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All Journal JITSI : Jurnal Informatika Dan Teknologi Sistem Informasi
Muhammad Raka Abdurrahman
STMIK Mulia Darma

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Sistem prediksi cuaca sederhana menggunakan fuzzy logic Muhammad Raka Abdurrahman; Muhammad Raka Abdurrahman; Niel Berkat Harefa; Mia Sefitri Zalukhu; Berliana Yohana Pasaribu
Interaksi : Jurnal Informatika Dan Teknologi Sistem Informasi Vol 1 No 2 (2026): Mei
Publisher : PT. Ndruru Jaya Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67763/jitsi.v1i2.68

Abstract

Climate change and increasing daily weather uncertainty, particularly extreme rainfall events, make fast, simple, and easy-to-understand weather forecast information increasingly important for decision-making at the local level. This condition encourages the need to develop a weather prediction system that is not only accurate, but also computationally lightweight and interpretable. This study aims to design and implement a simple daily weather prediction system using the Mamdani Fuzzy Inference System (FIS) method. This study uses an experimental quantitative approach by utilizing historical weather data for Medan City for the period 2023–2025 which includes variables of average temperature, rainfall, wind speed, and air pressure. Data were collected through documentation techniques and analyzed using fuzzification, IF–THEN rule-based inference, and defuzzification with the Centroid of Area method. The results show that the developed fuzzy system is able to classify daily weather conditions into Sunny, Cloudy, and Rainy categories consistently and easily interpreted. This system provides a practical contribution as a simple and adaptive local-scale weather prediction tool, as well as a scientific contribution in clarifying the application of the minimalist Mamdani FIS for weather prediction. The study's conclusion confirms that the fuzzy logic approach is effective as a simple weather prediction system, and further research is recommended to increase the number of variables, expand the study area, and compare the fuzzy method with other artificial intelligence approaches.
Co-Authors Berliana Yohana Pasaribu Mia Sefitri Zalukhu Muhammad Raka Abdurrahman Niel Berkat Harefa