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INDONESIA
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 1,045 Documents
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Perbandingan Kinerja Model ARIMA-GARCH dan LSTM Dalam Peramalan Volatilitas Bitcoin Miezan El khoir; Fenty Ariany
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.9788

Abstract

Bitcoin is a cryptocurrency aset with extreme volatility, necessitating precise forecasting models for investment risk mitigation. This study aims to analyze and forecast Bitcoin price volatility using an integrated Autoregressive Integrated Moving Average - Generalized Autoregressive Conditional Heteroskedasticity (ARIMA-GARCH) approach and compare its performance with a Deep Learning method, specifically Long Short-Term Memory (LSTM). The data used is the daily closing price of Bitcoin for the period 2018 to 2025. The results indicate that the ARIMA(1,1,1)-GARCH(1,1) model effectively captures the volatility clustering phenomenon, with a significant beta parameter value of 0.8691, indicating long-term volatility persistence. However, in terms of price prediction accuracy, the LSTM model significantly outperforms the conventional statistical model. Based on the testing, the ARIMA-GARCH model produced a Mean Absolute Percentage Error (MAPE) of 18.11%, which falls into the "good forecasting" category. In contrast, the LSTM model achieved a MAPE of 3.09%, categorized as "highly accurate forecasting." The significant difference in Root Mean Square Error (RMSE) values also reinforces that the LSTM architecture is more adaptive in processing non-linear data patterns and complex Bitcoin price fluctuations. This study concludes that while ARIMA-GARCH excels in risk structure analysis, the LSTM model provides more reliable price projection results for crypto market participants.
Sentiment Classification on Indonesian Game Sequels: A Comparative Analysis of SVM and Naive Bayes on Coffee Talk Franchise Reviews Nanda Yuris Riziq; Edy Mulyanto
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.9794

Abstract

User reviews on Steam are a critical source of feedback for game developers, yet manual sentiment analysis at scale is impractical. This study aims to compare Support Vector Machine (SVM), Multinomial Naive Bayes (MNB), and Complement Naive Bayes (CNB) for binary sentiment classification and to analyze sequel reception patterns through cross-game evaluation. Reviews were preprocessed with negation-aware stopword removal and WordNet lemmatization, then vectorized with TF-IDF unigram and bigram features. Four scenarios were evaluated: two within-game baselines, a cross-game generalization, and a combined evaluation. Class imbalance was handled at the model level via class weighting for SVM and the CNB variant. Macro-averaged F1-Score was the primary metric. SVM consistently outperformed both Naive Bayes variants, achieving macro-F1 of 0.81 within-game and 0.75 cross-game. MNB collapsed to majority-class prediction across all scenarios; in S2, all three models also failed on the minority class due to the small test partition (n=6). The cross-game result indicates that sentiment patterns transfer reasonably from the original game to its sequel, with the performance drop concentrated in the minority class. These findings offer practical guidance for Indonesian game developers monitoring sequel reception through automated sentiment analysis.
Klasifikasi Risiko Bencana di Indonesia Menggunakan SVM dan Random Forest Erland Adhe Sharendra; Tri Widodo; Damayanti Damayanti; Okma Arnilia
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.9818

Abstract

Indonesia is a country with a high level of disaster vulnerability, requiring effective methods to accurately classify disaster risk levels. This study aims to analyze and compare the performance of Support Vector Machine (SVM) and Random Forest algorithms in disaster risk classification. The dataset used consists of disaster event data from 2019–2024, including disaster type, region, number of victims, and population density. Disaster risk levels were classified into three categories, namely low, medium, and high, based on the total impact calculated from the number of victims. The proposed method includes data preprocessing, normalization, and train-test data splitting. The results show that both models achieved high performance, where Random Forest obtained an accuracy of 95.66% and SVM achieved 95.28%, with ROC-AUC values of 0.9823 and 0.9769, respectively. Random Forest demonstrated slightly better performance with an accuracy difference of 0.38% and more consistent prediction results. The high performance indicates that the models were able to recognize the main patterns within the dataset, although the results were also influenced by the characteristics of the data used. Overall, Random Forest is more suitable for disaster risk classification on data with complex characteristics.
Arrhythmia Detection Using XGBoost with Recursive Feature Elimination: A Two-Stage Machine Learning Approach Suci Mutiarani; Tikaridha Hardiani
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.9840

