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All Journal Student Research Journal Journal of Computer Science Advancements
Wahyu Abadi Harahap
Universitas Negeri Medan
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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.
Decision Support System for Determining Tutoring Institutions for SNBT Preparation Using the Weighted Product Method Shabrina Husna Batubara; Sandy Andika Maulana; Wahyu Abadi Harahap; Debi Yandra Niska
Journal of Computer Science Advancements Vol. 2 No. 2 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v2i2.1108

Abstract

There are several stages in choosing a state university, one of which is through the Joint Selection for State University Admission. To prepare for this, many SNBT candidates attend Learning Guidance Institutions. Learning guidance is a service or educational program designed to help students understand the subject matter taught in school. Its main goal is to improve students' academic abilities through various methods. This research aims to develop a Decision Support System (DSS) using the Weighted Product (WP) method to help choose the most appropriate Tutoring Institution. The system is designed to help parents and prospective students make decisions that suit their needs, taking into account factors such as teaching quality, facilities, and costs. With this DSS, it is hoped that the process of selecting Tutoring Institutions can be carried out more efficiently and accurately so that prospective participants can prepare themselves better to face SNBT. Based on the completed case study, the highest preference value is obtained by Adzkia (A4) with a value of 0.231, while the lowest preference value is Ganesha Operation (A1) with a value of 0.168. The results from the DSS built, and the manual calculations, show the same values.
Co-Authors Niska, Debi Yandra Rafiqi Aidil Fitra Sandy Andika Maulana Shabrina Husna Batubara Wahyu Kurnia Rahman