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All Journal Mutiara: Jurnal Penelitian dan Karya Ilmiah Student Research Journal
Wahyu Kurnia Rahman
Universitas Negeri Medan
Religion Humanities Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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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.
Penerapan Metode Naïve Bayes dalam Peramalan Polusi Udara di Kota Jakarta Sandy Andika Maulana; Shabrina Husna Batubara; Wahyu Kurnia Rahman
Mutiara : Jurnal Penelitian dan Karya Ilmiah Vol. 1 No. 6 (2023): Desember: Mutiara : Jurnal Penelitian dan Karya Ilmiah
Publisher : STAI YPIQ BAUBAU, SULAWESI TENGGARA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59059/mutiara.v1i6.702

Abstract

This research aims to analyze and predict the level of air pollution in Jakarta City using ISPU data of DKI Province, adopting the Naïve Bayes method. The test results show that the Naïve Bayes algorithm has excellent performance, with 93% accuracy, 98% precision, 100% recall, and 99% f1-score. The implication is that this model can be effectively used for air pollution forecasting in Jakarta City, assisting authorities in making decisions related to air quality and environmental improvement efforts.
Co-Authors Rafiqi Aidil Fitra Sandy Andika Maulana Shabrina Husna Batubara Wahyu Abadi Harahap