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All Journal JIKSI (Jurnal Ilmu Komputer dan Sistem Informasi)
Jeffrey Alexander
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Computer Science & IT Mathematics Other

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ANALISIS SENTIMEN TERHADAP WACANA KEBIJAKAN SUBSIDI KRL BERBASIS NIK MENGGUNAKAN ALGORITMA NAIVE BAYES Jeffrey Alexander; Tri Sutrisno; Irvan Lewenusa
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 13 No. 1 (2025): Jurnal Ilmu Komputer dan Sistem Informasi
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v13i1.32916

Abstract

Kereta Rel Listrik (KRL) is a public transportation that uses rail-based electric power. Although KRL is the main choice of many people because of its efficiency, it has various challenges to face, especially regarding the discourse on the NIK-based KRL subsidy policy. This can be seen from various comments from KRL users on social media that contain comments on the policy discourse. Based on the Financial Memorandum Book document of the Draft State Budget (APBN) 2025, the general KRL fee subsidy will be changed based on the population's Family Identification Number (NIK). As a result, the KRL fee will change from Rp3,000 for the first 25km to approximately Rp25,000 for the first 25km. Complaints and grievances of people today tend to prefer to share posts (uploads) in the form of stories of their daily life through social media. One of the social media that is often used to argue is X (Twitter). One topic that is often discussed is public transportation, usually shared through social media posts or quotes as a form of service user satisfaction. To improve the quality of KRL services, it is necessary to know the level of satisfaction of KRL public transportation users in Indonesia. Sentiment analysis is used in this research to determine the sentiment of KRL service user comments in Indonesia. The Software Development Life Cycle (SDLC) method used is the Agile method. Public sentiment is collected using X social media, and sentiment analysis stages will be carried out such as crawling, labeling, preprocessing, naive bayes classification, accuracy testing, and visualization using Power BI software. The accuracy test results obtained using confusion matrix with 80% training data division and 20% test data obtained 76% accuracy results.
Co-Authors Irvan Lewenusa TRI SUTRISNO