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Building of Informatics, Technology and Science
Published by Forum Kerjasama Pendidikan TInggi
ISSN : 26848910     EISSN : 26853310     DOI : -
Core Subject : Science,
Computer Science & IT
Building of Informatics, Technology and Science (BITS) is an open access media in publishing scientific articles that contain the results of research in information technology and computers. Paper that enters this journal will be checked for plagiarism and peer-rewiew first to maintain its quality. This journal is managed by Forum Kerjasama Pendidikan Tinggi (FKPT) published 2 times a year in Juni and Desember. The existence of this journal is expected to develop research and make a real contribution in improving research resources in the field of information technology and computers.
Arjuna Subject : -
Articles 1,083 Documents
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Analisis Sentimen Komentar TikTok pada Konten Sound Horeg Menggunakan XLM-RoBERTa, Kamus Bahasa Jawa dan CatBoost Fiona Evelyne Luther; Radius Tanone
Building of Informatics, Technology and Science (BITS) Vol 8 No 2 (2026): September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i2.11115

Abstract

Comments on TikTok can provide valuable data for analyzing public sentiment toward social and cultural phenomena. However, informal language, multilingual expressions, code-mixing, and limited linguistic resources for regional languages present challenges for sentiment classification. Sound Horeg was selected as the research subject because this social and cultural phenomenon generates diverse public responses on social media, including support and opposition, making it a relevant context for examining public perceptions through sentiment analysis. This study aims to develop a hybrid sentiment analysis pipeline for classifying TikTok comments related to Sound Horeg by integrating XLM-RoBERTa, a Javanese sentiment lexicon, and CatBoost. The methodological novelty of this study lies in integrating the multilingual semantic representations of XLM-RoBERTa with a Javanese lexical resource in the context of Sound Horeg sentiment analysis, while evaluating the contribution of each component through an ablation study. The dataset was collected from five Sound Horeg videos and consisted of 7,330 comments, which were processed through data cleaning, preprocessing, Lexicon-Based sentiment labeling, XLM-RoBERTa embedding extraction, and Random Undersampling. Lexicon-based labeling produced 960 labeled comments, consisting of 559 positive and 401 negative comments, while Random Undersampling produced 802 balanced samples for the main model. The 768-dimensional XLM-RoBERTa embeddings were used as input features for CatBoost. The main model achieved an accuracy of 89.44%, an F1-Macro score of 89.44%, and an ROC-AUC of 90.25%. The ablation study showed that the Lexicon-only configuration achieved an accuracy of 94.79%, followed by XLM-R only at 93.23% and XLM-R + Lexicon at 91.15%. These results indicate that the simpler configurations, particularly Lexicon-only and XLM-R only, achieved higher performance than the main pipeline, suggesting that the hybrid pipeline cannot yet be considered the most optimal configuration for operational implementation. These findings indicate that XLM-RoBERTa can provide effective semantic representations without using skor leksikals as the main features, while the contribution of Javanese vocabulary remains limited because it was detected in only 1.6% of Group A comments and 0.8% of Group B comments. This finding implies that the selection of components in a sentiment analysis system should consider the balance between predictive performance, processing complexity, and the linguistic characteristics of the analyzed data.
Model Hibrida PSO-LSTM dan Interpretasi SHAP untuk Peramalan Harga Emas Domestik Multivariat Fajar Arafat; Nugroho Dwi Saputro; Aris Tri Joko Harjanto
Building of Informatics, Technology and Science (BITS) Vol 8 No 2 (2026): September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i2.11144

Abstract

Forecasting volatile domestic gold prices requires a predictive model that is not only accurate but also transparent. Although the Long Short-Term Memory (LSTM) architecture excels at capturing temporal dependencies in time-series data, its performance is highly sensitive to hyperparameter tuning and suffers from a black-box nature. To overcome the limitations of manual optimization and lack of transparency, this study proposes a hybrid multivariate PSO-LSTM forecasting model integrated with the SHapley Additive exPlanations (SHAP) method. Built upon the CRISP-DM framework using daily historical data from 2020 to 2026, the Particle Swarm Optimization (PSO) algorithm is employed to automatically optimize the number of neurons, learning rate, and batch size. Furthermore, the SHAP method with K-Means background reduction is applied to map the contribution of input features. Experimental results demonstrate that the PSO-LSTM model outperforms the baseline LSTM by reducing Root Mean Squared Error (RMSE) by 11.70% and Mean Absolute Error (MAE) by 14.08%, while achieving a highly accurate Mean Absolute Percentage Error (MAPE) of 2.62%. Statistical testing using the Diebold-Mariano test confirms that the predictive superiority of the PSO-LSTM model is statistically significant. SHAP analysis indicates that historical memory of domestic gold prices provides the highest marginal contribution to model predictions, followed by USD/IDR exchange rate adjustments and global gold price sentiment. Overall, this study contributes an integrated financial modeling framework that not only enhances accuracy on highly volatile data but also provides rational feature interpretability as a foundation for hedging decisions by investors and policymakers.
Analisis Komparatif Metode Logistic Regression dan Support Vector Machine pada Sentimen Aplikasi GuitarTuna Muhammad Yusuf; M Rudi Sanjaya; Dwi Rosa Indah; Ardina Ariani
Building of Informatics, Technology and Science (BITS) Vol 8 No 2 (2026): September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i2.11158

Abstract

This study focuses on identifying sentiment trends among GuitarTuna app users while comparing the performance of Support Vector Machine (SVM) and Logistic Regression (LR) algorithms. A total of 20,339 raw data points were collected from the Google Play Store, the App Store, and questionnaires. Following a rigorous filtering process, which involved eliminating duplicate entries, empty text, and irrelevant responses, 13,615 valid data points were obtained. The analysis proceeded through text preprocessing, feature extraction using Term Frequency-Inverse Document Frequency (TF-IDF) weighting, and classification using both algorithms. Evaluation results indicate that SVM achieved an accuracy of 90.53%, a macro recall of 0.60, and a macro F1-score of 0.62. This performance was descriptively superior to that of LR, which recorded an accuracy of 90.30%, a macro recall of 0.56, and a macro F1-score of 0.59. However, McNemar's test, yielding a p-value of 0.6338, demonstrated that SVM's nominal advantage was not statistically significant. Furthermore, the limited sample size for the neutral class contributed to low prediction precision for both models within that category. Regarding review topics, positive sentiment was largely driven by user satisfaction with tuning precision, ease of use, and educational features. Conversely, negative sentiment was dominated by complaints concerning subscription costs, transaction issues, feature access restrictions, and connectivity problems. This research makes contributions in two key areas. From a practical standpoint, the topic extraction findings provide developers with concrete guidance for prioritizing feature and service improvements. From an academic perspective, the study offers comprehensive empirical evidence regarding the comparative evaluation of machine learning algorithms on cross-platform review datasets characterized by class imbalance.

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