Article Per Year (5 Year)
p-Index From 2020 - 2025
3.189
P-Index
This Author published in this journals
All Journal Jurnal Informasi dan Komputer Aisyah Journal of Informatics and Electrical Engineering Infotek : Jurnal Informatika dan Teknologi Istech Journal (International Science And Technology Journal) Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Jurnal Ilmiah Teknik Informatika dan Komunikasi Electrician : Jurnal Rekayasa dan Teknologi Elektro Jurnal Ilmu Komputer, Sistem Informasi, Teknik Informatika (JILKOMSITI) Jurnal Informatika: Jurnal Pengembangan IT Explore Polygon: Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi Saturnus: Jurnal Teknologi dan Sistem Informasi
Nuari Anisa Sivi
Universitas Nahdlatul Ulama Lampung
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Other

Published : 20 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Infotek : Jurnal Informatika dan Teknologi

 1 
Analisis Pengaruh Bayesian Optimization Terhadap Kinerja SVM Dalam Prediksi Penyakit Diabetes Dwi Utami; Fathoni Dwiatmoko; Nuari Anisa Sivi
Infotek: Jurnal Informatika dan Teknologi Vol. 8 No. 1 (2025): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v8i1.28468

Abstract

Diabetes is a prevalent and serious chronic illness that impacts millions of individuals globally. Early detection of diabetes is essential to mitigate severe health complications. This study investigates the application of Support Vector Machine (SVM) enhanced by Bayesian Optimization for the early prediction of diabetes. While SVM is a robust machine learning algorithm, its performance heavily depends on the proper selection of parameters. Bayesian Optimization is an efficient approach to fine-tune SVM parameters, such as the regularization parameter (C) and the kernel parameter (gamma). The research utilizes a Kaggle dataset that includes various diabetes risk factors. The study compares the performance of SVM optimized using Bayesian Optimization against SVM without optimization. The findings reveal that SVM with Bayesian Optimization achieves an accuracy of 95%, surpassing the 94% accuracy of the unoptimized SVM. These results highlight that Bayesian Optimization enhances SVM's effectiveness in predicting diabetes early
Co-Authors Ade Surahman Agus Komarudin Agus Komarudin Cahya Arnitha Lestari DWI UTAMI Fathoni Dwiatmoko Fathoni Dwiatmoko Fathoni Dwiatmoko2 Fathurrahman Kurniawan Ikhsan Fitri Hardiyanti Hafiz, Aliy Hartono, Rudi Imam Mualim Maulana Malik Hidayatullah Muhamad Iswanto Muhammad Taufik Kussofyan Muhammad Yusron Maulana El-Yunusi Najla Asyila Nurhidayah Nurhidayah Oriza Panduwinata Panji Andhika Putra Hanafi Reni Permata Sari Roro Fatikhin Rudi Hartono Rudi Hartono Suci Khotimah Tahta Herdian Andika Triandi Kurniawan Wafiq Abdulloh Wahyudi, Agung Deni Willy Artanika Rikarda