Jurnal Ilmu Komputer dan Informatika

Comparative Analysis of Bidirectional Encoder Representations from Transformers Models for Twitter Sentiment Classification using Text Mining on Streamlit

Ahmad Fajar Tatang, Mohammad Hasbi Assidiqi — Wed, 31 Dec 2025 00:00:00 +0000

Social media platforms like Twitter have become highly influential in shaping public opinion, making sentiment analysis on tweet data crucial. However, traditional techniques struggle with the nuances and complexities of informal social media text. This research addresses these challenges by conducting a comparative analysis between the non-optimized BERT (Bidirectional Encoder Representations from Transformers) model and the BERT model optimized with Fine-Tuning techniques for sentiment analysis on Indonesian Twitter data using text mining methods. Employing the CRISP-DM methodology, the study involves data collection through Twitter crawling using the keyword biznet, data preprocessing steps such as case folding, cleaning, tokenization, normalization, and data augmentation, with the dataset split into training, validation, and testing subsets for modeling and evaluation using the IndoBERT-base-p1 model specifically trained for the Indonesian language. The results demonstrate that the Fine-Tuned BERT model significantly outperforms the non-optimized BERT, achieving 91% accuracy, 0.91 precision, 0.90 recall, and 0.91 F1-score on the test set. Fine-Tuning enables BERT to adapt to the unique characteristics of Twitter sentiment data, allowing better recognition of language and context patterns associated with sentiment expressions. The optimized model is implemented as a web application for practical utilization. This research affirms the superiority of Fine-Tuned BERT for accurate sentiment analysis on Indonesian Twitter data, providing valuable insights for businesses, governments, and researchers leveraging social media data.

Hybrid Classification of Date Fruits Varieties Using GLCM, RGB Features and Convolutional Neural Network

Wed, 15 Oct 2025 00:00:00 +0000

Classifying date fruit varieties is a challenging task due to their high visual similarity in terms of texture and color. This study aims to address this issue by developing an automated classification model that combines handcrafted Gray Level Co-occurrence Matrix (GLCM) texture features and average RGB color channels with Convolutional Neural Network (CNN) classifiers. The dataset comprises 1,658 images from nine varieties of date fruits, divided into 70% training and 30% testing subsets. The proposed workflow includes image preprocessing (resizing, normalization, grayscale conversion), extraction of GLCM features (contrast, energy, homogeneity, correlation), computation of average RGB channels, feature fusion, and CNN training using VGG16 and VGG19 architectures with Adam and Adadelta optimizers. The model performance is evaluated using accuracy, precision, recall, F1-score, and confusion matrix. Experimental results demonstrate that VGG19 with the Adam optimizer achieved the highest validation accuracy of 91%, slightly outperforming VGG16 (90%) but remaining below the 96% accuracy reported in prior studies using MobileNetV2. The integration of handcrafted features enhanced sensitivity to subtle color and texture variations, although it introduced potential feature redundancy. In conclusion, the hybrid GLCM–RGB–CNN with VGG19 and Adam achieved 91% accuracy, proving the benefit of combining handcrafted and deep features while highlighting opportunities for further enhancement through data augmentation and architectural optimization.

Performance Comparison of Logistic Regression and XGBoost for Credit Card Fraud Detection using Random Undersampling and Hyperparameter Tuning

Hasri Akbar Awal Rozaq, Deni Sutaji — Wed, 31 Dec 2025 00:00:00 +0000

Credit card fraud is a growing problem due to the rise of card transactions. This study investigates the effectiveness of Logistic Regression (LogReg) and Extreme Gradient Boosting (XGBoost) in identifying fraudulent transactions in a highly imbalanced dataset, where only 8% of the data represents fraudulent activity. To address the class imbalance, random undersampling was applied, reducing the number of legitimate transactions. This technique significantly improved LogReg's ability to detect fraud, with the AUC-ROC increasing from 0.7994 to 0.9089. XGBoost performed well even without hyperparameter tuning or random undersampling, indicating its robustness as a baseline model. The study highlights the critical importance of addressing class imbalance in fraud detection. Both LogReg and XGBoost demonstrated potential, particularly when combined with techniques like undersampling or hyperparameter tuning. These findings underscore the need for effective data preprocessing methods to enhance the performance of machine learning models in detecting credit card fraud.