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All Journal Siber International Journal of Education Technology Greenation International Journal of Engineering Science
Ridwan Ridwan
Information Technology Study Program, Institut Teknologi dan Bisnis Dewantara, Jakarta, Indonesia
Aerospace Engineering Automotive Engineering Civil Engineering, Building, Construction & Architecture Education Electrical & Electronics Engineering Mechanical Engineering Social Sciences Other

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Text Mining for Sentiment Analysis of Social Media Regarding the Revision of the TNI Law Using Python by Utilizing the Lexicon Approach Ridwan, Ridwan; Ali, Hapzi; Lubis, Hendarman
Siber International Journal of Education Technology (SIJET) Vol. 2 No. 2 (2024): Siber International Journal of Education Technology (October 2024)
Publisher : Siber Nusantara Review & Yayasan Sinergi Inovasi Bersama (SIBER)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/sijet.v2i2.156

Abstract

This study aims to analyze public sentiment regarding the proposed revision of the Indonesian National Military (TNI) Law through social media, specifically Twitter. With the growing role of social media as a public sphere, it serves as a critical platform for expressing public opinions on national issues, including legal reforms. Using sentiment analysis, this research classifies Twitter posts into positive, neutral, or negative categories based on content related to the revision of the TNI Law. Data collection was carried out using the Twitter API (Tweepy), gathering 300 tweets using keywords related to the TNI Law revision. The analysis employed two methods: a lexicon-based approach using TextBlob and machine learning classification using Naive Bayes. The results reveal that the majority of the tweets express neutral sentiment (44.3%), followed by negative sentiment (28.3%) and positive sentiment (27.3%) based on TextBlob analysis. The Naive Bayes model demonstrated better sensitivity to negative sentiment, with 38.3% of tweets classified as negative. The findings suggest that public concern centers around the potential militarization of civil space, human rights violations, and threats to democracy. These concerns highlight the importance of transparent communication from the government regarding policy changes. Additionally, the research underscores the potential of natural language processing (NLP) and machine learning in monitoring public opinion in real time, which can serve as a valuable tool for policymakers in shaping responsive, data-driven legislation.
Implementation of Priority Scheduling Algorithm in Designing Infrastructure Damage Reporting System Prio Kustanto; Wisnu Arizky Kurniawan; Achmad Noe’man; Nurfiyah Nurfiyah; Ridwan Ridwan
Greenation International Journal of Engineering Science Vol. 2 No. 3 (2024): (GIJES) Greenation International Journal of Engineering Science (September - No
Publisher : Greenation Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/gijes.v2i3.233

Abstract

The rapid growth of population and urbanization in Bekasi District has increased the demand for adequate road infrastructure, but it has also posed challenges in the maintenance and repair of existing infrastructure. The main constraint in addressing this issue is the limited available budget, although the Bekasi District Government has tried various innovative programs. In this context, it is important to have mechanisms that allow the public to report infrastructure problems quickly and accurately. Based on preliminary studies, many factors can cause road infrastructure damage. The aim of this research is to design and develop a Road Infrastructure Damage Complaint System in Bekasi District, providing easy access for the public to report road infrastructure damage and enabling authorities to prioritize infrastructure repairs more quickly and accurately, thus improving the quality of life for the community and building a complaint system that can help expedite the repair process.
Co-Authors Achmad Noe’man Hapzi Ali Lubis, Hendarman Nurfiyah Prio Kustanto Wisnu Arizky Kurniawan