Prediction of Tides in the Gisik Cemandi Coastal Area Using the Support Vector Regression (SVR) Method - Dewi Sukmawati, Chandra; Novitasari, Dian Candra Rini; Dewi, Ratna Cintya (Journal of Information Technology and Computer Science, 2025)
This study was conducted to predict tidal fluctuations in the coastal area of Gisik Cemandi Village, Sidoarjo, using the Support Vector Regression (SVR) method. The dataset consisted of  time series records of sea level height for the period of March . The prediction process was implemented by testing three SVR kernel types, namely linear, polynomial, and Gaussian Radial Basis Function (RBF), along with variations of the parameters Cost , Gamma , and epsilon . Based on the evaluation using Mean Absolute Percentage Error ( MAPE), the Linear kernel demonstrated the best predictive performance with the lowest MAPE value of  under a  train-test split. The prediction results with the Linear kernel closely matched the actual data, indicating the model’s accuracy and reliability in capturing the linear patterns of tidal data. This model can be utilized as a supporting tool for tidal prediction to aid coastal activities such as navigation and fisheries.