Sentiment Analysis of Twitter Users Towards Kartu Prakerja Program Using the Naive Bayes Method

Harto Tomi Wijaya; Kustiyono Kustiyono

doi:10.59395/ijadis.v5i2.1342

Authors

Harto Tomi Wijaya Informatics Engineering, Ngudi Waluyo University, Indonesia
Kustiyono Kustiyono Informatics Engineering, Ngudi Waluyo University, Indonesia

DOI:

https://doi.org/10.59395/ijadis.v5i2.1342

Keywords:

Sentiment analysis, Twitter, Kartu Prakerja, Naive Bayes, Classification, deep learning

Abstract

This study conducts a sentiment analysis of Twitter users regarding the Indonesian government’s Kartu Prakerja program, utilizing the Naive Bayes method for classification. Launched in 2020 to enhance employability skills amidst the COVID-19 pandemic, the program has garnered various public responses. A total of 836 tweets containing the keyword "Kartu Prakerja" were collected using the Twitter API and analyzed to determine sentiment distribution. Results indicate a predominance of neutral sentiment (800 tweets), with only 17 positive and 22 negative tweets. The Naive Bayes method achieved an accuracy of 95%, demonstrating its effectiveness in sentiment classification. However, comparisons with other methods, such as Support Vector Machine (SVM) and Recurrent Neural Network (RNN), reveal that these techniques yield higher accuracy rates (98.34% and 96%, respectively). This research highlights the importance of sentiment analysis in understanding public perceptions and informs policymakers about areas needing improvement. The findings underscore the potential of integrating advanced machine learning techniques to enhance sentiment analysis and provide insights into the effectiveness of government programs like Kartu Prakerja.

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