Jurnal Teknik Informatika (JUTIF)
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Prediction Of Clay Mining Production Value Using Linear Regression Model With Multi-Swarm Particle Swarm Optimization
Get PDFThe progress of a nation or a country can be recognized from its income through various industries inside. Mining refers to one of the most advanced industries in Indonesia. The majority of mining in Indonesia is open-pit mining which is exposed directly to the sky. This study focuses on modeling data from rainfall, working hours, and production yields. It employed the Multi-Swarm Particle Swarm Optimization (MSPSO) algorithm to find multiple linear regression modeling by minimizing the Mean Squared Error (MSE) value. The value for the production results was then predicted using the existing multiple linear regression model. In terms of testing, the best model having an MSE of 288.0656 occurred at the parameters of Npop 180, acceleration coefficient 1 by 0.7, acceleration coefficient 2 by 0.7, acceleration coefficient 3 by 0.7, wmin 4, wmax 9 within 100 iterations
Comparative Analysis of DBSCAN, OPTICS, and Agglomerative Clustering Methods for Identifying Disease Distribution Patterns in Banjarnegara Community Health Centers
Get PDFThe variation in disease distribution patterns across community health centers in Banjarnegara Regency necessitates a precise segmentation analysis to support effective allocation of healthcare resources. This study aims to compare the effectiveness of three clustering methods DBSCAN, OPTICS, and Agglomerative Clustering in grouping Puskesmas based on the type and number of diseases they manage. The evaluation methods used include the Silhouette Score and the Davies-Bouldin Index, which assess the quality of the clustering results. The analysis indicates that Agglomerative Clustering produces the most stable cluster structures, reflected in its highest Silhouette Score, compared to DBSCAN and OPTICS, which tend to yield more noise and less optimal clustering quality. These findings suggest that hierarchical clustering approaches are more effective in the context of healthcare service distribution data at the primary care level. The results of this study are expected to serve as a foundation for the formulation of data-driven and region-based health policies, particularly in designing more targeted interventions and optimizing the distribution of healthcare services
Comparing BERTBase, DistilBERT and RoBERTa in Sentiment Analysis for Disaster Response
Get PDFSocial media platforms are vital for real-time communication during disasters, providing insights into public emotions and urgent needs. This study evaluates the performance of three transformer-based models—BERTBase, DistilBERT, and RoBERTa—for sentiment analysis on disaster-related social media data. Using a multilingual dataset sourced from the Social Media for Disaster Risk Management (SMDRM) platform, the models were assessed on classification metrics including accuracy, precision, recall, and weighted F1-score. The results show that RoBERTa consistently outperforms the others in classification performance, while DistilBERT offers superior computational efficiency. The analysis highlights the trade-offs between model accuracy and runtime, emphasizing RoBERTa's suitability for scenarios prioritizing accuracy, and DistilBERT's potential in time-sensitive or resource-constrained applications. These findings support the integration of sentiment analysis into disaster response systems to enhance situational awareness and decision-making
Sentiment Analysis of Fizzo Novel Application Using Support Vector Machine and Naïve Bayes Algorithm with SEMMA Framework
Get PDFThe increasing popularity of digital reading platforms in Indonesia, such as Fizzo Novel, has generated many user reviews that can be analyzed to understand their satisfaction. This study analyzes user sentiment toward Fizzo Novel using the SEMMA (Sample, Explore, Modify, Model, Assess) framework, and compares the performance of the Support Vector Machine (SVM) and Naïve Bayes algorithms. A total of 139,759 reviews were collected from the Google Play Store through web scraping. The data was then processed through normalization, tokenization, lexicon-based sentiment labeling, and feature extraction using TF-IDF. To address class imbalance, the SMOTE technique was applied. The results showed that SVM achieved the highest accuracy, exceeding 96%, with a consistent F1-score across all sentiment classes. In contrast, Naïve Bayes recorded lower accuracy (75.82% before SMOTE and 73.63% after SMOTE), along with a decline in performance for the neutral class. SVM proved more reliable in handling large and imbalanced text data. Practically, the results of this study can help application developers such as Fizzo Novel in automatically understanding user opinions. With an accurate sentiment classification model, developers can monitor reviews in real-time, identify issues such as excessive advertising or an unpopular chapter division system, and design feature improvements based on real user needs. This research also provides a foundation for algorithm selection in future large-scale sentiment analysis projects and recommends SVM as the more appropriate choice in this context
