IMPLEMENTASI ALGORITMA NAIVE BAYES DAN RANDOM FOREST DALAM MEMPREDIKSI PRESTASI AKADEMIK MAHASISWA UNIVERSITAS ISLAM NEGERI AR-RANIRY BANDA ACEH

Authors

  • Bustami Yusuf Department of Information Technology, Faculty Science and Technology, Islamic State University of Ar-Raniry
  • Muthmainna Qalbi Department of Information Technology Education, Faculty Teacher Training and Education, Islamic State University of Ar-Raniry
  • Basrul Basrul Department of Information Technology Education, Faculty Teacher Training and Education, Islamic State University of Ar-Raniry
  • Ima Dwitawati Department of Information Technology, Faculty Science and Technology, Islamic State University of Ar-Raniry
  • Malahayati Malahayati Department of Information Technology, Faculty Science and Technology, Islamic State University of Ar-Raniry
  • Mega Ellyadi Department of Information Technology, Faculty Science and Technology, Islamic State University of Ar-Raniry

DOI:

https://doi.org/10.22373/cj.v4i1.7247

Keywords:

Naive Bayes, Academic Achievement, Random Forest, Prediction, Motivation

Abstract

Academic achievement is determined by two factors, namely internal factors originating from within the individual in this case students and external factors that come from outside the individual or things that are influenced by the environment. There are many ways to find an academic achievement, one of which uses data mining which aims to predict or classify data using a classification algorithm. This study aims to 1) find out how to apply the Naive Bayes algorithm to student achievement, and 2) see the accuracy of the Naive Bayes algorithm to student achievement. This type of research is secondary data in the form of student data obtained from the information technology center and the Ar-Raniry UIN database. This research uses Naive Bayes algorithm and random forest algorithm. The results obtained from this study indicate the highest correlation value in the initial IP variable of r = 0.783 and the leave variable has a very weak correlation level of r = 0.054. The accuracy value of Naive Bayes algorithm after cleaning is 78.0% and Random Forest algorithm variable is 76.7%.

Author Biography

  • Bustami Yusuf, Department of Information Technology, Faculty Science and Technology, Islamic State University of Ar-Raniry
    Prodi PTI FTK UIN Ar-Raniry Banda Aceh Gedung B, Lt. 2

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Published

2020-07-01

Issue

Vol. 4 No. 1 (2020)

Section

Articles

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