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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 MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURNAL TEKNOLOGI DAN OPEN SOURCE Jurnal Linguistik Komputasional Jurnal Ilmu Teknik dan Komputer Jurnal Abdidas Jurnal Teknik Informatika (JUTIF) Jurnal Abdimas Indonesia : Jurnal Abdimas Indonesia 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 Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Neuroscience Public Health Social Sciences Other

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Journal : JUITA : Jurnal Informatika

 1 
Challenges of Sarcasm Detection for Social Network : A Literature Review Afiyati Afiyati; Azhari Azhari; Anny Kartika Sari; Abdul Karim
JUITA : Jurnal Informatika JUITA Vol. 8 Nomor 2, November 2020
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1012.329 KB) | DOI: 10.30595/juita.v8i2.8709

Abstract

Nowadays, sarcasm recognition and detection simplified with various domains knowledge, among others, computer science, social science, psychology, mathematics, and many more. This article aims to explain trends in sentiment analysis especially sarcasm detection in the last ten years and its direction in the future. We review journals with the title’s keyword “sarcasm” and published from the year 2008 until 2018. The articles were classified based on the most frequently discussed topics among others: the dataset, pre-processing, annotations, approaches, features, context, and methods used. The significant increase in the number of articles on “sarcasm” in recent years indicates that research in this area still has enormous opportunities. The research about “sarcasm” also became very interesting because only a few researchers offer solutions for unstructured language. Some hybrid approaches using classification and feature extraction are used to identify the sarcasm sentence using deep learning models. This article will provide a further explanation of the most widely used algorithms for sarcasm detection with object social media. At the end of this article also shown that the critical aspect of research on sarcasm sentence that could be done in the future is dataset usage with various languages that cover unstructured data problem with contextual information will effectively detect sarcasm sentence and will improve the existing performance.
Sentiment Analysis of X Users Toward Electric Motorcycles Using SVM and BERT Algorithms Calvin Adiwinata; Afiyati Afiyati
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 2, July 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i2.26152

Abstract

This study presents a comparative analysis of Support Vector Machine (SVM) and Bidirectional Encoder Representations from Transformers (BERT) for sentiment analysis on electric motorcycles in Indonesia using data from the social media platform X, formerly known as Twitter. The dataset of 128,711 tweets collected between 2015 and 2024 was refined through systematic preprocessing, reducing the corpus to 38,954 entries after data cleaning, tokenization, and feature selection. The objective was to evaluate algorithm performance in classifying public sentiment, with metrics including accuracy, precision, recall, and computational efficiency. Results showed that SVM achieved higher overall accuracy 89.74% with strong precision for positive sentiment 91%, while BERT, specifically the IndoBERT variant, demonstrated superior recall for negative sentiment 91% despite slightly lower accuracy 87.90%, effectively capturing nuanced contextual language, such as sarcasm, informal expressions, and emotionally ambiguous statements that require deeper semantic understanding beyond literal word meanings. Computational analysis revealed that SVM required approximately 53 minutes of CPU training, compared to BERT’s 3.3 hours on GPU. The study suggests that SVM is optimal for rapid, resource-constrained applications, whereas BERT excels in detailed contextual analysis. These findings guide stakeholders in selecting algorithms based on analytical priorities, such as monitoring public reception or addressing consumer concerns
Co-Authors Abdi Wahab 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 Budiana, Kelik Calvin Adiwinata Efy Yosrita, Efy Fahrezi, Zidane Gian Maulana Hadi Santoso Hadi Santoso Hanif, Ilham Hanifah, Nurul Afif Hilyah Magdalena Imbuh Rochmad Indrianto Indrianto Irfan Rasyid Muchtar Karisma Riskinanti Kurnia, Rizki Ade Lestari, Mulyati Lukman Hakim Mira Zulia Suriastuti Misni Misni Mohamad Yusuf Muhaimin Hasanudin muhammad rizky, muhammad Naima, Faaza Ningrum, Rahma Farah Novrizal, Kevin Prima, Andhika Rachmita Maun Harahap Rahmat Budiarto Riando, Dhafin Roza, Yuni Rudiarto, Sabar Saddam Wahib Rozaqi Sakhrassalam, Haytsam Setiyo Budiyanto Siti Maesaroh Sukur, Daan Surya Aji Prasetio Syaban, Zulfikar Syafiq, Nur Muhammad Zihni Yuli yuli Astari Zelina, Nur