Jurnal Ilmiah Sains dan Teknologi
Vol 6 No 1 (2022): Jurnal Ilmiah Sains dan Teknologi

IMPLEMENTASI DATA MINING MENGGUNAKAN METODE NAIVE BAYES DENGAN FEATURE SELECTION UNTUK PREDIKSI KELULUSAN MAHASISWA TEPAT WAKTU

Royan Habibie Sukarna (Institut Teknologi Tangerang Selatan)
Yulian Ansori (Universitas Budi Luhur)



Article Info

Publish Date
14 Feb 2022

Abstract

The Education Efficiency Rate (AEE) is one of the parameters of the quality of the education program. The quality is measured based on 7 main standards, one of which is students and graduates. Meanwhile, to predict students' graduation rates accurately based on manually owned data set characteristics is very difficult. Data Mining by Naïve Bayes method was chosen to find patterns in analyzing and predicting timely graduation of students. As for the test will be done by comparing the initial dataset and dataset characteristics using the algorithm attribute selector Gain Ratio Attribute with the help of tools WEKA. The results showed that there was a difference to the accuracy of the results, and the larger ROC or AUC curves on the dataset characteristics using the selector attribute by using the Gain Ratio Attribute, although not very significant. And the result of this research yield 81% accuracy level with precision equal to 83.563% and recall 88.41%. The method used is included in Good Classification and will become the reference of the college management side, to address the problems that may arise in the decrease of the quality of education (e.g. decrease ratio of lecturers with students).

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Journal Info

Abbrev

saintek

Publisher

Subject

Computer Science & IT Decision Sciences, Operations Research & Management Library & Information Science Physics

Description

The aim of this journal is to publish quality articles dedicated to all aspects of the latest outstanding developments in the field of informatics engineering. Its scope encompasses the applications of (but are not limited to) : ICT Software Engineering System Design Methodology Data mining and Big ...