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All Journal Jupiter ELSE (Elementary School Education Journal) : Jurnal Pendidikan dan Pembelajaran Sekolah Dasar Technomedia Journal Jurnal Bahtera - Jurnal Pendidikan Bahasa Sastra dan Budaya Journal of Information Systems and Informatics Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Tika Jurnal Pengabdian Masyarakat (ABDIRA) Jurnal Mandiri IT Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence J-Icon : Jurnal Komputer dan Informatika "JAMASTIKA" Jurnal Mahasiswa Teknik Informatika Jurnal Bakti Humaniora Ngudi Waluyo Empowerment : Jurnal Pengabdian Kepada Masyarakat INOVTEK Polbeng - Seri Informatika
Yoannes Romando Sipayung
Universitas Ngudi Waluyo
Aerospace Engineering Arts Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Nursing Social Sciences Other

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Penerapan Metode Klasifikasi Naive Bayes untuk Analisis Sentimen terhadap Undang-Undang ITE di Media Sosial Twitter Dimas Fajar Fiandaru; Yoannes Romando Sipayung
ELSE (Elementary School Education Journal) : Jurnal Pendidikan dan Pembelajaran Sekolah Dasar Vol 9 No 2 (2025): AUGUST
Publisher : UNIVERSITAS MUHAMMADIYAH SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30651/else.v9i2.24343

Abstract

The Electronic Information and Transactions Law has caused much debate in Indonesia regarding freedom of expression. Through social media, especially Twitter, people often express their opinions about this law. This study uses the naive bayes classification method to analyze comments on Twitter regarding the Electronic Information and Transactions Law (ITE). The results will be compared with five research journals that use similar or different methods for sentiment analysis on social media. The data used in this study are comments, tweets, and posts on Twitter social media. This study found that the naive bayes classification method on google collab provides 94% accuracy in classifying sentiment. This comparison shows that this method is competitive with other methods such as LSTM, K-NEAREST NEIGHBOR ALGORITHM, SVM, LSTM and BiLSTM, NAIVE BAYES ALGORITHM.
Sentiment Analysis of Public Service Using Naïve Bayes Classifier Purnama, Arga Aditia; Sipayung, Yoannes Romando
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1207

Abstract

Public administrative service quality is a crucial factor in citizen satisfaction. This study analyzes sentiment in public service reviews using a text mining approach with the Naïve Bayes Classifier method. The dataset was collected from citizen feedback on online platforms regarding public administrative services. Preprocessing steps included tokenization, case folding, stopword removal, and stemming. The Naïve Bayes algorithm with Laplace smoothing was applied for classification, and performance was evaluated using accuracy, precision, recall, and F1-score. The experiment resulted in an accuracy of 91.2%, precision of 90.3%, recall of 89.7%, and F1-score of 90.0%. The analysis revealed that Service Speed obtained an average score of 3.21, indicating a moderate level of citizen satisfaction in that aspect. These findings suggest that while the Naïve Bayes method is effective for sentiment classification, its greatest value lies in providing actionable insights for public service improvement. Specifically, policymakers can prioritize addressing delays in service speed through simplified procedures, improved staffing, and digital innovation, while maintaining strengths such as officer politeness and effective complaint handling. By leveraging sentiment analysis, public institutions can continuously monitor citizen feedback, identify problem areas, and implement evidence-based strategies to enhance service quality and strengthen public trust.
Co-Authors Abdul Rohman Agung Wibowo Bayu Evendi Budiati Budiati Desi Fitria Wulandika Dimas Fajar Fiandaru Efaforito Gulo Fauziah , Syifa Galih Agil Febri Ign.F. Bayu Andoro.S Isnia Anjar Setyani Keysa Kalina Elbaraka Krisna Cahya Ramadhan Kurnia Catur Saputra Lisa Virdinarti Putra Lutfi Munawaroh Mauliza, Risha Nur Miski Aderisa Prahesti , Swantyka Ilham Purnama, Arga Aditia Sabilatul Hidayah Shofiyatul Latifah Swantyka Ilham Prahesti Syifa Fauziah Tri Arie Bowo Tri Maula Nafi Ummi Nurul Hidayah Wafiq Lana Pradana Zulmi Restika Rini Zulmi Roestika Rini