The Application of Na ve 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

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 Na ve 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%.


Keywords


Sentiment Analysis; Online Learning; Naive Bayes Classifier (NBC); Feature Selection; XGBoost

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References


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DOI: http://dx.doi.org/10.26623/transformatika.v20i1.5144

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