Sugeno-Type Fuzzy Inference Optimization With Firefly and K-Means Clustering Algorithms For Rainfall Forecasting

Authors

  • M.Shochibul Burhan Brawijaya University
  • Wayan Firdaus Mahmudy Brawijaya University
  • Rizdania Dermawi Brawijaya University

DOI:

https://doi.org/10.25126/jitecs.20183134

Abstract

Rainfall is very influential in our daily lives, ranging from agriculture, aviation, to flood-prone areas. The intensity of rainfall is used as an early detection for preventing harmful effects of rainfall. This research used Sugeno-Method Fuzzy Logic, in which the prediction is accomplished by mapping rules from the data obtained using the K-Means Clustering Algorithm as the classification to form the membership function and mapping rules and Firefly Alghorithm for optimization output. The test result from the 30 examined data found is 2.93 RMSE. This shows that the data support from K-Means Clustering and Firefly Algorithms of the fuzzy logic can predict precipitation accurately.

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Published

2018-06-09

How to Cite

Burhan, M., Mahmudy, W. F., & Dermawi, R. (2018). Sugeno-Type Fuzzy Inference Optimization With Firefly and K-Means Clustering Algorithms For Rainfall Forecasting. Journal of Information Technology and Computer Science, 3(1), 35–48. https://doi.org/10.25126/jitecs.20183134

Issue

Vol. 3 No. 1: June 2018

Section

Articles

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