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All Journal JSM (Jurnal SIFO Mikroskil) Jurnal Teknologi Informasi dan Ilmu Komputer InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Data Science: Journal of Computing and Applied Informatics Jambura Journal of Mathematics Building of Informatics, Technology and Science Jurnal Mantik INFOKUM Journal La Multiapp Engineering Science Letter Journal of Novel Engineering Science and Technology
Ronsen Purba
Magister Komputer, STMIK Mikroskil
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering

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Journal : Building of Informatics, Technology and Science

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Deteksi Potensi Depresi dari Unggahan Media Sosial X Menggunakan IndoBERT Situmorang, Gilbert Fernando; Purba, Ronsen
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5496

Abstract

Over the past few decades, mental disorders such as depression have increased and become a serious public health issue. Many affected individuals choose not to seek professional support due to social stigma. Social media platforms like X provide opportunities to study mental health on a large scale because users often share their personal experiences and emotions. However, there are challenges in understanding language patterns and context in posts, necessitating appropriate techniques and models to effectively detect potential depressions. Utilizing Natural Language Processing (NLP) techniques, this study analyzes 37,554 texts from social media posts to detect potential depressions. This study employs the IndoBERT model, an adaptation of BERT trained on Indonesian text data, to identify potential depression from social media texts. Data were collected through scrapping using negatively and positively connotated keywords, which were consulted with psychiatrists. The text pre-processing includes case folding, text cleaning, spell normalization, stopword removal and stemming. The data were then labeled using the IndoBERT emotion classification model, categorizing negative emotions as depression and positive emotions as normal. The model was trained and evaluated using accuracy, precision, recall, and F1-score metrics, with the best results showing an accuracy of 94.91%, precision of 94.91%, recall of 94.91%, and an F1-score of 94.91%. The results indicate that the IndoBERT model is effective in detecting potential depression from social media texts. However, there are limitations due to the reliance on social media posts, which may not fully reflect the users’ emotional conditions.
Implementation of IndoBERT in Sarcasm Detection using Random Forest Towards Sentiment Analysis Sibarani, Sabrina Adela Br; Purba, Ronsen; Limbong, Ricky Paian
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.5801

Abstract

Sarcasm, a subtle form of irony, often introduces a discrepancy between the literal meaning of words and the intended message, making it a significant challenge for sentiment analysis systems. Misinterpreting sarcasm in social media comments can lead to inaccurate sentiment classification, hindering decision-making processes in areas like customer feedback analysis and social opinion mining. This study addresses this issue by evaluating the effectiveness of sarcasm detection in Indonesian text using a Random Forest Classifier (RFC) integrated with IndoBERT. The research employs 10-fold cross-validation to measure performance. Without IndoBERT, the RFC model achieved average accuracy, precision, recall, and F1-score of 78.83%, 78.83%, 79.01%, and 78.83%, respectively. Incorporating IndoBERT significantly improved performance, with all metrics exceeding 84%. Furthermore, 5-fold cross-validation achieved the highest performance, with all metrics reaching 97.24%. This research contributes to developing more robust natural language processing models tailored to Indonesian linguistic contexts, specifically for sarcasm detection.
Political Comperative Analysis of Indonesian Political Fake News Detection using IndoBERT-Bi-GRU-Attention Models: Evaluating Performance on Narratives and News Headlines Datasets Manurung, Juliana Damayanti; Purba, Ronsen
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6938

Abstract

The instant and massive spread of fake news on social media negatively impacts public trust in the media and news agencies. In politics, fake news is often used by politicians to gain support ahead of elections. Detecting fake news in Indonesia poses a significant challenge, especially for communities vulnerable to misinformation. This study aims to develop a new model that combines IndoBERT with Bi-GRU and Attention. Additionally, a comparison is made between the main model and two word embedding models, FastText and GloVe. The tests were conducted on datasets of headlines and news narratives separately. Data was sourced from CNN, Tempo.co, Kompas, and TurnBackHoax.ID. The results show that the IndoBERT-Bi-GRU-Attention model with FastText excelled on the headline dataset with an accuracy of 99.76% and an F1-Score of 99.61%, while the main IndoBERT-Bi-GRU-Attention model excelled on the narrative dataset with an accuracy of 99.08% and an F1-Score of 98.40%. This research demonstrates that IndoBERT can be combined with Bi-GRU, significantly contributing to the development of fake news detection models.
Co-Authors Adek Efnaldi Rambe Aldo Alex Sitorus Ali Akbar Lubis Andri Andri, Andri Anisah - Anna Agustiana Anwar Utama Apin Ridwan Aritonang, Mendarissan Arwin Arwin Arwin Halim Arwin Halim Arwin Halim Asif Rana Br Sibarani, Sabrina Adela Erlina Halim Erwin Setiawan Panjaitan Fandi Presly Simamora Feredy Lestari Pandia Frans Agus Purba Harry Darmawan Hendra - Indra Syahputra Irpan Pardosi Jimmy Nganta Ginting Jimmy Peranginangin Kardika Sidabariba Kelvin Chen Lestari, Wulan Sri Manurung, Juliana Damayanti Mark Dhruba Sikder Maya Silvi Lidya Megawaty Simamora Muhammad Fermi Pasha Munthe, Andreas Nadya Sikana Pasha, Muhammad Fermi Pipin, Sio Jurnalis Poerianto Poerianto Ricky Paian Limbong Rivaldi Lubis Rudy Rudy Sari Fatmawati Sastra Wandi Nduru Sibarani, Sabrina Adela Br Sinaga, Frans Mikael Situmorang, Gilbert Fernando Sunario Megawan Vicky, Vicky Winardi, Sunaryo Wita Clarisa Ginting Yudi Pratama Hasibuan Yundari, Yundari