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All Journal Global Health Management Journal Jurnal Riset Informatika INTI Nusa Mandiri Hermeneutika : Jurnal Ilmu Hukum PREPOTIF : Jurnal Kesehatan Masyarakat Tunas Medika Jurnal Kedokteran & Kesehatan Abdiformatika: Jurnal Pengabdian Masyarakat Informatika Ibnu Sina: Jurnal Kedokteran dan Kesehatan - Fakultas Kedokteran Universitas Islam Sumatera Utara
ruhyana, nanang
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Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Education Electrical & Electronics Engineering Health Professions Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health

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Journal : Jurnal Riset Informatika

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SENTIMENT ANALYSIS OF TWITTER DATA ON KIP-KULIAH USING TEXTBLOB AND GRADIENT BOOSTING Desi Masdin; Ruhyana, Nanang
Jurnal Riset Informatika Vol. 7 No. 1 (2024): December 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i1.353

Abstract

The Indonesian government aims to position the country among developed nations by 2045, with a primary focus on improving education quality from elementary to higher education levels. One of the key initiatives is the KIP-Kuliah (Indonesia Smart College Card) program, which supports high-achieving students from underprivileged economic backgrounds in accordance with UU No. 12/2012 on Higher Education. This study applies sentiment analysis using TextBlob and the Gradient Boosting algorithm to build a predictive model that identifies public support for the program through Twitter data. The results reveal a significant dominance of negative sentiment, with the model achieving an accuracy of 97%. These findings underscore the importance of sentiment analysis as a feedback tool for policymakers during the implementation of education-related programs. Furthermore, the results suggest that continuous monitoring of public opinion via social media can contribute to more adaptive and responsive policy development. This research highlights the need for future studies to expand the scope of analysis using more advanced natural language processing techniques for deeper understanding and broader coverage of public sentiment.
TWITTER SENTIMENT ANALYSIS ON THE 2024 PRESIDENTIAL DISPUTE DECISION USING NAÏVE BAYES AND SVM Aulia Rahman, Ihsan; Ruhyana, Nanang
Jurnal Riset Informatika Vol. 7 No. 1 (2024): December 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i1.355

Abstract

Public sentiment regarding the 2024 presidential election dispute decision was analyzed through the Twitter platform. The method employed was Naïve Bayes, implemented using RapidMiner software. The dataset consisted of thousands of tweets collected during the presidential election dispute period. Each tweet was classified into three sentiment categories: positive, negative, and neutral. The text mining process involved data cleaning, tokenization, and the application of natural language processing (NLP) techniques for feature extraction. The results of the analysis revealed the distribution of sentiments among Twitter users and changes in sentiment trends over specific periods. This research is expected to provide insights into public perceptions and sentiment patterns related to the presidential election dispute decision
ANALYSIS OF PUBLIC SENTIMENT TOWARDS 2024 PRESIDENTIAL CANDIDACY USING NAÏVE BAYES ALGORITHM Rianggi; Ruhyana, Nanang
Jurnal Riset Informatika Vol. 7 No. 1 (2024): December 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1869.319 KB) | DOI: 10.34288/jri.v7i1.356

Abstract

This study analyzes public sentiment towards presidential nominations using text mining techniques and machine learning. The dataset consists of 670 tweets collected from social media. The analysis process includes a data pre-processing phase, encompassing text cleaning, case folding, tokenization, stopword removal, and stemming using the Sastrawi library for the Indonesian language. Sentiment labeling was was performed using NLTK's SentimentIntensityAnalyzer, categorizing tweets into positive, negative, or neutral sentiments. The analysis results reveal the sentiment distribution among the analyzed tweets. Data modeling was performed using the Naive Bayes algorithm, which achieved an accuracy of 97.78% on the Iris dataset as an implementation example. The confusion matrix and classification report demonstrate the model's excellent performance in distinguishing sentiment classes. This research provides insights into public opinion regarding presidential nominations and demonstrates the effectiveness of text mining techniques and machine learning in sentiment analysis. The method can be applied to understand public opinion trends in other political and social contexts
Co-Authors Ahmad, Ali Mafaluthi ANI OKTARINI SARI, ANI OKTARINI Aulia Rahman, Ihsan Bakri, Edy Riyanto Bayhaqy, Achmad Bela, Tatan Tandu Budiman, Pahmi Desi Masdin Endang Sutrisno Fahdia, Muhammad Rizki Hartini, MC Inge Mustopo, Mustopo Neni Nuraeni, Neni Nisa, Qoirun Nisaa, Defa Rahmatun Pandjaitan, Putri Diviacita Pratama, Annisa Shintya Purnamasari, Febryanti Rianggi Riyadi , Andri Agung Riyadi, Andri Agung Saputra, Irwansyah Setiaji Setiaji, Setiaji Siti Maria Ulfah, Siti Maria Sutrisno, Wildan Arismunandar Tati Mardiana, Tati Thysa Thysmelia Affandi Zaidah, Niklah Zulhaidah, Febiana