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All Journal Jurnal Teknik Informatika dan Teknologi Informasi
Muhammad Raviansyah
Universitas Bina Sarana Informatika
Computer Science & IT

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Komparasi Algoritma Machine Learning (Random Forest, Gradient Boosting, dan Ada Boosting) untuk Prediksi Tingkat Penyakit Alzheimer Muhammad Raviansyah; Andika Amansyah; Farhan Fadhilah; Sumanto Sumanto; Imam Budiawan; Roida Pakpahan
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6227

Abstract

Alzheimer’s disease is one of the most common forms of progressive dementia and has become a major global health challenge as the aging population continues to increase. Early detection of this disease is crucial to mitigate its social, economic, and health impacts. In this context, data-driven approaches using machine learning algorithms can be utilized to predict Alzheimer’s risk more accurately. This study aims to compare the performance of three ensemble learning algorithms—Gradient Boosting, Random Forest, and AdaBoost—in predicting the risk level of Alzheimer’s disease using the public Alzheimer’s Disease Dataset, which includes demographic, clinical, and lifestyle data. The research process involved several stages, including data preprocessing, splitting data into training and testing sets, model training using cross-validation, and performance evaluation based on accuracy, precision, recall, F1-score, and AUC metrics. The experimental results show that the Gradient Boosting algorithm achieved the best performance with an accuracy of 0.956, an F1-score of 0.956, and an AUC of 0.985, demonstrating its ability to capture complex non-linear relationships among features such as age, MMSE score, and lifestyle factors. Meanwhile, Random Forest and AdaBoost achieved competitive yet slightly lower performance. These findings indicate that ensemble boosting approaches, particularly Gradient Boosting, hold great potential for medical decision-support systems in the early detection of Alzheimer’s disease and can serve as a foundation for developing more accurate and adaptive predictive models in the future.
Co-Authors Andika Amansyah Farhan Fadhilah Imam Budiawan Roida Pakpahan Sumanto, Sumanto