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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jumat Informatika: Jurnal Pengabdian Masyarakat Jurnal Informatika: Jurnal Pengembangan IT
Sari, Luky Vianika
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Computer Science & IT Education Electrical & Electronics Engineering Other

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A Hybrid Classification Model Based on BERT for Multi-Class Sentiment Analysis on Twitter Uyun, Shofwatul; Rosalin, Rizqi Praimadi; Sari, Luky Vianika; Sucinta, Hanny Handayani
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i2.30665

Abstract

Social media is one of the media to convey opinions and sentiments. Sentiment analysis is an important tool for researchers and business people to understand user emotions efficiently and accurately. Choosing the right classification model has a significant impact on sentiment classification performance. However, the diversity of model architectures and training techniques poses its own challenges. In addition, relying on a single classification model often causes noise, bias, data imbalance, and limitations in handling data variations effectively. This study proposes a hybrid classification model where BERT is the baseline. Furthermore, BERT will be hybridized using LSTM, and BERT is hybridized with CNN to improve sentiment analysis on Twitter social media data. The hybrid approach aims to reduce the limitations of a single model classifier by increasing model effectiveness, reducing bias, and optimizing the model on imbalanced data. The following are the steps in this study, data preprocessing, data balancing, tokenization, model training, and performance evaluation. Three models were trained: the baseline BERT model, the BERT-CNN hybrid, and the BERT-LSTM hybrid. Model performance was assessed using accuracy, precision, recall, and F1 score. Experimental results show that the baseline BERT model achieves an accuracy of 91.45%, while BERT-LSTM achieves 91.60%, and BERT-CNN achieves the highest accuracy of 91.80%. However, further analysis is needed to determine whether these improvements are statistically significant and whether the hybrid model offers additional benefits beyond accuracy, such as remembering underrepresented sentiment categories.
Peningkatan Kapasitas Mahasiswa dalam Penulisan Skripsi melalui Pelatihan Microsoft Word dan Mendeley Sari, Luky Vianika; Rosalin, Rizqi Praimadi Rosalin; Akmal, Muhammad Syafiq; Fahrurrozi, Muhammad
Jumat Informatika: Jurnal Pengabdian Masyarakat Vol. 6 No. 2 (2025): Agustus
Publisher : LPPM Universitas KH. A. Wahab Hasbullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32764/abdimasif.v6i2.6060

Abstract

Writing a thesis is a crucial step in completing undergraduate studies. However, many students experience technical difficulties, particularly with document organization and citation management. This Community Service (PKM) activity aims to improve students' thesis writing skills through Microsoft Word and Mendeley training. The training was conducted online via Google Meet and was attended by final-year students from various study programs. The material covered organizing scientific documents using Microsoft Word features (headings, automatic table of contents, citation styles), as well as reference management using Mendeley. The implementation method involved material presentations, live demonstrations, and real-time guided independent practice sessions. Evaluation results showed an increase in participants' understanding of the use of applications to support scientific writing. This activity significantly contributed to preparing students for the thesis writing process more systematically and efficiently.
Evaluasi User Experience Google Lens pada Pengguna Baru Menggunakan Metode Cognitive Walkthrough Sari, Luky Vianika; Rosalin, Rizqi Praimadi; Mulyanto, Agus; Zamzami, Ahmad; Juliyanto, Tatang
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 3 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i3.8651

Abstract

New users often experience difficulties using Google Lens due to a lack of familiarity with Augmented Reality (AR) technology. This study aims to evaluate the usability of Google Lens using the Cognitive Walkthrough method, focusing on three main task scenarios: text translation, object identification, and barcode scanning. A total of 60 respondents participated in the study, with data collected through a Likert-based questionnaire (1–5) and direct observation of user interactions. The quantitative analysis results showed that Google Lens obtained an average score of 3.5 for satisfaction, 3.8 for efficiency, and 3.2 for ease of navigation. Evaluation per scenario showed the highest success rate for barcode scanning (92%), followed by text translation (85%), while object identification had the lowest success rate (78%). Qualitative findings revealed that less intuitive navigation, unclear function icons, a lack of initial guidance, and limited object identification accuracy were the main obstacles for new users. Based on these results, this study recommends several improvements, including optimizing the interface design, adding descriptive labels to icons, providing personalization features, and developing interactive tutorials for new users. With these recommendations, it is hoped that Google Lens can become an application that is easier to learn, more efficient, and provides a more satisfying experience, while also enriching the literature related to usability evaluation of Augmented Reality-based applications. 
Co-Authors Agus Mulyanto Ahmad Zamzami Akmal, Muhammad Syafiq Juliyanto, Tatang Muh. Fahrurrozi Rosalin, Rizqi Praimadi Rosalin, Rizqi Praimadi Rosalin Shofwatul ‘Uyun Sucinta, Hanny Handayani