OPTIMASI METODE NAIVE BAYES DENGAN ALGORITMA GENETIKA UNTUK KLASIFIKASI KETEPATAN WAKTU STUDI MAHASISWA
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Institusion
Universitas Sriwijaya
Author
ELSYA KRISMI AFINDRI (STUDENT ID : 09021281419061)
Yoppy Sazaki (LECTURER ID : 0006067406)
Danny Matthew Saputra (LECTURER ID : 0010058507)
Yoppy Sazaki (LECTURER ID : 0006067406)
Danny Matthew Saputra (LECTURER ID : 0010058507)
Subject
R858-859.7 Computer applications to medicine. Medical informatics
Datestamp
2019-09-30 04:00:46
Abstract :
Naive Bayes is a very effective and widely used classification method, in this method, there are independences of features that assume all features are important and need to be calculated individually to determine the results of the classification, this affects the accuracy of classification. This research will optimize the classification by Naive Bayes using Genetic Algorithm to do feature selection. Feature selection used to obtain important features to be calculated for determine the data classification and increase the accuracy of the classification. The data used are the data from Informatics students of Sriwijaya University from class of 2011 until 2013. The classification that done here is the classification of timeliness of student study. The result of this research shows the average accuracy of classification is 83%, which has an average increase by 8% from the accuracy of classification without optimization, and the maximum accuracy of classification that can be reached by optimization is 84.12% with 27 selected features. The Genetic Algorithm gives a better effect on the results of Naïve Bayes Classification by doing feature selection that increase the accuracy.
Naive Bayes is a very effective and widely used classification method, in this method, there are independences of features that assume all features are important and need to be calculated individually to determine the results of the classification, this affects the accuracy of classification. This research will optimize the classification by Naive Bayes using Genetic Algorithm to do feature selection. Feature selection used to obtain important features to be calculated for determine the data classification and increase the accuracy of the classification. The data used are the data from Informatics students of Sriwijaya University from class of 2011 until 2013. The classification that done here is the classification of timeliness of student study. The result of this research shows the average accuracy of classification is 83%, which has an average increase by 8% from the accuracy of classification without optimization, and the maximum accuracy of classification that can be reached by optimization is 84.12% with 27 selected features. The Genetic Algorithm gives a better effect on the results of Naïve Bayes Classification by doing feature selection that increase the accuracy.
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RAMA_55201_09021281419061_0006067406_0010058507_07_ref.pdf
RAMA_55201_09021281419061_0006067406_0010058507_08_lamp.pdf
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