KONSTRUKSI BAYESIAN NETWORK DENGAN ALGORITMA K2 PADA KASUS PREDIKSI CUACA

Authors

  • Herastia Maharani Departemen Teknik Informatika Institut Teknologi Harapan Bangsa

DOI:

https://doi.org/10.61769/telematika.v7i2.56

Keywords:

Variabel cuaca, Bayesian Network, algoritma K2, prediksi

Abstract

Sistem peramalan cuaca dibangun dengan menganalisis hubungan antara variabel cuaca adalah dengan menggunakan data mining. Bayesian Network merupakan salah satu metode data mining yang dapat menggambarkan hubungan sebab- akibat antara variabel dalam sebuah sistem. Dalam penelitian ini dibangun Bayesian Network dengan algoritma K2 untuk memodelkan hubungan antara variabel-variabel cuaca dan melakukan prediksi berdasarkan model yang dihasilkan. Hasil pengujian menunjukan bahwa Bayesian Network mampu memodelkan hubungan antara variabel cuaca dan menghasilkan prediksi yang cukup akurat.


Weather forecast systems are built by analyzing the dependency between weather variables. One alternative approach to analyze the dependency between weather variables is by using data mining technique. Bayesian Network is a method capable of representing causal dependencies between  variables in a system. This research used Bayesian Network built by using K2 algorithm to model the dependencies between weather variables and making predictions based on the obtained model. The experiment results showed that Bayesian Network managed to represent the dependencies between weather variables and made quite accurate predictions.

References

G. F. Cooper and E. A. Herskovits, “A Bayesian Method for the

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C. Ruiz, “Illustrations of the K2 Algorithm for Learning Bayes Net

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Computer Science, Worcester Polytechnic Institute, 2005.

J. Pearl, “Graphical Models for Probabilistic and Causal Reasoning”.

Computer Science Department, University of California, 1997.

J. Cheng, D. Bell, W. Liu, “Learning Bayesian Networks from Data:

An Efficient Approach Based On Information Theory”, Faculty of

Informatics, University of Ulster, U.K., 1998

R. E. Neapolitan, “Learning Bayesian Networks”, Pearson Prentice

Hall, 2004

http://www.bom.gov.au, diakses tanggal 9 Desember 2009

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Published

2015-05-07

Issue

Vol. 7 No. 2 (2011)

Section

Articles

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