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All Journal Jurnal Sains Student Research JUTEKOM - Jurnal Teknologi dan Ilmu Komputer
Syabil, Muhammad Al Faris
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Computer Science & IT Other

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Sentiment Analysis of Coretax Tax Application Users Using IndoBERT and Web Scraping on the X (Twitter) Platform: Case Study on Indonesian Taxpayer Digital Service Feedback Syabil, Muhammad Al Faris; Al-Fahri, Fajar
JOURNAL SAINS STUDENT RESEARCH Vol. 4 No. 1 (2026): Februari
Publisher : CV. KAMPUS AKADEMIK PUBLISING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61722/jssr.v4i1.7258

Abstract

This study analyzes public sentiment toward the Coretax tax system based on user opinions posted on the X (Twitter) platform. The objective is to assess how the public perceives the system’s stability, accessibility, and performance during periods of high usage. A quantitative text-based approach was applied using Natural Language Processing (NLP) techniques. Data were collected through web scraping of tweets containing Coretax-related keywords and processed through six preprocessing stages: case folding, cleaning, tokenizing, normalization, stopword removal, and stemming. Sentiment classification was conducted using the IndoBERT model mdhugol/indonesia-bert-sentiment-classification, which categorized tweets into positive, negative, and neutral classes. The results show that 181 tweets expressed positive sentiment, 171 negative sentiment, and 29 neutral sentiment. Negative sentiment predominantly relates to system errors and login difficulties, whereas positive sentiment commonly appears when the system functions normally. These findings demonstrate that system instability remains the primary factor influencing negative perceptions of Coretax and provide useful insights for improving the reliability of digital tax services.
Facial Emotion Recognition Based on Convolutional Neural Network Using FER2013 Dataset Syabil, Muhammad Al Faris; Harahap, Lailan Sofinah; Nasution, Muhammad Rafiq
Jurnal Teknologi informasi dan Ilmu Komputer Vol. 2 No. 1 (2026): Januari 2026
Publisher : Nolsatu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65258/jutekom.v2.i1.44

Abstract

Facial emotion recognition is an important research area in computer vision and artificial intelligence, with applications in human–computer interaction, affective computing, and intelligent systems. This study aims to evaluate the performance of a Convolutional Neural Network (CNN) for facial emotion recognition using the FER2013 dataset. The FER2013 dataset consists of grayscale facial images with a resolution of 48×48 pixels and includes seven emotion classes: angry, disgust, fear, happy, neutral, sad, and surprise. Due to its low image resolution and imbalanced class distribution, FER2013 presents significant challenges for emotion classification tasks. An experimental research approach was employed by implementing a baseline CNN architecture composed of convolutional, pooling, and fully connected layers. Image normalization and batch-based data generation were applied during preprocessing. The model was trained using the Adam optimizer with categorical cross-entropy loss, and an early stopping mechanism was utilized to prevent overfitting. Model performance was evaluated using accuracy, precision, recall, F1-score, and confusion matrix analysis. The experimental results show that the proposed CNN model achieved an overall test accuracy of 55.50%. Emotions with distinctive facial features, such as happy and surprise, obtained higher F1-scores, while minority and visually subtle classes, particularly disgust and fear, exhibited lower performance. These findings indicate that a simple CNN architecture can provide reasonable performance on challenging facial emotion datasets while highlighting the impact of class imbalance and limited image resolution. The proposed model can serve as a baseline for further improvements in facial emotion recognition systems.
Co-Authors Al-Fahri, Fajar Harahap, Lailan Sofinah Nasution, Muhammad Rafiq