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All Journal Teknika Jupiter Jurnal Ilmiah Matrik Jurnal Teknologi Sistem Informasi dan Aplikasi JOISIE (Journal Of Information Systems And Informatics Engineering) Abdi Dosen : Jurnal Pengabdian Pada Masyarakat Zonasi: Jurnal Sistem Informasi Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Teknologi Informatika dan Komputer Journal of Information Technology Ampera Brilliance: Research of Artificial Intelligence Jurnal Bina Komputer Jurnal Ilmiah Sistem Informasi Jurnal Rekayasa Sistem Informasi dan Teknologi JIMR : Journal Of International Multidisciplinary Research Jurnal Indonesia : Manajemen Informatika dan Komunikasi International Journal of Artificial Intelligence and Science
Yulianingsih, Evi
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Journal : International Journal of Artificial Intelligence and Science

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A Comparative Study of Logistic Regression and Support Vector Machine for COVID-19 Symptom Prediction Ferdiansyah, Ferdiansyah; Putra, Briandy Tri; Yulianingsih, Evi; Fatmasari, Fatmasari; Idham, Muhammad
International Journal of Artificial Intelligence and Science Vol. 1 No. 1 (2024): September
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/IJAIS.v1.i1.8

Abstract

The rapid spread of COVID-19 has created a critical need for accurate and efficient tools to predict symptoms and aid in early diagnosis. This study aims to compare the effectiveness of two machine learning models, Logistic Regression and Support Vector Machine (SVM), in predicting COVID-19 symptoms based on patient data. The dataset used contains key COVID-19 symptoms, which were processed and modeled using both techniques. Logistic Regression was evaluated alongside SVM using three different kernels: Linear, Sigmoid, and Radial Basis Function (RBF). The models' performance was measured using the Confusion Matrix to assess accuracy. Logistic Regression achieved an accuracy of 96.78%, while the SVM with the RBF Kernel slightly outperformed it with an accuracy of 96.85%. The SVM with the Sigmoid Kernel performed the least effectively, with an accuracy of 95.19%. These findings suggest that both models are highly effective for symptom prediction, with the RBF Kernel showing the best overall performance in handling complex, non-linear data patterns.
Co-Authors .Kusnaedi, Kusnaedi Amalia, Rahayu Amanda Putri, Febrina Apriyani Apriyani Arsando, Dani Damayanti, Nita Rosa Dian Purnamasari, Susan Fatmasari Fatmasari Ferdiansyah Ferdiansyah Fernando, Muhammad Albert Firdaus, Muhammad Dimas Ilman Zuhri Yadi Indah Novita Sari Kalifatullah, M.Ajie Khotimah, Indah Husnul Muhammad Hidayat Muhammad Idham Muhammad Nasir Mutia Mawardah Nurjanah, Inne Oktarina, Tri Oktaviani, Nia Prameswara, Dimas Putra, Briandy Tri Rahmadini, Aulia Nurul Rian Hidayat Rohman, Azzam Fadhlur Rohmawati, Ema Tandean, Yoga Yudiastuti, Helda