Comparative Analysis of Naïve Bayes and Decision Tree Algorithms in Data Mining Classification to Predict Weckerle Machine Productivity
Abstract
The level of data accuracy in everyday life is necessary because it is reflected in the ever-advancing development of information technology. Analysis of data processing in information that can provide knowledge with the help of data mining systems. Algorithms commonly used for prediction are Naive Bayes and Decision Trees. The purpose of this study is to compare the Nave-Bayes algorithm and the decision tree algorithm in terms of the accuracy of predicting the productivity of the Weckerle machine at PT XYZ. The method used is a literature study from various related sources and understanding of the data in the source related to the subject of the classification method of the Naive Bayes algorithm and the decision tree into the data mining system. The results of this study are a classification using the Nave-Bayes algorithm with a higher level of confidence than the decision tree algorithm.
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References
N. M. Huda, APLIKASI DATA MINING UNTUK MENAMPILKAN INFORMASI TINGKAT KELULUSAN MAHASISWA (Skripsi), Semarang: Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Diponegoro, 2010.
A. Fikri, Penerapan Data Mining Untuk Mengetahui Tingkat Kekuatan Beton Yang Dihasilkan Dengan Metode Estimasi Menggunakan Linear Regression (Skripsi), Semarang: Fakultas Ilmu Komputer Universitas Dian Nuswantoro, 2013.
Y. I. Kurniawan, "PERBANDINGAN ALGORITMA NAIVE BAYES DAN C.45 DALAM KLASIFIKASI DATA MINING," Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), pp. 455-463, 2018.
M. S. S. G. Arpit Bansal, "Improved K-mean Clustering Algorithm for Prediction Analysis using Classification Technique in Data Mining," International Journal of Computer Applications, pp. 35-40, 2017.
P. B. Davies, Database Systems Third Edition, New York: Plgrave Macmillan, 2004.
R. J. Roiger, Data Mining: A Tutorial-Based Primer, CRC Press, 2017.
A. Saleh, "KLASIFIKASI METODE NAIVE BAYES DALAM DATA MINING UNTUK MENENTUKAN KONSENTRASI SISWA ( STUDI KASUS DI MAS PAB 2 MEDAN )," Konferensi Nasional Pengembangan Teknologi Informasi dan Komunikasi, pp. 200-208, 2015.
Bustami, "PENERAPAN ALGORITMA NAIVE BAYES UNTUK MENGKLASIFIKASI DATA NASABAH ASURANSI," JURNAL INFORMATIKA, pp. 884-898, 2018.
A. K. N. I. A. Ayung Candra Padmasari, "Penerapan Model Decision Tree untuk Rancangan Game Multiplayer Berbasis Jaringan (Uka-Uka Tresure Hunter)," Jurnal Edsence Vol. 1 No. 1, pp. 19-24, 2019.
B. Unhelkar, Software Engineering with UML, Boca Raton: Auerbach Publications/CRC Press, 2018.
Z. Syahputra, "Penerapan Permodelan UML Sistem Informasi Perpustakaan pada Universitas Islam Indragiri Berbasis Client Server," Jurnal SISTEMASI, pp. 57-64, 2015.
J. Martin, Rapid Application Development, New York: Macmillan Publishing, 1991.
Nik, M. N., Nor, A. A., & Hazlifah, M. R., "Implementing Rapid Application Development," in Implementing Rapid Application Development (RAD) Methodology in Developing Practical Training Application System, Kuala Lumpur, Malaysia, IEEE, 2010, pp. 1664-1667.
T. Lucidchart, "4 Phases of Rapid Application Development Methodology," 23 May 2018. [Online]. Available: https://www.lucidchart.com/blog/rapid-application-development-methodology. [Accessed 7 11 2020].
D. D. A. R. Ricardo M. Bastos, Extending UML Activity Diagram for Workflow Modeling in Production Systems, Hawaii: IEEE, 2002.
Y. Mardi, "Data Mining : Klasifikasi Menggunakan Algoritma C4.5," Jurnal Edik Informatika, pp. 213-219, 2016.
E. Elisa, "Analisa dan Penerapan Algoritma C4.5 Dalam Data Mining Untuk Mengidentifikasi Faktor-Faktor Penyebab Kecelakaan Kerja Kontruksi PT.Arupadhatu Adisesanti," Jurnal Online Informatika, pp. 36-41, 2017.
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