Abstract

Arrhythmia is a cardiac rhythm disorder that can lead to severe complications, including heart failure and sudden cardiac death. Accurate electrocardiogram (ECG)-based arrhythmia detection remains challenging due to high-dimensional features and class imbalance. Therefore, this study aims to develop a two-stage machine learning approach for arrhythmia detection using Recursive Feature Elimination (RFE) and Extreme Gradient Boosting (XGBoost). The proposed approach performs binary classification to distinguish normal and arrhythmia conditions, followed by multi-class classification to identify arrhythmia subtypes. SMOTE is applied to address class imbalance, while Grid Search with cross-validation is used for hyperparameter optimization. Furthermore, the trained model is implemented in a web-based application for interactive prediction and visualization. Experimental results show that the optimized binary classification model achieves an accuracy of 0.89 and an F1-score of 0.87. Meanwhile, the multi-class classification model achieves an accuracy of 0.69 and a weighted F1-score of 0.66. The results indicate that the proposed approach performs effectively for binary arrhythmia detection. However, performance in multi-class classification remains limited due to imbalance and insufficient samples in several arrhythmia subtypes. This study contributes by proposing an integrated framework that combines Recursive Feature Elimination (RFE) for feature selection, SMOTE for imbalance handling, XGBoost with GridSearchCV-based hyperparameter optimization, and a two-stage classification approach for ECG-based arrhythmia detection and subtype classification. In addition, the proposed model is implemented in a web-based application to support interactive prediction and visualization. Overall, this study demonstrates the potential of integrating RFE, XGBoost, and SMOTE for ECG-based arrhythmia detection and practical web-based implementation.
Aspect-Based Sentiment Analysis on Skintific Product Reviews Using IndoBERT Asyifa Hafizah Putri; Christian Sri Kusuma Aditya
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.9864

Abstract

The rapid growth of the beauty industry has generated a massive volume of online reviews where traditional sentiment analysis fails to capture contradictory opinions across specific product features. This study implements Aspect-Based Sentiment Analysis (ABSA) using the IndoBERT-base-p1 architecture on 2,139 review data points from Female Daily, integrated with a specialized slang normalization stage to mitigate linguistic noise. The novelty lies in evaluating IndoBERT’s bidirectional attention robustness in processing technical medical terminology alongside Indonesian social media slang—a complexity often overlooked in prior beauty domain studies. This study contributes a novel methodological pipeline that bridges deep learning architectures with domain-specific linguistic preprocessing, providing a benchmark dataset for Indonesian beauty product reviews. The results showed that IndoBERT was able to distinguish nuances of sentiment, with superior performance in the Effectiveness (F1-Score 72.57%) and Texture (F1-Score 71.10%). Although the average score was affected by sample limitations in certain aspects, the model proved effective in capturing the semantics of Indonesian consumer slang. Ultimately, this research provides a practical contribution for consumers in validating product quality specifically and for producers as a basis for evaluating product performance in the public eye.
Hybrid CNN-BiLSTM untuk Analisis Sentimen Multi-Platform terhadap Insiden Keamanan Pangan Program Makan Bergizi Gratis Mohamad Rival Farid Riwaldi; Aripin Aripin
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.9896

