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Wahyu Abadi Harahap
Universitas Negeri Medan
Religion Humanities 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.
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 : Pusat Riset dan Inovasi Nasional

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 Rafiqi Aidil Fitra Raihan Maulana Raisya Dwi Zahra Putri Wahyu Kurnia Rahman