Optimasi Naive Bayes Dengan Pemilihan Fitur Dan Pembobotan Gain Ratio

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I Guna Adi Socrates Afrizal Laksita Akbar Mohammad Sonhaji Akbar Agus Zainal Arifin Darlis Herumurti


Naïve Bayes is one of data mining methods that are commonly used in text-based document classification. The advantage of this method is a simple algorithm with low computation complexity. However, there is weaknesses on Naïve Bayes methods where independence of Naïve Bayes features can’t be always implemented that would affect the accuracy of the calculation. Therefore, Naïve Bayes methods need to be optimized by assigning weights using Gain Ratio on its features. However, assigning weights on Naïve Bayes’s features cause problems in calculating the probability of each document which is caused by there are many features in the document that not represent the tested class. Therefore, the weighting Naïve Bayes is still not optimal. This paper proposes optimization of Naïve Bayes method using weighted by Gain Ratio and feature selection method in the case of text classification. Results of this study pointed-out that Naïve Bayes optimization using feature selection and weighting produces accuracy of 94%.


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SOCRATES, I Guna Adi et al. Optimasi Naive Bayes Dengan Pemilihan Fitur Dan Pembobotan Gain Ratio. Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, [S.l.], p. 22-30, mar. 2016. ISSN 2541-5832. Available at: <https://ojs.unud.ac.id/index.php/lontar/article/view/19506>. Date accessed: 24 mar. 2019. doi: https://doi.org/10.24843/LKJITI.2016.v07.i01.p03.
Data Mining; Naïve Bayes; Weighted Naïve Bayes; Gain Ratio; Pemilihan Fitur


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