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All Journal Jurnal Transformatika
Ryanda Satria Putra
STMIK Amik Riau
Computer Science & IT

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The Application of Naïve Bayes Classifier Based Feature Selection on Analysis of Online Learning Sentiment in Online Media Ryanda Satria Putra; Wirta Agustin; M. Khairul Anam; Lusiana Lusiana; Saleh Yaakub
Jurnal Transformatika Vol 20, No 1 (2022): July 2022
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v20i1.5144

Abstract

There are problems that still exist in online learning including limited-reach networks, inadequate facilities and infrastructure, and others. This study discussed the analysis of sentiment which used the Naïve Bayes Classifier (NBC) method with XGBoost feature selection as a performance improvement that took data from news portals. The results of this study showed that graph data on the application of online learning forms in Indonesia had a "Negative" opinion. Performance testing of the NBC method based on XGBoost feature selection was conducted four times. The first experiment resulted in an accuracy value of 60.18% with 50/50 split data. The next experiment had an accuracy value of 56.92% with 70/30 split data. After that, the third experiment resulted in an accuracy value of 65.90% with 80/20 split data. The result of the last experiment was an accuracy value of 63.63% with 90/10 split data. After using XGBoost feature selection, it produced an accuracy of 60.18%, 67.69%, 70.45%, and 77.27%. The study also produced the highest average score at 10-Fold Cross-Validation in the second trial with a score of 65.62%.
Co-Authors Anam, M Khairul Efrizoni, Lusiana Wirta Agustin Yaakub, Saleh