Pemanfaatan Data Citra Satelit Untuk Memprediksi Produksi Padi Tahun 2018-2022 dengan Membandingkan Metode Machine Learning dan Ekonometrik - Hidayat, Arief Ramadhan Rifky; Parina, Okta; Kurniawan, Robert (Seminar Nasional Official Statistics, 2023)
This study aims to evaluate and compare the prediction accuracy of rice production in East Java in 2018-2022 using three methods namely Support Vector Regression (SVR), Autoregressive Integrated Moving Average With Exogenous Variable (ARIMAX), and Autoregressive Distributed Lag (ARDL). The dependent variable is rice production with the independent variables Normalized Difference Water Index (NDWI), Soil Adjusted Vegetation Index (SAVI), and Farmer's Exchange Rate (NTP) derived from satellite imagery and the Central Bureau of Statistics. The best model of this research is SVR with Radial Basis Function (RBF) because it has Mean Absolute Percentage Error (MAPE) and Mean Squared Error (MSE) values of 35.42% and 46.93. The parameters cost (C), gamma (γ), epsilon (ε), and number of support vectors used in the SVR model are 1; 0.33; 0.1; and 43. SAVI is the variable that best describes rice production because it has the same distribution pattern and is the only significant variable in the long-term model.