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All Journal Media Statistika Jurnal Gaussian TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics International Journal of Advances in Intelligent Informatics EKSAKTA : Jurnal Penelitian dan Pembelajaran MIPA Unisda Journal of Mathematics and Computer Science (UJMC) Indonesian Journal of Electrical Engineering and Computer Science Mathematics and Applications (MAp) Journal Journal of Fundamental Mathematics and Applications (JFMA)
Danardono Danardono
Departemen Matematika, FMIPA Universitas Gadjah Mada
Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Mathematics Other

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Journal : Bulletin of Electrical Engineering and Informatics

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Discretization methods for Bayesian networks in the case of the earthquake Devni Prima Sari; Dedi Rosadi; Adhitya Ronnie Effendie; Danardono Danardono
Bulletin of Electrical Engineering and Informatics Vol 10, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i1.2007

Abstract

The Bayesian networks are a graphical probability model that represents interactions between variables. This model has been widely applied in various fields, including in the case of disaster. In applying field data, we often find a mixture of variable types, which is a combination of continuous variables and discrete variables. For data processing using hybrid and continuous Bayesian networks, all continuous variables must be normally distributed. If normal conditions unsatisfied, we offer a solution, is to discretize continuous variables. Next, we can continue the process with the discrete Bayesian networks. The discretization of a variable can be done in various ways, including equal-width, equal-frequency, and K-means. The combination of BN and k-means is a new contribution in this study called the k-means Bayesian networks (KMBN) model. In this study, we compared the three methods of discretization used a confusion matrix. Based on the earthquake damage data, the K-means clustering method produced the highest level of accuracy. This result indicates that K-means is the best method for discretizing the data that we use in this study.
Co-Authors Adhitya Ronnie Effendie, Adhitya Ronnie Ani Apriani Dedi Rosadi Devni Prima Sari Gunardi Gunardi Iwan Tri Riyadi Yanto Muhammad Muhajir, Muhammad Rianti Siswi Utami Septiana Fathurrohmah Siti Alfiatur Rohmaniah Sri Haryatmi Sri Haryatmi Kartiko Syarto Musthofa Triastuti Wuryandari Wirna Arifitriana