Perbaikan Algoritma Naive Bayes Classifier Menggunakan Teknik Laplacian Correction

Muhammad Rizki, Muhammad Arhami, Huzeni Huzeni


Naïve Bayes Classifier is one of the classification algorithms in Data Mining with a good processing speed and a fairly high level of accuracy. In the classification process the Naïve Bayes Classifier adopts the Bayesian theorem to map a data against a class by taking into account the probability of the attribute data, but because the Naïve Bayes Classifier makes probability the basis for its calculations, it is certainly very risk if it is wrong. If one class that is contained in the attribute has a value of 0, this will reduce the level of accuracy of the classification process carried out by the Naïve Bayes Classifier algorithm itself, therefore in this study the Laplacian Correction technique is used as an alternative to fix the problems that are owned by the Naïve Bayes Classifier Algorithm. The result of this research is that the Laplace Correction technique has succeeded in improving the performance of the Naïve Bayes Classifier by fixing the 0 value for each attribute. The level of accuracy that is owned by the Naïve Bayes Classifier after experiencing improvements with the Laplacian correction technique is 94.44%.


Data mining, naïve bayes classifier, laplacian correction.

Full Text:



Muktamar, A., Setiawan, A., and Adji, B., 2015. Pembobotan Korelasi Pada Naive Bayes Classifier, Stmik Amikom Yogyakarta, in Seminar Nasional Teknologi Informasi dan Multimedia.

Kantarcıoglu, M., Vaidya, J., and Clifton, C., 2003. Privacy Preserving Naive Bayes Classifier for Horizontally Partitioned Data, in IEEE ICDM workshop on privacy preserving data mining, 3-9.

Xhemali, D., J HINDE, C., and G STONE, R., 2009. Naïve Bayes Vs. Decision Trees Vs. Neural Networks in the Classification of Training Web Pages, D. XHEMALI, CJ HINDE and Roger G. STONE," Naive Bayes vs. Decision Trees vs. Neural Networks in the Classification of Training Web Pages", International Journal of Computer Science Issues, IJCSI, Volume 4, Issue 1, 16-23.

Syahputra, I.K., Bachtiar, F.A., and Wicaksono, S.A., 2018. Implementasi Data Mining Untuk Prediksi Mahasiswa Pengambil Mata Kuliah Dengan Algoritme Naive Bayes, Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer e-ISSN, Vol. 2548, 964X.

Mussa, H.Y., Mitchell, J.B., and Glen, R.C., 2013. Full “Laplacianised” Posterior Naive Bayesian Algorithm, Journal of cheminformatics, Vol. 5, No. 1, 1-6.

Nyambura, M.M. and Simon, K., 2019. Effect of Safety Awareness Campaigns on Employee Performance in Power Transmission Companies in Kenya, International Journal of Business Management and Finance, Vol. 2, No. 1.

Salim, E., Puspa, D.F., and Darmayanti, Y., 2014. Faktor-Faktor Yang Mempengaruhi Penggunaan Fasilitas E-Filling Oleh Wajib Pajak Sebagai Sarana Penyampaian Spt Masa Secara Online Dan Realtime (Studi Empiris Pada Wajib Pajak Badan Di Kpp Madya Jakarta Pusat), Jurnal Fakultas Ekonomi, Vol. 4, No. 1.



  • There are currently no refbacks.

Copyright (c) 2021 Muhammad Rizki, Muhammad Arhami, Huzeni Huzeni

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.



Jurnal Teknologi - Politeknik Negeri Lhokseumawe is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License 

©2021 All rights reserved | E-ISSN: 2550-0961; P-ISSN:1412-1476