Fuzzy Mamdani-Based Vegetable Crop Recommendation System with Historical Climate Pattern Analysis in Deli Serdang Regency
DOI:
https://doi.org/10.30871/jaic.v10i3.11923Keywords:
Decision Support System, Fuzzy Mamdani, Climate Pattern Analysis, Vegetable Crop Recommendation, Historical DataAbstract
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.
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[1] R. Z. Ramdhan, D. F. Zella, and E. Zulkarnain, "Analisis Dampak Perubahan Iklim Terhadap Produksi Tanaman Pangan di Indonesia," Jurnal Ilmu Pertanian Indonesia, vol. 27, no. 1, pp. 1-10, 2022.
[2] A. R. Lubis, M. Husna, and L. P. Purba, "Implementasi Sistem Monitoring Iklim Mikro pada Lahan Pertanian Berbasis Internet of Things (IoT)," Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 9, no. 3, pp. 589-596, 2022.
[3] M. A. Syakur, M. B. Mustakim, and Y. D. Wibowo, "Penerapan Sistem Pendukung Keputusan Penentuan Komoditas Pertanian Unggulan Menggunakan Metode SAW," Jurnal Informatika, vol. 18, no. 1, pp. 56-65, 2024.
[4] S. N. Hasan, Y. A. Singarimbun, and N. R. Hasim, "Sistem Pendukung Keputusan Penentuan Jenis Tanaman Pangan Menggunakan Metode Fuzzy Mamdani," Jurnal Komputer dan Informatika, vol. 10, no. 1, pp. 1-8, 2023.
[5] K. B. E. Gede, N. M. K. Yuliantari, and P. A. S. Suarsana, "Analisis Kesesuaian Lahan Tanaman Hortikultura Terhadap Perubahan Iklim di Dataran Tinggi," Jurnal Agronomi dan Hortikultura, vol. 5, no. 2, pp. 10-21, 2021.
[6] K. B. E. Gede, N. M. K. Yuliantari, and P. A. S. Suarsana, "Analisis Kesesuaian Lahan Tanaman Hortikultura Terhadap Perubahan Iklim di Dataran Tinggi," Jurnal Agronomi dan Hortikultura, vol. 5, no. 2, pp. 10-21, 2021.
[7] A. R. Rauf, S. Sumiharni, and F. Fahmi, "Karakteristik Curah Hujan dan Adaptasi Pola Tanam di Lahan Kering Tropis," Jurnal Ilmu Pertanian Indonesia, vol. 20, no. 2, pp. 120-130, 2018.
[8] D. E. Putri, R. Handayani, and S. D. Pratiwi, "Metode Data Cleaning dan Preprocessing pada Data Time Series Iklim," Prosiding Seminar Nasional Ilmu Komputer dan Teknologi Informasi, vol. 5, no. 1, pp. 34-40, 2022.
[9] F. C. P. Simarmata, Y. C. Tarigan, and B. Y. S. Wibawa, "Klasifikasi Jenis Tanah untuk Keputusan Pola Tanam Menggunakan Algoritma K-Nearest Neighbor," Jurnal Ilmu Komputer dan Teknologi Informasi, vol. 7, no. 2, pp. 45-52, 2024.
[10] M. K. Pandey, R. Mishra, and S. K. Pal, "Fuzzy logic-based crop selection for precision agriculture," Journal of Ambient Intelligence and Humanized Computing, vol. 11, pp. 4959-4971, 2020.
[11] R. Prabowo, B. Setiawan, and H. Nugroho, “Analisis efektivitas Kalender Tanam dalam peningkatan produktivitas pertanian,” Jurnal Agroklimat, vol. 14, no. 2, pp. 112–120, 2019.
[12] S. P. Sari, A. Gunawan, and J. M. A. Wibowo, "Perancangan Sistem Informasi Budidaya Tanaman Berbasis Internet of Things (IoT) untuk Monitoring Iklim Mikro," Jurnal Rekayasa Sistem Industri, vol. 13, no. 1, pp. 1-8, 2024.
[13] R. Susanto, B. Wibowo, and T. Hartono, “Implementasi time series analysis untuk prediksi iklim pertanian lokal,” Jurnal Komputasi, vol. 9, no. 4, pp. 290–308, 2024.
[14] P. Solahudin, H. Asdak, and N. Kusuma, “Pengembangan sistem informasi pertanian untuk dukungan pengambilan keputusan berbasis data iklim,” Jurnal Sistem Informasi Pertanian Indonesia, vol. 4, no. 1, pp. 34–48, 2023.
[15] C. Smith, R. Johnson, and M. Williams, “Climate-based agricultural decision support systems: A systematic review,” Computers and Agriculture, vol. 145, pp. 102–118, 2020.
[16] J. Lee, K. Park, and S. Kim, “Fuzzy logic applications in agricultural crop selection systems,” Journal of Agricultural Computing, vol. 8, no. 2, pp. 45–62, 2021.
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Copyright (c) 2026 Meryatul Husna, Mhd Ikhsan P Siregar, Fachry Ferdiansyah Sembiring, Arif Ridho Lubis
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