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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Informatika: Jurnal Pengembangan IT
Rosalin, Rizqi Praimadi
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Computer Science & IT Electrical & Electronics Engineering

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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.
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 Juliyanto, Tatang Sari, Luky Vianika Shofwatul ‘Uyun Sucinta, Hanny Handayani