Sentiment Analysis Of Full Day School Policy Comment Using Naïve Bayes Classifier Algorithm

Authors

  • Miftahul Kahfi Al Fath State Islamic University Syarif Hidayatullah Jakarta , Indonesia
  • Arini Arini State Islamic University Syarif Hidayatullah Jakarta , Indonesia
  • Nashrul Hakiem State Islamic University Syarif Hidayatullah Jakarta , Indonesia

DOI:

10.33395/sinkron.v5i1.10564

Keywords:

Sentiment Analysis; Naïve Bayes Classifier; Lexicon Based Method

Abstract

Sentiment analysis is an important and emerging research topic today. Sentiment analysis is done to see opinion or tendency of opinion to a problem or object by someone, whether it tends to have a negative or positive view. The main purpose of this study is to find out public sentiment on Full Day school's policy comment from Facebook Page of Kemendikbud RI and to find out the performance of the Naïve Bayes Classifier Algorithm. In this study, the authors used the Naïve Bayes Classifier algorithm with trigram and quad ram character feature selection with two different training data models and labeling of training data using Lexicon Based method in the classification of public sentiment toward the Full day school policy. The result of this research shows that public negative sentiment toward Full Day School policy is more than positive or neutral sentiment. The highest accuracy value is the Naïve Bayes Classifier algorithm with trigram feature selection of 300 data training models with a value of 80%. The greater of training data and feature selection used on the Naïve Bayes Classifier Algorithm affected the accurate result.

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How to Cite

Al Fath, M. K., Arini, A., & Hakiem, N. (2020). Sentiment Analysis Of Full Day School Policy Comment Using Naïve Bayes Classifier Algorithm. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 5(1), 107-115. https://doi.org/10.33395/sinkron.v5i1.10564

Issue

Vol. 5 No. 1 (2020): Article Research, October 2020

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