Comparative Analysis of Naïve Bayes and Decision Tree Algorithms in Data Mining Classification to Predict Weckerle Machine Productivity

  • Fried Sinlae Universitas Budi Luhur
  • Anugrah Sandy Yudhasti Universitas Budi Luhur
  • Arief Wibowo Universitas Budi Luhur
DOI: https://doi.org/10.29207/joseit.v1i2.3439
Keywords: Naive Bayes, Decision Tree, Algoritma, Data Mining

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.

Downloads

Download data is not yet available.

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.

Published
2022-09-25
How to Cite
Sinlae, F., Anugrah Sandy Yudhasti, & Arief Wibowo. (2022). Comparative Analysis of Naïve Bayes and Decision Tree Algorithms in Data Mining Classification to Predict Weckerle Machine Productivity . Journal of Systems Engineering and Information Technology (JOSEIT), 1(2), 47-51. https://doi.org/10.29207/joseit.v1i2.3439
Issue
Vol 1 No 2 (2022): September 2022
Section
Articles

Copyright (c) 2022 Journal of Systems Engineering and Information Technology (JOSEIT)

Creative Commons License

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

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).