Article Per Year (5 Year)
p-Index From 2021 - 2026
2.908
P-Index
This Author published in this journals
All Journal Telematika : Jurnal Informatika dan Teknologi Informasi JUITA : Jurnal Informatika JOIV : International Journal on Informatics Visualization IJCIT (Indonesian Journal on Computer and Information Technology) Martabe : Jurnal Pengabdian Kepada Masyarakat JURNAL TEKNOLOGI DAN OPEN SOURCE Jurnal Linguistik Komputasional Jurnal Ilmu Teknik dan Komputer Jurnal Abdidas JUSTIN (Jurnal Sistem dan Teknologi Informasi) Universal Raharja Community (URNITY Journal) Jurnal Indonesia Sosial Teknologi Eduvest - Journal of Universal Studies Jurnal Pengabdian Masyarakat Nasional Engagement: Jurnal Pengabdian Kepada Masyarakat Journal Collabits
Afiyati, Afiyati
Universitas Mercu Buana
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Neuroscience Public Health Social Sciences Other

Published : 21 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 21 Documents
Search

 1   2   3 
Comparative Sentiment Analysis of Indonesian Leadership Transitions on Platform X Using LSTM and Naïve Bayes: A Dual-Label Evaluation Using Lexicon-Based and Manual Annotation Alief Rama, Alief Ramadhan Dwi Putra; Afiyati, Afiyati
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 2 (2025): Jurnal Teknologi dan Open Source, December 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i2.4596

Abstract

This study compares Long Short-Term Memory (LSTM) and Naïve Bayes algorithms for sentiment analysis focused on leadership transitions within Indonesian social media. A dataset of 5,942 Indonesian-language tweets from platform X (formerly Twitter) was collected and labeled using both lexicon-based and manual annotation methods. Manual labeling was crucial to capture the nuanced and context-dependent sentiment often missed by lexicon-based techniques, especially during periods of heightened political discourse. The LSTM model was implemented for its ability to capture sequential dependencies in text, while Naïve Bayes was used as a computationally efficient baseline. Both models were rigorously evaluated using standard classification metrics, including accuracy, precision, recall, and F1-score. Experimental results show that LSTM achieved 71.6% accuracy with lexicon-based labels and 77.9% with manual labels. In comparison, Naïve Bayes achieved 61.5% and 78.2%, respectively. LSTM demonstrated better generalization across sentiment categories, particularly for neutral sentiments, while Naïve Bayes proved more effective on highly polarized datasets. These findings underscore the importance of strategic model selection based on data quality and labeling methods. The results offer valuable insights for political sentiment analysis and the development of data-driven decision-making tools in the digital political landscape.
Co-Authors Abdul Karim Ahmad, Ersa Alief Rama, Alief Ramadhan Dwi Putra Anis Cherid Anny Kartika Sari Ardi, Liandy Hayanto Arrachman, Abdul Kholiq Ayu, Kurnia Gusti Azhari Azhari Bambang Jokonowo Fahrezi, Zidane Gian Maulana Hadi Santoso Hadi Santoso Hanif, Ilham Hilyah Magdalena Karisma Riskinanti Kurnia, Rizki Ade Lestari, Mulyati Lukman Hakim Maun Harahap, Rachmita Mohamad Yusuf Muchtar, Irfan Rasyid muhammad rizky, muhammad Naima, Faaza Ningrum, Rahma Farah Novrizal, Kevin Riando, Dhafin Roza, Yuni Sabar Rudiarto Saddam Wahib Rozaqi Sakhrassalam, Haytsam Siti Maesaroh Sukur, Daan Surya Aji Prasetio Syaban, Zulfikar Syafiq, Nur Muhammad Zihni Yuli yuli Astari Zelina, Nur Zulia Suriastuti, Mira