Weather Determination Prediction Using Expert Fuzzy Logic Mamdani Method

Intan Utnasari(1*), Narti Eka Putria(2),

(1) STIE Nagoya Indonesia, Batam, Kepulauan Riau, Indonesia
(2) STIE Nagoya Indonesia, Batam, Kepulauan Riau, Indonesia
(*) Corresponding Author

Abstract


The current climate and weather patterns are very extreme. This kind of weather condition can harm many people. In recent years, heavy rains have resulted in flooding. So far, computers can be used to help people solve problems. The smarter the system and the higher the level of information handling, the more active the role played by the Weather computer is the condition of the air at a certain time and in a certain area that is relatively narrow and in a short period of time. The weather is formed from a combination of weather elements and the weather period can only be a few hours. For example morning, afternoon, or evening, and the situation can be different for each place and every hour. The purpose of this research is to help predict the weather as information. This research uses the Mamdani method. This Mamdani method uses 4 stages to produce an output value, namely, determining the input value (fuzzification), determining the value of x (implication function application), Combination of Rules (Rules), and finally determining the final value or (Defuzzification). This research produces an output value of 35 which is located in the Panas range.

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References


Ak, V. N. O. V. (2016). A Note To Interpretable Fuzzy Models, 13(7), 53–65.

Astawa, I.G.S., (2012). Penerapan Logika Fuzzy Dan Jaringan Syaraf Tiruan Pada Sistem Penilaian Berbasis Komputer, Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI), Volume 1 Nomor 1, 67.

Banjarbaru, H. D. I. (2011). Penerapan Logika Fuzzy Untuk Memprediksi Cuaca, 13–19.

Djunaidi, M. (2005). Penentuan Jumlah Produksi Dengan. Jurnal Ilmiah Teknik Insudtri, 4(2), 95–104. Retrieved from

Industri, F. T., Elektro, J. T., & Petra, U. K. (n.d.). Aplikasi Kendali Fuzzy Logic untuk Pengaturan Kecepatan Motor Universal.

Kadkhoda, M., & Taheri, S. M. (2016). Mining Fuzzy Temporal Itemsets Within Various, 13(7), 67–89.

Kudrat, S. N., Sibaroni, Y., & Time, F. (n.d.). Simulasi Pengaturan Lampu Lalu Lintas Menggunakan Cellular Automata Dan Fuzzy Inference System Traffic Light Control Simulation Using.

Meimaharani, Rizkysari dan Tri Listyorini, (2014). Analisis Sistem Inference Fuzzy Sugeno Dalam Menentukan Harga Penjualan Tanah Untuk Pembangunan Minimarket, Jurnal SIMETRIS, Vol 5 No 1,90-91.

Naba, Agus. (2009). Belajar Cepat Fuzzy Logic Menggunakan MATLAB. Andi. Yogyakarta.

Puspita, E. S., & Yulianti, L. (2016). Perancangan Sistem Peramalan Cuaca Berbasis Logika Fuzzy, 12(1).




DOI: https://doi.org/10.30645/ijistech.v5i4.172

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