Perbandingan Algoritma Data Mining Naïve Bayes dan Bayes Network untuk Mengidentifikasi Penyakit Tiroid

Bambang Wijonarko
Journal article Pilar Nusa Mandiri • March 2018

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(Bahasa Indonesia, 6 pages)

Abstract

In data mining, known Classification model that can be used to identify thyroid disease, is Naive Bayes and Bayes Network methods. In this study, a model is made by using both algorithm. the data used are taken from the data of Patients with thyroid by using the tools KNIME. The model then compared to determine the best algorithm in determination of disease identification. To measure the performance of the two algorithms, it used methods of testing of cross validation and split percentage. The measurement results using confusion matrix and ROC curves. By using the confusion matrix, Bayes Network has higher accuracy with 98,491% compared with the Naive Bayes with 91,803%. By Using the ROC curve, Bayes Network also has higher accuracy with the ROC curve - negative (0.9337), ROC - hypertiroid (0.9933) and ROC - hypotiroid (0.9977). while Naive Bayes with ROC curve - negative (0.8760), ROC - hypertiroid (0.9789) and ROC - hypotiroid (0.9018). The method which has very good classification is sequentially bayes network and naïve bayes based on assessment AUC between 0.90-1.00. thus Bayes Network algorithm can provide solutions the problems of identifying thyroid disease.

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Pilar Nusa Mandiri

Pilar Nusa Mandiri is a journal published by the Community Service Research Center (PPPM) STMIK N... see more