Implementation of Information Gain for Sentiment Analysis of PSE Policy using Naïve Bayes Algorithm

Abstract

The Ministry of Communication and Information Technology of Indonesia (Kominfo) has established the Penyelenggara Sistem Elektronik (PSE) policy as a mandatory registration requirement for both domestic and foreign Electronic Systems (ES). As a result, Kominfo will impose sanctions on all ES by temporarily suspending their access if they fail to register by July 29, 2022, at 23:59 WIB. This policy has sparked both support and opposition among the Indonesian public, and it has become a topic of discussion, including among Twitter users. Therefore, sentiment analysis is employed as a solution to identify public concerns or issues regarding the policy based on negative and positive tweets. The objective of this research is to evaluate the results of feature selection using Information Gain and the Naïve Bayes Classifier algorithm in analyzing Twitter users' sentiment towards the policies of the Information and PSE of the Ministry of Communication and Information Technology. A total of 1153 lines of tweets were collected from the Twitter platform using the keyword "PSE Kominfo," which were then analyzed using the Naïve Bayes Classifier algorithm and Information Gain feature selection with three scenarios: 90:10, 80:20, and 70:30. Based on the evaluation using the confusion matrix, overall, Scenario 1 with a 90:10 ratio and Information Gain feature selection performed the best, achieving an accuracy of 79.7%, recall of 85%, and an F-1 score of 88%. However, the best precision was observed in Scenario 2 with an 80:20 ratio, reaching 92% due to the higher proportion of positive predictions made by the model compared to other scenarios.

Keywords

• pse kominfo
• naïve bayes classifier
• information gain
• confusion matrix