Implementation of the K-Nearest Neighbor Algorithm for Birth Rate Prediction - Alhafiz, Akhyar; Kurniawan R., Rakhmat (Journal of Applied Informatics and Computing, 2025)
This study aims to predict the monthly birth rate using the K-Nearest Neighbor (KNN) regression algorithm. The dataset consists of historical data from 2010 to 2020, covering six districts and including variables such as total population, number of couples of reproductive age, family planning participation rate, and monthly birth rate as the prediction target. Data preprocessing involved handling missing values and applying Min-Max normalization. To maintain the time-series nature of the data, a chronological split was used, with 576 records from 2010 to 2018 for training and 216 records from 2019 to 2020 for testing. The model was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R²). The best performance was achieved at K = 7, with MAE = 19.94, RMSE = 30.91, and R² = 0.34. Additionally, the KNN model was compared with Linear Regression and Decision Tree, where KNN outperformed both alternatives. The final model was implemented in a web-based application to facilitate demographic data management and automatic birth rate prediction per district. This system is expected to support policy planning in the fields of population control and public health.