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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Proceedings of KNASTIK TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Informatika Jurnal Sarjana Teknik Informatika Rekursif: Jurnal Informatika Jurnas Nasional Teknologi dan Sistem Informasi Komuniti : Jurnal Komunikasi dan Teknologi Informasi EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Mobile and Forensics Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls (AVITEC) ABDIMASTEK
Dewi Soyusiawaty
Universitas Ahmad Dahlan
Aerospace Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering Neuroscience Social Sciences

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Journal : IJCCS (Indonesian Journal of Computing and Cybernetics Systems)

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SMOTE-SVM for Handling Imbalanced Data in Obesity Classification Biddinika, Muhammad Kunta; Yuliansyah, Herman; Soyusiawaty, Dewi; Razak, Farhan Radhiansyah
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 2 (2025): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.103994

Abstract

 Obesity is a significant health issue associated with various chronic diseases, making its early classification critical for effective interventions. This study investigates the performance of Support Vector Machine (SVM) models with Radial Basis Function (RBF) and Linear kernels on imbalanced obesity datasets. To address data imbalance, Synthetic Minority Over-sampling Technique (SMOTE) and Random Undersampling (RUS) were applied. The results reveal that balancing techniques significantly enhance classification performance, with the Linear model achieving the highest accuracy of 96.54% when balanced using SMOTE. However, limitations include reduced recall for minority classes and potential overfitting risks. These findings underscore the importance of balancing techniques in health data classification and offer insights for further optimizing model performance. The study highlights the need for advanced data balancing strategies to improve predictive accuracy and equity across all classes.
Co-Authors Abdul Fadlil Aldini Fujiarti Aning Setiya Rini Ardian M.N. Buhari Muslim Catur Prasongko Devi Ayunani Wandasari Endri Rahmawanto Enggar Tyasto Wingwirawan Esty Istiqamah Failasuf Abid Dainuri Fathurrahman Fathurrahman Freddy Alpino Furqon, Ihtiar Nur Hapsari, Intan Herman Yuliansyah, Herman Irfan Nur Aditya Jones, Anna Hendri Soleliza Kholid Rohman Saputro Linda Mariana Lisna Zahrotun Muhammad Kunta Biddinika Nizar Anggit Pramana Nuri Piyanto Perdana, Tirtanusa Kurnia Adhi Ramadhan, Imam Razak, Farhan Radhiansyah Rosary Indah Dewi Sunardi Sunardi