Rainfall Prediction in Blora Regency Using Mamdani's Fuzzy Inference System
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Abstract
In the case study of weather prediction, there are several tests that have been carried out by several figures using the fuzzy method, such as the Tsukamoto fuzzy, Adaptive Neuro Fuzzy Inference System (ANFIS), Time Series, and Sugeno. And each method has its own advantages and disadvantages. For example, the Tsukamoto fuzzy has a weakness, this method does not follow the rules strictly, the composition of the rules where the output is always crisp even though the input is fuzzy, ANFIS has the disadvantage of requiring a large amount of data. which is used as a reference for calculating data patterns and the number of intervals when calculating data patterns and Sugeno has the disadvantage of having less stable accuracy results even though some tests have been able to get fairly accurate results. In research on the implementation of the Mamdani fuzzy inference system method using the climatological dataset of Blora Regency to predict rainfall, it can be concluded as follows: (1) The fuzzy logic of the Mamdani method can be used to predict the level of rainfall in the city of Blora by taking into account the factors that affect the weather, including temperature, wind speed, humidity, duration of irradiation and rainfall. (2) Fuzzy logic for prediction with uncertain input values is able to produce crisp output because fuzzy logic has tolerance for inaccurate data. (3) The results of the accuracy of calculations using the Mamdani fuzzy inference system method to predict rainfall in Blora Regency are 66%.
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References
H. Harys, I. Suprayogi, and Rinaldi, “Aplikasi logika fuzzy untuk prediksi kejadian hujan (studi kasus: Sub Das Siak Hulu),” J. Online Mhs., 2014.
A. Fadholi, “Pemanfaatan suhu udara dan kelembaban udara dalam persamaan regresi untuk simulasi prediksi total hujan bulanan di Pangkalpinang,” Cauchy, vol. 3, no. 1, p. 1, 2013.
Y. N. Ifriza, C. E. Edi, and J. E. Suseno, “Expert system irrigation management of agricultural reservoir system using analytical hierarchy process ( AHP ) and forward chaining method,” pp. 74–83, 2017.
I. Wahyuni, W. F. Mahmudy, and A. Iriany, “Rainfall prediction in Tengger region Indonesia using tsukamoto fuzzy inference system,” Proc. - 2016 1st Int. Conf. Inf. Technol. Inf. Syst. Electr. Eng. ICITISEE 2016, pp. 130–135, 2016.
D. R. Navianti, I. G. N. R. Usadha, and F. A. Widjajati, “Penerapan fuzzy inference system pada prediksi curah hujan di Surabaya Utara,” J. Sains dan Seni ITS, vol. I, no. 1, p. I, 2012.
M. I. Azhar and W. F. Mahmudy, “Prediksi curah hujan menggunakan metode adaptive neuro fuzzy inference system (ANFIS),” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 11, pp. 4932–4939, 2018.
D. A. Susetyo, “Systematic literature review of expert system, fuzzy logic, and artificial neural network applications,” Universitas Islam Indonesia, 2019.
D. Desmonda, T. Tursina, and M. A. Irwansyah, “Prediksi besaran curah hujan menggunakan metode fuzzy time series,” J. Sist. dan Teknol. Inf., vol. 6, no. 4, p. 141, 2018.
M. T. Dewi, U. Zaaidatunni’mah, M. F. A. Hakim, and J. Jumanto, “Implementation of fuzzy tsukamoto in employee performance assessment,” J. Soft Comput. Explor., vol. 2, no. 2, pp. 143–152, 2021.
Z. Julisman and Erlin, “Prediksi tingkat curah hujan di kota pekanbaru menggunakan logika fuzzy mamdani,” J. SATIN - Sains dan Teknol. Inf., vol. 3, no. 1, pp. 65–72, 2014.
B. P. Statistik, “Kabupaten Blora dalam angka,” Blora, 2015.