Jurnal Teknologi Informasi dan Multimedia
Vol. 7 No. 2 (2025): May

Penerapan Metode K-Nearest Neighbor Untuk Prediksi Jumlah Kasus HIV di Provinsi Jawa Barat

Muhammad Adam Rizky Habibi (Program Studi Sistem Informasi, Universitas Buana Perjuangan Karawang)
Shofa Shofia Hilabi (Program Studi Sistem Informasi, Universitas Buana Perjuangan Karawang)
Bayu Priyatna (Program Studi Sistem Informasi, Universitas Buana Perjuangan Karawang)
Elfina Novalia (Program Studi Sistem Informasi, Universitas Buana Perjuangan Karawang)



Article Info

Publish Date
04 May 2025

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.

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Journal Info

Abbrev

jtim

Publisher

Subject

Computer Science & IT

Description

Cakupan dan ruang lingkup JTIM terdiri dari Databases System, Data Mining/Web Mining, Datawarehouse, Artificial Integelence, Business Integelence, Cloud & Grid Computing, Decision Support System, Human Computer & Interaction, Mobile Computing & Application, E-System, Machine Learning, Deep Learning, ...