Penerapan Data Mining Produksi Padi di Pulau Sumatera Menggunakan Analisis Regresi Linear

Abstract

Indonesia, primarily an agrarian nation, relies heavily on farming as a livelihood, particularly in rice production. Rice is a crucial commodity, especially in Sumatra. Understanding the influential factors such as rainfall, humidity, average temperature, and harvest area is vital for effective rice production. This research applies the CRISP-DM method: Business Understanding, Data Understanding, Data Preparation, and Modeling. Multiple linear regression analysis is employed using Python programming in Google Colab to assess the impact of these factors on rice production. Results indicate that rainfall, humidity, and average temperature insignificantly affect rice production, while harvest area significantly influences it. The regression model is expressed as Y = 12.3X1 + 1637.1X2 – 159677.3X3 + 5.1X4. This model provides valuable insights for farmers to prioritize influential factors in future rice production