Comparison of IndoNanoT5 and IndoGPT for Advancing Indonesian Text Formalization in Low-Resource Settings
Get PDFThe rapid growth of digital communication in Indonesia has led to a distinct informal linguistic style that poses significant challenges for Natural Language Processing (NLP) systems trained on formal text. This discrepancy often degrades the performance of downstream tasks like machine translation and sentiment analysis. This study aims to provide the first systematic comparison of IndoNanoT5 (encoder-decoder) and IndoGPT (decoder-only) architectures for Indonesian informal-to-formal text style transfer. We conduct comprehensive experiments using the STIF-INDONESIA dataset through rigorous hyperparameter optimization, multiple evaluation metrics, and statistical significance testing. The results demonstrate clear superiority of the encoder-decoder architecture, with IndoNanoT5-base achieving a peak BLEU score of 55.99, significantly outperforming IndoGPT's highest score of 51.13 by 4.86 points—a statistically significant improvement (p<0.001) with large effect size (Cohen's d = 0.847). This establishes new performance benchmarks with 28.49 BLEU points improvement over previous methods, representing a 103.6% relative gain. Architectural analysis reveals that bidirectional context processing, explicit input-output separation, and cross-attention mechanisms provide critical advantages for handling Indonesian morphological complexity. Computational efficiency analysis shows important trade-offs between inference speed and output quality. This research advances Indonesian text normalization capabilities and provides empirical evidence for architectural selection in sequence-to-sequence tasks for morphologically rich, low-resource languages
Enhancing Customer Purchase Behavior Prediction Using PSO-Tuned Ensemble Machine Learning Models
Get PDFPredicting customer purchase behavior remains a significant challenge in e-commerce and marketing analytics due to its complex and nonlinear patterns. This study introduces a machine learning framework that integrates ensemble learning models with Particle Swarm Optimization (PSO) for hyperparameter tuning to improve classification accuracy and class discrimination. Several ensemble algorithms, including CatBoost, XGBoost, LightGBM, AdaBoost, and Gradient Boosting, were compared against a baseline Logistic Regression model, both with default and PSO-optimized configurations. Experiments on a real-world e-commerce dataset containing behavioral and demographic variables showed that ensemble methods substantially outperformed traditional models across accuracy, F1-score, and ROC AUC metrics. Notably, the PSO-tuned Gradient Boosting model achieved the highest ROC AUC of 0.9547, improving the AUC by approximately 0.0076 compared to its default configuration, while CatBoost obtained the highest overall accuracy and F1-score. PSO optimization was especially effective in enhancing simpler models such as Logistic Regression but showed marginal gains and some convergence instability in more complex ensemble models. Feature importance analyses consistently identified variables such as time spent on the website, discounts availed, age, and income as key drivers of purchase intent. These findings demonstrate the benefit of combining ensemble learning with metaheuristic optimization, offering actionable insights for developing robust, data-driven marketing strategies
SEMAR v1.0: An AI-driven Conceptual Model and Architecture for Smart Government in Indonesia Using a Mixed-Methods Approach
Get PDFBuilding smart cities represents a national priority for Indonesia to enhance global competitiveness, with artificial intelligence (AI) driven smart government as a key enabler. However, implementation faces significant challenges including unmeasured organizational maturity, lack of service innovation, fragmented governance, and minimal citizen engagement leading to government institutions' failure in achieving smart city vision. This study aims to develop a holistic conceptual model to identify critical success factors and evaluate processes that integrate public services, fostering AI-driven smart government innovation at strategic level. This research employs mixed-methods exploratory sequential design combining qualitative techniques (Systematic Literature Review, expert interviews) with quantitative validation (citizen survey, statistical analysis). The model was constructed using Factor Analysis, Thematic Analysis, TOGAF framework, and multidimensional view with validation through triangulation, expert judgment, Focus Group Discussions, and statistical analysis. Results show a comprehensive model consisting of 6 dimensions, 17 key components, and 5-layer organizational architecture with high reliability (Cronbach's Alpha 0.709-0.866) and expert consensus (86% agreement in Fuzzy Delphi Method analysis). This framework, referred to as SEMAR v1.0 (Smart Government Nusantara), serves as a benchmark for assessing the maturity and readiness of local government institutions in Indonesia. It offers the potential to improve SPBE scores through systematic evaluation, while also providing a theoretical foundation for smart government scholarship and a practical blueprint for policy implementation
User Interface Evaluation of the Business Development Center Website at UIN Syarif Hidayatullah Jakarta: A Content, Visual, and Navigation Perspective
