COMPARISON OF HOLT-WINTER EXPONENTIAL SMOOTHING AND LSTM METHODS ON RAINFALL FORECASTING IN SUBAK ABIAN WANASARI KENJUNG - Wahyu Citrayanti, Ni Made; I Komang Arya Ganda Wiguna; Aniek Suryanti Kusuma; I Gede Iwan Sudipa; Ni Made Mila Rosa Desmayani (Proceeding International Conference on Information Technology, Multimedia, Architecture, Design, and E-Business, 2024)
Rainfall is important in the agricultural sector, as rainfall uncertainty affects planting schedules. Therefore, forecasting is necessary to help farmers to plan agricultural activities more efficiently, and reduce the risk of losses caused by weather fluctuations. Some methods can be used, two of which are Holt-Winters and LSTM. To test methods, a case study of one of the subaks in the Catur Village, namely Subak Abian Wanasari Kenjung, was taken. The data used is monthly rainfall data of Catur Village, obtained from BMKG Region III Denpasar. In this research, the Holt-Winter Multiplicative produces a MAPE of 47.88%, the Holt Winter Additive produces a MAPE of 47.69% and the LSTM produces a MAPE of 79.78%. From the test results, Holt-Winter Additive is the method that produces the lowest MAPE, namely 47.69%, so the Holt-Winter Additive is the most accurate and most suitable method for use in predicting future rainfall. The results of this study can help farmers optimize planting schedules and reduce weather-related risks. Stakeholders such as governments and agricultural organizations can also utilize rainfall predictions for efficient planning of water, fertilizer, and seed distribution, supporting timely decision-making. Thus, rainfall forecasts can enhance the accuracy and timeliness of decision-making processes.