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All Journal SMARTICS Journal Kurawal - Jurnal Teknologi, Informasi dan Industri Jurnal Pengabdian Masyarakat Khatulistiwa Rainstek : Jurnal Terapan Sains dan Teknologi BIMASAKTI
Amak Yunus
Universitas PGRI Kanjuruhan Malang
Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

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PERBANDINGAN AKURASI ALGORITMA SUPPORT VECTOR MACHINE DAN LOGISTIC REGRESSION DALAM APLIKASI PREDIKSI DIABETES BERBASIS WEB MENGGUNAKAN PYTHON Ahmad Firmansyah; Amak Yunus; Heri Santoso
Jurnal Fakultas Teknologi Informasi Vol 8 No 2 (2026): BIMASAKTI
Publisher : Prodi Teknik Informatika, Fakultas Sains dan Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/bimasakti.v8i2.13174

Abstract

This study compares the accuracy of the Support Vector Machine (SVM) and Logistic Regression (LR) algorithms in web-based diabetes prediction using Python. The goal is to assess the performance of both algorithms in classifying patient data based on medical parameters. Testing was conducted with a dataset and measuring performance using Accuracy, Precision, Recall, and F1-score. The results showed that SVM obtained Accuracy of 77.27%, Precision of 75.68%, Recall of 51.85%, and F1-score of 61.54%, while Logistic Regression obtained Accuracy of 75.32%, Precision of 64.91%, Recall of 67.27%, and F1-score of 66.07%. SVM was superior in Accuracy and Precision, while Logistic Regression was better in Recall and F1-score. The choice of algorithm depends on the purpose of the application: if the priority is to detect more positive cases, then Logistic Regression is more recommended, while if the focus is to reduce positive prediction errors, SVM is more appropriate. The developed web application makes it easier for users to predict diabetes quickly, helping medical personnel or individuals in early detection and decision making for disease prevention and treatment..
Co-Authors Achmad Cholirul Rohman Agung Indra Ahmad Firmansyah Budianto, Alexius Endy Danang Aditya Nugraha Fransiskus Heri Heliani Dessy Heri Santoso Ivan Yusuf Rahadika Mohammad Ahsan Pandhu Robil Maula Romadhon Romadhon Sely Prastikasari Syahminan Syahminan Wahyudi Priyanto Wasum Wennedy Surya Pratama Yulia Windi Astuti