Data Mining Dengan Pendekatan Multiple Linear Regression Untuk Prediksi Hasil Panen Padi - Alwi Syahbirin; William Ramdhan; Wan Mariatul Kifti (JURIKOM (Jurnal Riset Komputer), 2026)
The rice agricultural sector plays an essential role in Asahan Regency, but current harvest predictions are still carried out conventionally and subjectively. This causes data inaccuracies that impact uncertainty in logistics planning and production policies by the local government. Therefore, this study aims to build a more accurate rice harvest prediction model to assist the Department of Agriculture of Asahan Regency in making strategic decisions. The research methodology used is data mining techniques by implementing the multiple linear regression method, utilizing historical data on land area and rainfall to predict harvest yields. The main results of this study indicate that the web-based prediction model designed is capable of performing valid calculations, producing a harvest projection for 2025 of 54,308.79 tons that aligns with mathematical model calculations. The implication of this research is that the relevant agencies have a reliable decision support tool for planning food security, irrigation systems, and fertilizer provision more efficiently, thereby minimizing errors caused by manual calculations