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Wahyu Abadi Harahap
Universitas Negeri Medan
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Journal : Student Research Journal

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Analisis Perbandingan Algoritma Machine Learning untuk Klasifikasi Tingkat Risiko Ibu Hamil Rafiqi Aidil Fitra; Wahyu Abadi Harahap; Wahyu Kurnia Rahman
Student Research Journal Vol. 1 No. 6 (2023): Desember : Student Research Journal
Publisher : Sekolah Tinggi Ilmu Administrasi (STIA) Yappi Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/srjyappi.v1i6.846

Abstract

This research aims to conduct a comparative analysis of machine learning algorithms for classifying the risk levels of maternal health. With a focus on the significance of identifying and classifying health risks for pregnant women, this study applies supervised learning methods employing Naïve Bayes, Decision Tree, and K-Nearest Neighbors algorithms. Utilizing the "Maternal Health Risk" dataset from UCI Machine Learning, the research is conducted on Google Colaboratory using Python. The results indicate that the Decision Tree algorithm achieves the highest accuracy rate at 90%, surpassing K-Nearest Neighbors (86%) and Naïve Bayes (65%). Consequently, Decision Tree emerges as the preferred choice for predicting maternal health risks, offering the potential for enhanced care and monitoring.
Co-Authors Crismastiana Koloman Rafiqi Aidil Fitra Raihan Maulana Raisya Dwi Zahra Putri Wahyu Kurnia Rahman