Abstract

The decline in stunting prevalence in Indonesia has not been accompanied by improvements in the quality of nutritional intervention program implementation, including the Free Nutritious Meal Program (MBG), which sparked public controversy following food safety incidents in several regions. The high volume of cross-platform public opinion on social media requires an analytical approach capable of simultaneously capturing diverse linguistic styles from various sources. This study proposes a hybrid Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) classification model to analyze public sentiment regarding the incidents, with CNN extracting local feature patterns and BiLSTM modeling bidirectional word-sequence dependencies. A total of 3,416 comments were collected from five social media platforms (X, Instagram, TikTok, YouTube, and Facebook), then processed through text preprocessing and initial lexicon-based labeling into three sentiment classes: negative, neutral, and positive. To strengthen label validity, the labeling quality was validated through manual annotation by two independent annotators, yielding a Cohen’s Kappa value of κ = 0.828. The dataset was split using an 80:20 stratified scheme, with class weight applied to reduce bias caused by class imbalance without changing the number of samples in each class. The hybrid model was compared with two baseline models, CNN and BiLSTM, using macro F1-score as the primary metric, while accuracy was used as a supporting metric. The experimental results show that the hybrid CNN–BiLSTM model achieved a macro F1-score of 90.38% and an accuracy of 94.59%, outperforming both baseline models. Misclassification analysis revealed that most errors occurred in argumentative comments, negation, and contrastive sentences, reflecting the limitations of lexicon-based labeling in capturing nuanced language. Overall, this approach demonstrates the potential of cross-platform deep learning-based sentiment analysis as an initial component for monitoring public opinion on national-scale government policies. This study contributes by providing a manually validated multi-platform Indonesian dataset, developing a hybrid CNN-BiLSTM architecture with a class weight scheme effective for three-class sentiment classification on informal text, and opening opportunities for applying deep learning as a means of data-driven public opinion monitoring.
Recommendation of Vocational Major Recommendation Using Artificial Neural Networks with ADAM Optimization Fawwaz Dzakwan; Muhammad Hikmat Fathur Rahman; Mardi Hardjianto
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.7472

Abstract

This study proposes an Artificial Neural Network (ANN)-based recommendation framework for vocational major selection using student academic-feature profiles and ADAM optimization. The dataset consisted of 9,984 student records from West Java, derived from the school proposal recapitulation linked to DAPODIK. The predictive task was formulated as a six-class classification problem covering major categories in vocational education. Data preprocessing included data checking, target encoding, feature standardization, and train-test splitting. The ANN model used two hidden layers with ReLU activation and a softmax output layer, and was trained for 50 epochs using the ADAM optimizer. To enhance practical usability, predicted class probabilities were combined with major-specific qualification thresholds to enable the system to produce both primary and alternative recommendations. Experimental results showed that the model achieved 82% accuracy, an average precision of 0.88, a recall of 0.82, and an AUC of 0.8748, indicating useful discriminative performance for vocational major recommendation. The study provides a clearer distinction among the recommendation basis, predictive model, and operational qualification screening in educational decision support. However, the system still relies mainly on academic features and has not yet been benchmarked against alternative classifiers. Future work should incorporate interest and aptitude variables, class-wise error analysis, and comparative baseline evaluation.
Comparative Evaluation of Local Llama and Mistral Models in RAG for Helpdesk Documentation Septian Pratama; Sajarwo Anggai; Murni Handayani
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.8895

Abstract

The increasing volume of technical documentation and repetitive support requests at PT XYZ has made it difficult for helpdesk personnel to retrieve accurate information efficiently and consistently. The 114 internal applications that PT XYZ oversees have different documentation, troubleshooting protocols, and frequently asked questions, which leads to dispersed knowledge sources and drawn-out problem solving. This study uses locally deployed Large Language Models (LLMs), specifically Llama and Mistral, to design and assess a Retrieval-Augmented Generation (RAG) based chatbot for internal helpdesk knowledge retrieval in order to solve this issue. The suggested solution combines generative language models with semantic document retrieval in PostgreSQL using PGVector. The study compares the effectiveness of local LLMs in RAG and non-RAG configurations, employing 1,068 internal question-answer pairs for knowledge retrieval and 214 evaluation questions for performance assessment. ROUGE, BLEU, and Cosine Similarity measures are used for evaluation. According to experimental findings, RAG greatly enhances both models' performance. While Mistral improved from 0.0984 to 0.8983, Llama ROUGE-1 score rose from 0.1710 to 0.7345. With a ROUGE-1 score of 0.8983, a BLEU-1 score of 0.8161, and a Cosine Similarity score of 0.8916, Mistral with RAG performed the best of all setups. These results show that integrating RAG with locally installed LLMs greatly improves contextual correctness and response relevance, enabling safe and scalable implementation for enterprise helpdesk knowledge management.
Komparasi Decision Tree, Random Forest, dan XGBoost untuk Deteksi Dini Risiko Akademik, Kehadiran, dan Karakter Siswa M. Hafidhatul Fathoni; Junadhi Junadhi; Susanti Susanti; Hadi Asnal
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.9452

