Fuzzy Mamdani-Based Vegetable Crop Recommendation System with Historical Climate Pattern Analysis in Deli Serdang Regency - Meryatul Husna; Mhd Ikhsan P Siregar; Fachry Ferdiansyah Sembiring; Arif Ridho Lubis (Journal of Applied Informatics and Computing, 2026)
Climate variability poses significant challenges to short-cycle vegetable farming, leading to crop failure and economic losses. This study develops a Decision Support System (DSS) to recommend suitable vegetable crops based on historical climate pattern analysis in Deli Serdang Regency. The system utilizes meteorological data from BMKG spanning January 2022 to December 2024, including average temperature, rainfall, and humidity. Historical pattern analysis employs a three-month rolling mean to predict climate conditions for the upcoming planting period. The Fuzzy Mamdani method is implemented as the inference engine to determine crop suitability scores by processing uncertainty in growing requirements. The system was tested across four planting periods (January, April, July, and October) and successfully generated differentiated recommendations with fuzzy scores ranging from 50% to 88%. Results demonstrate that the system effectively adapts recommendations to seasonal climate variations, providing farmers with data-driven decision support to reduce planting risks and improve crop success rates. Future enhancements include real-time climate data integration and expansion of input variables such as soil type and solar radiation intensity.