Get PDFThe business development center State Islamic University Syarif Hidayatullah Jakarta (UIN Jakarta) as the main manager of the campus's business lines that functions to manage, develop, generate funding sources, underpin the development of business ideas and implement business ideas that have been designed and agreed upon by the UIN Jakarta Leadership and other stakeholders. This study aims to evaluate the business development center website UIN Jakarta from a user interface perspective. This study uses a questionnaire containing to evaluate the user interface in terms of content, visuals and navigation. Based on the questionnaire data that has been filled out by respondents, the most significant findings from each aspect. The content aspect, valued at 76.3% for information regarding the vision, mission, work programs, and the foreword of the head of the center are presented completely and easily accessible, but 63.2% complained that some parts of the service are not detailed enough. The visual aspect, valued at 63.2% for the consistent campus color identity on the background and title, but 60.5% complained that the contrast of white text on the dark gray background area is less user-friendly. While the navigation aspect, valued at 71.1% for the about, services, and gallery features are clearly visible, but 63.2% complained that there is no visual highlight for the menu being accessed. This study contributes to the development of the business development center website UIN Jakarta based on responses from participants completed the questionnaire to be more optimal and provide all information regarding the business units at UIN Jakarta as a form of promotion to be known and serve the wider community. In addition, this study presents innovations for developing a user interface evaluation framework for institution websites, particularly those related to business development center in state islamic universities
From Logs to Insights in the Pulp & Paper Industry: Generating Structured Alarm Reports Using LLMs and RAG
Get PDFEffective alarm management is essential in industrial environments to ensure operational safety and minimize costly downtime. Traditional rule-based reporting systems often struggle to handle heterogeneous alarm log formats and the complexity of natural language queries, limiting their adaptability in real-world applications. To address these limitations, this study proposes a generative alarm reporting system that integrates Large Language Models (LLMs) with a Retrieval-Augmented Generation (RAG) framework. The system converts natural language queries into structured JSON filters, enabling efficient retrieval of contextual information from historical alarm logs. Three open-source LLMs—CodeLlama-7B, LLaMA 3.1-8B, and Mistral-7B—were locally deployed and evaluated using both quantitative and qualitative methods. Experimental results show that CodeLlama-7B achieved the best overall performance, with an Exact Match Accuracy of 0.80, a Field Match score of 93.8%, and a 0% Parse Failure Rate, outperforming the other models in reliability and structural consistency. Compared to conventional rule-based approaches, the proposed LLM-RAG integration demonstrates improved relevance, interpretability, and responsiveness in alarm reporting. This work represents the first systematic benchmarking of locally deployed open-source LLMs for industrial alarm management, providing a replicable framework and highlighting their potential to advance intelligent, real-time, and domain-specific reporting in the pulp and paper industry and beyond
PROTEGO: Improving Breast Cancer Diagnosis with Prototype-Contrastive Autoencoder and Conformal Prediction on the WDBC Dataset
Get PDFBreast cancer remains one of the leading causes of mortality among women, making accurate and trustworthy early detection a critical challenge in healthcare. To address this, we propose PROTEGO, a Prototype-Contrastive Autoencoder with integrated Conformal Prediction, designed to achieve both high diagnostic accuracy and reliable uncertainty quantification. The framework combines dual-head autoencoding, supervised contrastive learning, prototype-based regularization, and conformal calibration to generate discriminative yet interpretable representations. Using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, PROTEGO was trained and evaluated through stratified data splits, with performance measured by AUROC, AUPRC, F1-score, Balanced Accuracy, Brier score, calibration error, and conformal coverage metrics. The results show that PROTEGO achieves highly competitive performance with an AUROC of 0.992 and an AUPRC of 0.995, while uniquely providing conformal coverage guarantees with an average set size close to one and more than 92% decisive predictions. Ablation studies confirm the complementary role of each component in enhancing both accuracy and calibration. These findings demonstrate that integrating prototype-guided representation learning with conformal prediction establishes a clinically meaningful diagnostic framework. PROTEGO highlights the importance of unifying precision and reliability in medical AI, offering a step toward more interpretable, safe, and clinically trustworthy systems for breast cancer detection