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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) SMATIKA KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Sistemasi: Jurnal Sistem Informasi Jurnal Eksplora Informatika SPIRIT VCD (Journal of Visual Communication Design) Jurnal Pengabdian Masyarakat Asia International Journal of Robotics and Control Systems International Journal of Community Service Joutica : Journal of Informatic Unisla PELS (Procedia of Engineering and Life Science) Jurnal Pengabdian Masyarakat - Teknologi Digital Indonesia JUSIFOR : Jurnal Sistem Informasi dan Informatika Jurnal Inovasi Teknologi dan Edukasi Teknik Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Jurnal Sistem Komputer Asia Jeskovsia (Jurnal Desain Komunikasi Visual Asia) Jurnal ilmiah teknologi informasi Asia
Rina Dewi Indahsari
Institut Teknologi Dan Bisnis Asia Malang
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Komparasi Kinerja Algoritma XGBoost dengan Reduksi Dimensi PCA pada Klasifikasi Diabetes Nusa, Rivale Belano; Indahsari, Rina Dewi
JUSIFOR : Jurnal Sistem Informasi dan Informatika Vol 4 No 2 (2025): JUSIFOR - Desember 2025
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/jusifor.v4i2.8563

Abstract

Diabetes is one of the most prevalent chronic diseases worldwide and requires accurate early detection to prevent long-term complications. In the field of medical data analysis, the application of machine learning algorithms such as XGBoost has proven effective in classifying disease risk. This study aims to compare the performance of the XGBoost algorithm before and after applying Principal Component Analysis (PCA) in diabetes risk classification using the Early Stage Diabetes Risk Prediction Dataset. The research stages include data preprocessing involving missing value checking, label encoding, outlier removal, normalization, and followed by the application of PCA with a 90% variance retention threshold. The experimental results show that the XGBoost model without PCA achieved the highest accuracy of 99.04%, while the model with PCA achieved 98.08%. Although the application of PCA slightly reduced accuracy, this technique successfully decreased the number of features and improved computational efficiency without losing important information. Therefore, PCA is proven to be effective in simplifying data complexity while maintaining optimal model performance.
Co-Authors ., Setyorini A.A. Ketut Agung Cahyawan W Abdulloh Eizzy Irsyada Adit Sugiarto Aditya Tri Istanto Aqromi , Nur Lailatul Aryo Prakosa Broto Poernomo T.P Danar Kurniawan, Karolus Daniel, Kevin Oktavianus Dina Komar Lia Dyah Lestari Effendi, Mas Iwan Ali Endang Setyati Fadilla Cahyaningtyas Farah Rifdah Arzumni Farokhah, Lia Fitria, Vivi Aida Hari Kurniawan, Hironimus Imam Zuhdi irsyada, abdulloh eizzi Jaenal Arifin Jaenal Arifin Justita Dura Kala'lembang, Adriani Kurniawan, Hironimus Hari Kurniawan, Karolus Danar Lailatu Aqromi , Nur Lalu Ryan Ravisafitra Louvareto Costa Câmara Marito De Oliveira Amaral Barreto Muhammad Saikhul Masykur Nasirin, Rahmad Khoirun Nur Munawaroh Nurul Afiyah, Siti Nusa, Rivale Belano Poernomo T.P, Broto Rizqi, Afif Naufal Samsul Arifin Setyorini . Setyorini Setyorini Siti Nurul Afiyah Siti Sendari Suastika Yulia Riska Supriadi, Joko Titania Dwi Andini Tivvany Aijen, Loudry Wahyu Sakti Gunawan Irianto Yuliana Melita Pranoto Yuwono Sindunata Yuwono Sindunata, Yuwono Zaeni, Ilham Ari Elbaith