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All Journal Journal of Creative Student Research Student Research Journal Journal of Computer Science Advancements
Wahyu Abadi Harahap
Universitas Negeri Medan
Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Social Sciences Transportation

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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.
Sistem Rekomendasi Pekerjaan di bidang IT Menggunakan Algoritma Content-Based Filtering Crismastiana Koloman; Raihan Maulana; Raisya Dwi Zahra Putri; Wahyu Abadi Harahap
Journal of Creative Student Research Vol. 1 No. 6 (2023): Desember : Journal of Creative Student Research
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jcsrpolitama.v1i6.2992

Abstract

Rapid growth in the Information Technology (IT) industry has created an abundance of career opportunities, but job seekers often face difficulty in finding jobs that match their background and skills. To overcome this challenge, this research presents a “Job Recommendation System” that focuses on the IT industry. The method used in this research is "Content-Based Filtering," which allows the system to recommend jobs based on similarities between the skills possessed by the user and the available job descriptions. The system allows users to enter their skills, and based on these skills, analyzes the description jobs to recommend suitable jobs. Apart from providing job recommendations, this method also helps users to identify skill areas that need improvement. The research results show that the content-based filtering method is a powerful approach for providing relevant and effective job recommendations in the IT industry. This method provides great benefits to job seekers, helping them find job opportunities that suit their background and skills. In addition, this method has the potential to be applied in various applications in various industries.
Co-Authors Crismastiana Koloman Niska, Debi Yandra Rafiqi Aidil Fitra Raihan Maulana Raisya Dwi Zahra Putri Sandy Andika Maulana Shabrina Husna Batubara Wahyu Kurnia Rahman