PENERAPAN FUZZY TIME SERIES-MARKOV CHAIN UNTUK PENJADWALAN TANAM PADI BERDASARKAN PERAMALAN CURAH HUJAN DI MEGALUH - Umi Rahmawati, Evis; Dwi Indriyanti, Aries; Heru Mujianto, Ahmad; Kistofer , Terdy (Inovate, 2025)
Extreme climate change poses a significant challenge for the agricultural sector, especially in determining the ideal rice yield. This research develops a web-based information system to optimize rice planting scheduling using the Fuzzy Time Series - Markov Chain method with human judgment. A case study was conducted in Megaluh, Jombang Regency, where the majority of participants are farmers. The developed system successfully provides rainfall predictions with a Mean Absolute Percentage Error (MAPE) of around 90.74% and recommends planting schedules that can reduce the risk of crop failure. The survey results conducted in January 2023 showed a rainfall of 507.21 mm, which falls into the high rainfall category. The aim of this research is to develop a web-based information system that can optimize rice planting scheduling using the Fuzzy Time Series - Markov Chain method, through the implementation of rainfall data in Megaluh District, Jombang Regency. The research methodology includes problem identification, literature review, data collection, system requirements analysis, system penetration, coding, and testing. Rainfall data from 2018 to 2022 is used to predict rainfall for the next two years, which is then used to recommend the ideal planting time for farmers. This research not only helps practitioners reduce the risk of crop failure due to weather uncertainty, but also introduces new concepts in information technology. Additionally, this system has the potential to be further developed and applied to other case studies for more accurate and optimal results. Keywords: Scheduling, Fuzzy Time series, Forecasting.