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All Journal Jurnal Informatika Upgris JURNAL MEDIA INFORMATIKA BUDIDARMA JURIKOM (Jurnal Riset Komputer) JSAI (Journal Scientific and Applied Informatics) Jurnal Manajemen Informatika dan Sistem Informasi Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) KLIK: Kajian Ilmiah Informatika dan Komputer Dikmas: Jurnal Pendidikan Masyarakat dan Pengabdian International Journal Software Engineering and Computer Science (IJSECS)
Utiarahman, Siti Andini
Universitas Ichsan Gorontalo
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering

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Journal : KLIK: Kajian Ilmiah Informatika dan Komputer

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Analisis Perbandingan KNN, SVM, Decision Tree dan Regresi Logistik Untuk Klasifikasi Obesitas Multi Kelas Utiarahman, Siti Andini; A. Mulawati M. Pratama
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 6 (2024): Juni 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i6.1871

Abstract

Obesity has become a concerning global health issue, with continuously increasing prevalence. Early identification and accurate classification of obesity are crucial for implementing appropriate prevention and treatment strategies. This study aims to analyze and compare the performance of four popular classification algorithms: K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree, and Logistic Regression, in performing multi-class obesity classification based on Body Mass Index (BMI)  according to World Health Organization (WHO) standards. Using a dataset reflecting population diversity, this research evaluates the ability of each algorithm to classify obesity into several categories, such as normal, overweight, obesity grade 1, obesity grade 2, and obesity grade 3. The study utilizes 2.111 records with 17 attributes. Results indicate that the Decision Tree Algorithm outperforms other algorithms, achieving an accuracy of 99.3%, precision of 0.97-1.00, recall of 0.98-1.00, and f1-score of 0.98-1.00. KNN follows with an accuracy of 99.0%, precision of 0.98-1.00, recall of 0.98-1.00 and f1-score of 0.98-1.00. meanwhile, the Logistic Regression algorithm achieves an accuracy of 98%, precision of 0.95-1.00, recall of 0.95-1.00, and f1-score of 0.95-1.00. SVM demontrates slightly lower performance, although still showing overall good results with an accuracy of 96.6%, precision of 0.90-0.99, recall of 0.94-1.00, and f1-score of 0.93-0.99..
Co-Authors A. Mulawati M. Pratama Adam, Novita Alang Alang Alang Alang Alang, Alang Ali, Satriadi D Andi Bode Andi Mulawati Mas Pratama Citra Yustitya Gobel Drajana, Ivo Colanus Rally Farid Hastuti Dalai Jorry Karim Labolo, Abdul Yunus Marlin Lasena Maryati Kadir Thalib Misrawati Aprilyana Puspa Moh. Fachrinanda Putra Bowta Mohamad Efendi Lasulika Mokodompit, Rian Anugrawan Muh. Fitra Yusuf Nur Oktavin Idris Nurjana Suleman Olha Musa Pratama, Andi Mulawati Mas Rapika Anwar Satriadi D Ali Satriadi D. Ali Sri Oktavia Dai Suleman, Fitriyanti Suleman, Nurjana Sulistiawati Rahayu Ahmad Utiarahman, Isra Mirna Yundari, Yundari Yusrianto Malago Zufrianto K Dunggio