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All Journal Jurnal Teknologi Informasi dan Multimedia
Muhammad Adam Rizky Habibi
Program Studi Sistem Informasi, Universitas Buana Perjuangan Karawang
Computer Science & IT

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Penerapan Metode K-Nearest Neighbor Untuk Prediksi Jumlah Kasus HIV di Provinsi Jawa Barat Muhammad Adam Rizky Habibi; Shofa Shofia Hilabi; Bayu Priyatna; Elfina Novalia
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 2 (2025): May
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i2.721

Abstract

The high number of HIV/AIDS cases in Indonesia, especially in West Java Province, is a serious challenge in the field of public health. Limitations in understanding the pattern of spread and predicting the trend of HIV cases cause countermeasures to be less than optimal. To overcome this, this study was conducted with the aim of predicting the number of HIV cases in West Java using the K-Nearest Neighbor (KNN) algorithm, based on historical data from Open Data Jabar from 2019 to 2023 which includes 1,617 data from various districts / cities. The research stages include data collection, preprocessing, feature selection, normalization, division of training and test data, and model evaluation using regression metrics: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R²). The evaluation results show that the KNN model with an optimal K value of 19 produces an MAE of 142.31, MSE of 40,442.92, RMSE of 201.10, and R² value of 0.2397. Predictions for 2024 show that areas with the highest number of HIV cases are in Bandung City, Bogor Regency, Bekasi City, Bekasi Regency, and Indramayu Regency.
Co-Authors Bayu Priyatna Elfina Novalia Shofa Shofia Hilabi