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All Journal JSI: Jurnal Sistem Informasi (E-Journal) Journal of Information Systems Engineering and Business Intelligence JIKO (Jurnal Informatika dan Komputer) JURNAL MEDIA INFORMATIKA BUDIDARMA JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Sebatik Kilat JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Infotekmesin Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Komunitas: Jurnal Pengabidian Kepada Masyarakat TIN: TERAPAN INFORMATIKA NUSANTARA Infotek : Jurnal Informatika dan Teknologi Idealis : Indonesia Journal Information System SKANIKA: Sistem Komputer dan Teknik Informatika ABDI MOESTOPO: Jurnal Pengabdian pada Masyarakat Bulletin of Computer Science Research Bit (Fakultas Teknologi Informasi Universitas Budi Luhur) Prosiding Seminar Nasional Rekayasa Teknologi Industri dan Informasi ReTII Telematika MKOM Jurnal Pengabdian Masyarakat Nasional
Juanita, Safitri
Program Studi Sistem Informasi,Fakultas Teknologi Informasi, Universitas Budi Luhur
Aerospace Engineering Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

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Journal : bulletin of computer science research

 1 
Model Deteksi Berita Hoaks Bahasa Indonesia Menggunakan Multinomial Naïve Bayes dan AdaBoost Classifier Haniifaa Hafiizh; Safitri Juanita
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.927

Abstract

The rapid growth of the internet has led to the massive and uncontrolled dissemination of information across various digital platforms, allowing hoax news to reach a wide audience and influence public opinion in a short period of time. This condition highlights the need for a reliable automated detection system. However, existing methods still face limitations in terms of accuracy, result stability, and reliance on manual verification processes. Therefore, this study aims to compare and analyze the performance of two classification algorithms in detecting Indonesian-language hoax news accurately and effectively. This study follows the CRISP-DM framework, beginning with the collection of hoax and non-hoax news articles from turnbackhoax.id and detik.com, resulting in 2,281 samples. The data understanding stage involves analyzing dataset characteristics and evaluating data quality. During data preparation, text elements that explicitly indicate hoax labels are removed, followed by feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF). The dataset is then trained and tested using data split ratios of 70:30, 80:20, and 90:10 by applying Multinomial Naïve Bayes and AdaBoost Classifier algorithms. Model performance is evaluated using a confusion matrix. The results show that AdaBoost achieves superior performance, with an accuracy of 0.9879 (98.79%), outperforming Multinomial Naïve Bayes, which attains an accuracy of 0.9712 (97.12%). The performance of AdaBoost is also consistent across different evaluation scenarios, indicating that it is more suitable as an automated hoax news detection model for the dataset used in this study.
Pemodelan Topik pada Komentar Media Sosial X menggunakan Latent Dirichlet Allocation Ardelia Adzra; Safitri Juanita
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1161

Abstract

Sexual harassment is a social issue widely discussed on the social media platform X. However, the high volume of unstructured comments makes it difficult to manually identify the main topics of discussion. This study aims to identify the main topics in comments related to sexual harassment on X using the Latent Dirichlet Allocation (LDA) method. The data used consist of comments on the topic of sexual harassment collected from X during the 2024–2026 period. The research stages include data collection, data preparation, dictionary and corpus construction, LDA modeling with hyperparameter tuning, evaluation using coherence score, and topic interpretation based on dominant keywords and representative data. The results show that the best LDA model consists of four topics with a coherence score of 0.517. These four topics are interpreted as Handling Cases of Sexual Harassment in Educational Environments, Victims’ Experiences and Psychological Impacts, Cases of Sexual Harassment in Higher Education, and Protection Related to Sexual Harassment. These findings indicate that the LDA method is capable of identifying the main topics in sexual harassment comments and helping to organize unstructured social media data into information that is easier to understand. The contribution of this study is the proposed Latent Dirichlet Allocation (LDA)-based topic modeling approach with hyperparameter tuning to identify and organize unstructured sexual harassment comments on the social media platform X into coherent and interpretable topic clusters. The resulting topic mapping provides valuable insights into the issues that receive the greatest public attention and can serve as a foundation for understanding public concerns. Furthermore, these findings have the potential to support the development of victim support services, including telemedicine-based systems.
Co-Authors Abid Famasya Abdillah Ahya Ulumuddin Ananda Heruansyah Apriantoni Apriantoni Ardelia Adzra Asef Solehuddin Atha Shalih Fatin Azrul Akbar Cornelius Bagus Purnama Putra Devita Diah Prasetyaningrum Diana Permatasari Diana Purwitasari Dolly Virgian Shaka Yudha Sakti Eva Febriyanti Fadil Muhammad Zuhri Farill Andika Wardana Fildzah Fitriani Grace Gata, Grace Haniifaa Hafiizh Ika Disja Afriyani Ita Novita Java Sika Maulana Joko Wijayanto Kurnia Ramadhan Laura Putri Wijaya Handoyo Lukas Ono Harsongko M. Anif Mahmud Didi Nugraha Misbahul Fajri Mochamad Andri Muhammad Anif Muhammad Wildan Muzdalifah Muzdalifah Peter Jaya Putri Hayati Reva Ragam Santika Ripal Abadi Rizky Indra Ramadhani Samsinar Samsinar Sarita Fabriani Siswanto Siswanto Sopan Bukhari Lubis Syahnaz Sofinar Amru Wahyu Pramusinto Win darto Yuliazmi, Yuliazmi Zahra Syifa Prasasti