Hermansyah, Mas'ud (2023) Twitter Sentiment Analysis for Exploring Public Opinion on the Merdeka Belajar-Kampus Merdeka (MBKM) 2023 with the Naïve Bayes Classifier Algorithm. Proceeding International Conference on Economics, Business and Information Technology (ICEBIT), 4. pp. 852-860. ISSN 3025-4892

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Abstract

The Merdeka Belajar-Kampus Merdeka (MBKM) as one of the public policies by the Ministry of Education, Culture, Research and Technology, cannot be separated from public opinion. Opinion directions are divided into three categories, positive opinion, negative opinion, and neutral opinion. Utilizing tweet data from Twitter with the keyword Merdeka Belajar-Kampus Merdeka (MBKM) in May 2023, a sentiment analysis was carried out to identify public opinion on the Merdeka Belajar-Kampus Merdeka (MBKM) program. Through this research, it is hoped that opinions, opinion factors, and problems that may arise from the implementation of the Merdeka Belajar-Kampus Merdeka (MBKM) policy can be identified as early as possible. The Naïve Bayes Classifier is used to classify the direction of a person's sentiment towards the Merdeka Belajar-Kampus Merdeka (MBKM), both positive sentiment, neutral sentiment, and negative sentiment. Dataset collection and preparation begins with feature selection, eliminating duplication and tweet selection, then pre-processing is carried out, namely case folding, tokenizing, character cleaning, normalization to stemming for use in labeling. Of the 1,212 data used, the model managed to classify 453 sentiments as neutral, 477 negative sentiments, and 282 positive sentiments. With these results, information is obtained that the community still has many pros and cons regarding the implementation of the Merdeka Belajar-Kampus Merdeka (MBKM) program. In this study, from the results of Twitter sentiment analysis on the Merdeka Belajar-Kampus Merdeka (MBKM) program, the Naïve Bayes Classifier algorithm produces an accuracy value of 74.25%.

Item Type:	Article
Subjects:	T Technology > T Technology (General)
Divisions:	STI-S1
Depositing User:	Mas'ud Hermansyah
Date Deposited:	18 Jan 2024 03:38
Last Modified:	18 Jan 2024 03:38
URI:	http://repo.itsm.ac.id/id/eprint/1153

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