civil Society Jurnal Teknik Informatika (JUTIF) JINAV: Journal of Information and Visualization Journal of Applied Data Sciences KLIK: Kajian Ilmiah Informatika dan Komputer Prosiding Konferensi Nasional PKM-CSR eProceedings of Engineering Telkatika: Jurnal Telekomunikasi Elektro Komputasi & Informatika
Warih Maharani
Telkom University
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Mathematics Nursing Public Health Social Sciences

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Journal : Journal of Applied Data Sciences

 1 
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 Adhie Rachmatulloh Sugiono Adinda Putri Rosyadi Adiwijaya Agung Toto Wibowo Aisyiyah, Syarifatul Ajeung Angsaweni Aji Gunadi, Gagah Al Giffari, Muhammad Zacky Aldy Renaldi Alfian Akbar Gozali Algi Erwangga Putra Alif Rahmat Julianda Andre Agasi Simanungkalit Angelina Prima Kurniati Anisa Herdiani annisa Imadi Puti Arianti Primadhani Tirtopangarsa Arie Ardiyanti Suryani Artanto Ageng Kurniawan Asep Aprianto Aziz Alfauzi Aziz Azka Zainur Azifa Bondan Ari Bowo Daud, Hanita Dicky Wahyu Hariyanto Diska Yunita Dita Martha Pratiwi Elroi Yoshua Ersy Ervina Evizal Abdul Kadir Fadhel, Muhammad Fadhil Hadi Fairuz Ahmad Hirzani Fathin, Felicia Talitha Fika Apriliani Fikri Ilham Guntur Prabawa Kusuma Hafshah Haudli Windjatika Hilda Fahlena Holle, Alfransis Perugia Bennybeng I Kadek Bayu Arys Wisnu Kencana I Nyoman Cahyadi Wiratama Ilham Rizki Hidayat Imelda Atastina Intan Nurma Yunita Intan Ramadhani Joshua Tanuraharja Keri Nurhidayat Kurniawan Adina Kusuma Latifa, Agisni Zahra M.Syahrul Mubarok Marcello Rasel Hidayatullah Moch Arif Bijaksana Mohamad Mubarok Mohamad Syahrul Mubarok Muh. Akib A. Yani Muhammad Fadhil Mubaraq Muhammad Husein Adnan Muhammad, Noryanti Niken Dwi Wahyu Cahya Nugraha, Endri Rizki Nugroho, Bayu Seno Nungki Selviandro Nur Ghaniaviyanto Ramadhan Nyoman Rizkha Emillia Pratama, Rio Ferdinand Putra Prati Hutari Gani Prati Hutari Gani Prisla Novia Anggreyani Pursita Kania Praisar Purwanto, Zadosaadi Brahmantio Putri Ester Sumolang Putri Samapa Hutapea Rachdian Habi Yahya Raihan Nugraha Setiawan Rasyad, Gerald Shabran Ria Aniansari Rianda Khusuma Rifki Wijaya Ryan Armiditya Pratama Salsabila Anza Salasa Sendika Panji Anom Serventine Andhara Evhen Setiawan, Abiyyu Daffa Haidar Suyanto Suyanto Tiara Nabila Tri Ayu Syifa'ur Rohmah Trysha Cintantya Dewi Tsaqif, Muhammad Abiyyu Veronikha Effendy Wijaya, Yaffazka Afazillah Yantrisnandra Akbar Maulino Yanuar Ega Ariska Yanuar Firdaus AW Yusup, Axel Haikal