Perbandingan Algoritma Naive Bayes dan Decision Tree(C4.5) dalam Klasifikasi Dosen Berprestasi

Andy  Supriyadi

doi:10.29407/gj.v7i1.19797

Authors

Andy Supriyadi Universitas Sebelas Maret

DOI:

https://doi.org/10.29407/gj.v7i1.19797

Keywords:

categorization, decision tree, k-fold cross validation, lecturers, naive bayes

Abstract

Abstract – Enhancing the execution of Tri Dharma for lecturers is one of the factors in obtaining and sustaining the level of universities with good institution achievement. The Rectorate should exercise consideration while making a decision to reward lecturers who do very well. The information was gathered through speaking with members of the rectorate staff to classify lectures at Sebelas Maret University. In this study, accuracy results in the classification based on lecturers' accomplishments will be compared. International and national publications, education level, the length of doctoral studies, becoming an associate professor, and the length of certification as a lecturer are the features considered in the classification. To categorize lecturers according to their accomplishment, the algorithms Naive Bayes and Support Vector Machine were applied. 350 records of training data and 130 records of testing data total 500 records in this study. From 2018 to 2021, the study was carried out at Sebelas Maret University. The accuracy value obtained from 10-fold cross validation the testing using the Naive Bayes method is 94,80%, while the accuracy value obtained from the testing using the Decision Tree is 95,80%.

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