METODE ALGORITMA C4.5 DAN NAÏVE BAYES UNTUK KLASIFIKASI TINGKAT KESEJAHTERAAN KESEHATAN MASYARAKAT PEKANBARU
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Date
2021-06
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perpustakaan UR
Abstract
The level of public health welfare in a country can determine the quality and
circumstances of the country. The purpose of this study is to classify the level of public
health welfare of Pekanbaru City in 2019. The study used C4.5 and Naïve Bayes
algorithms with k-fold cross validation to predict the accuracy of classification and
performance evaluation measure as evaluation of both models. Performance evaluation
measure results with k-fold cross validation show that models with Naïve Bayes have
better classification results than C4.5 algorithm models. This is because Naïve Bayes'
accuracy, precision, sensitivity and specificity are greater than the C4.5 algorithm by
100%. This is also because Naïve Bayes obtained the result from the probability value
of each attribute being free of each other.
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Keywords
algorithm, Naïve Bayes, k-fold cross validation, performance evaluation measure, health wellbeing level