Abstract

Schools routinely collect academic, attendance, and character data, but their use remains limited to administrative recording. As a result, changes in student conditions are not always transformed into early risk signals, such as academic decline, repeated absenteeism, or accumulated character-related violations. The problem addressed in this study is how to evaluate an early detection model that can capture these three risk dimensions simultaneously using imbalanced and temporally ordered monthly longitudinal data. This study compares Decision Tree, Random Forest, and XGBoost for detecting Academic Risk, Attendance Risk, and Character Risk in the following month. The dataset consists of longitudinal student records from SMKS YUM Pesantren Teknologi Riau in 2025, covering 206 students across 12 months. The raw dataset contained 2,472 rows and was reduced to 2,266 valid rows after feature engineering and time-shifting target construction. Model evaluation used rolling-origin temporal evaluation, majority baseline, temporal persistence baseline, and imbalance handling strategies: no balancing, class_weight, and partial SMOTE on training data. Macro F1 was used as the primary metric because the class distribution was imbalanced. The best models were Random Forest with class_weight for Academic Risk, with Macro F1 of 0.5005 and High-Risk recall of 0.6185; Decision Tree with class_weight for Attendance Risk, with Macro F1 of 0.4586 and High-Risk recall of 0.5339; and Random Forest with class_weight for Character Risk, with Macro F1 of 0.4761 and High-Risk recall of 0.5698. These findings indicate that machine learning models can provide early High-Risk signals, but predictions should be verified by teachers or homeroom teachers.
Random Forest, LSTM, and IndoBERT Comparison for TikTok App Sentiment Analysis Imam Saputra; Mesran Mesran; Ruziana Mohamad Rasli
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.9714

Abstract

The rapid growth of social media platforms like TikTok has generated a massive volume of user reviews on the Google Play Store, serving as a critical indicator of application service quality. However, the unstructured nature of Indonesian social media text and the significant imbalance between sentiment classes pose substantial challenges for automated classification systems. Addressing this class imbalance is highly crucial for application developers, as critical negative and neutral feedback containing essential feature complaints is easily marginalized by the overwhelming majority of positive reviews, leading to biased operational insights. This research conducted a comprehensive comparative study of three distinct computational paradigms: Random Forest, Long Short-Term Memory (LSTM), and IndoBERT, to identify the most effective model for sentiment analysis. A dataset of 5,000 TikTok reviews was meticulously processed using a negation-aware preprocessing pipeline to preserve semantic integrity. To address class imbalance, architecture-specific techniques were deployed, including SMOTE for Random Forest, Class Weighting for LSTM, and Random OverSampling for IndoBERT. The experimental results demonstrate that IndoBERT significantly outperforms other models, achieving the highest global accuracy of 81% and a Macro F1-Score of 0.56. While Random Forest and LSTM yielded lower accuracies of 75% and 71%, respectively, they exhibited stability in predicting the majority class but struggled with the inherent ambiguity of neutral sentiments. The study concludes that IndoBERT’s bidirectional self-attention mechanism provides superior contextual understanding of Indonesian slang and non-formal syntax. This research contributes a robust framework for application developers to monitor public opinion objectively. Furthermore, the findings highlight that despite advanced balancing techniques, the "neutrality bottleneck" remains a challenge, suggesting that future research should explore aspect-based sentiment analysis to enhance classification granularity in the Indonesian NLP domain.

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