Imputasi Data Temperatur Maksimum Menggunakan Metode Support Vector Regression - Sukhna, Isa Kholifatus; Miftahurrohmah, Brina; Wulandari, Catur; Amelia, Putri (JISKA (Jurnal Informatika Sunan Kalijaga), 2025)
Temperature is a crucial element affecting various aspects, from agriculture to natural disasters. Temperature data imputation is also important because, in some cases, temperature data is not always complete. This study aims to predict missing temperature data in the East Nusa Tenggara (NTT) region using the Support Vector Regression (SVR) method. The data used comes from six BMKG observation stations in NTT and ERA-5 Reanalysis data. The choice of the SVR method is based on its ability to handle data with complex structures. Modeling is conducted separately for each station using the Radial Basis Function (RBF) kernel. Model evaluation employs the metrics Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R²), presenting the evaluation results with low error. The results show that among several parameter tests, the parameter ranges [C = 1, 5, 10, 15], [ε = 0,1, 0,3, 0,6, 0,9], and [γ = 1, 5, 10, 15] in the SVR method are the best parameter ranges across all stations. The prediction graphs display different temperature fluctuation patterns at each station. This study contributes to enhancing the availability of accurate climate data, supporting sustainable decision-making in the NTT region.