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Implementation of Agile Scrum in the Digital Transformation of Insurance Claims Services at Integrated Ports
Purpose: This study develops a web-based insurance claim information system at PT Jasaraharja Putera within an integrated port environment. The conventional claim process, which relies on manual communication such as WhatsApp and email, often causes verification delays, document loss, lack of transparency, and risks of duplicate claims or fraud. The system aims to accelerate and simplify claim submission, particularly during reporting and initial verification by field officers, through real-time integration with the company’s core systems. Additionally, digital monitoring features enhance oversight of the claims process, reducing duplication and fraud potential. Methods: This research employed a descriptive qualitative method with an Agile Scrum system development approach. Data collection involved interviews with field officers and the claims team, analysis of existing business processes, system trials, and distribution of questionnaires to users. System development was conducted iteratively over several sprints, with each sprint producing features that were tested and evaluated based on user feedback. Results: Accelerate the claims input and verification process in the field. Increase transparency of claims status through a real-time monitoring dashboard. Reduce the risk of lost documents, human error, and potential duplicate claims. Achieve high user satisfaction levels based on questionnaire results at each development sprint. Novelty: This research introduces a real-time, integrated web-based claims system for port operations, applying Agile Scrum and monitoring features to prevent fraud. It offers practical solutions to public insurance claim inefficiencies and theoretical insights for advancing insurance information systems.
Copyright (c) 2026 Scientific Journal of Informatics
Automated Essay-Answer Grading using Transformer and GenAI-Based Error Analysis on Answer Sentence
Purpose: This study aims to address the limitations of manual essay assessment, which is time-consuming and prone to subjectivity, by developing an automated essay grading system that is faster, more efficient, and more objective. In addition to producing scores, the system is intended to support learning by providing meaningful feedback on students’ writing errors. Methods: The research applies a transformer-based approach to automated essay scoring in order to capture deeper semantic relationships between student answers and reference answers. Unlike conventional word embedding methods, transformers are used to model contextual meaning at the word, phrase, and sentence levels. The system is further enhanced with an AI-based error analysis module that identifies categories of student errors, including content omissions, conceptual misunderstandings, and structural inaccuracies, enabling the generation of corrective feedback alongside numerical scores. Result: The experimental results show that the proposed model achieves strong agreement with human scoring, indicated by a Pearson correlation coefficient (r) of 0.8432 and a Mean Absolute Error (MAE) of 0.6013. These results demonstrate an improvement in prediction accuracy compared to word embedding-based approaches. Furthermore, the system successfully generates relevant and actionable error feedback that supports students in understanding their mistakes and improving their essay quality. Novelty: The novelty of this research lies in the integration of transformer-based automated essay scoring with an AI-driven error analysis module. Rather than focusing solely on score prediction accuracy, the proposed approach combines quantitative scoring with qualitative error feedback, enabling the system to function not only as an assessment tool but also as a learning support mechanism that helps students understand and improve their writing.
Performance Evaluation of Otsu and Sauvola Thresholding for Structured Document Binarization
Purpose: Digitizing public administration records, particularly structured forms such as the Transport of Plants and Wildlife Abroad (Surat Angkut Tumbuhan dan Satwa Liar Luar Negeri / SATS-LN), necessitates meticulous preparation for precise subsequent analysis. Most of the photos in the SATS-LN archives are scanned, and they have inconsistent lighting, varying resolution, and background noise, which makes it difficult to separate the text from the backdrop and read it clearly. This work identifies the optimal SATS-LN binarization approach for preserving textual structure and suppressing background artifacts. Methods: A four-stage pipeline is used. First, Detectron2 localizes seven important SATS-LN fields. Second, binarization is investigated with global Otsu and adaptive Sauvola thresholding under three parameter configurations. Third, following binarization, Contrast-Limited Adaptive Histogram Equalization (CLAHE) boosts local contrast. Finally, Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Difference from Reference for Distortion (DRD), Precision, Recall, F1-score, and Foreground Ratio are assessed on 200 annotated SATS-LN documents (150 scanner-based/DOC and 50 camera captured/CAM). Result: The acquisition domain and assessment model affect binarization performance on 200 SATS-LN documents (150 DOC scans and 50 CAM images). Global Otsu_T10 has the highest median PSNR (21.19 dB) and the lowest median MSE (494.69), indicating a visually cleaner background. However, segmentation-based metrics show better stroke preservation with Sauvola, as Sauvola_k05 has the strongest DOC text–background separation (F1 = 0.938). In the CAM domain, where illumination variability dominates, Sauvola performs better across structural and segmentation indicators, with Sauvola_k04 performing best overall (F1 = 0.980) and mitigating the over-segmentation tendency of strict global thresholds. The Sauvola window (25x25) and CLAHE clip limit (1.0) results suggest using Sauvola_k05 for DOC and Sauvola_k04 for CAM to preserve text integrity and reduce background artifacts. Novelty: This study presents a novel field-level binarization assessment that combines automated cropping and ground-truth evaluation, providing practical guidance for robust preprocessing that supports scalable, reliable, and cross-device public document digitization.
Gene Expression-Based Lung Cancer Prediction in Smokers Using SVM and Moth-Flame Optimization Algorithm
Purpose: Lung cancer remains one of the leading causes of death worldwide, especially among active smokers, yet early detection is still difficult because traditional imaging methods have limited sensitivity for identifying early-stage abnormalities. This study was conducted to address the need for a more accurate computational approach capable of detecting lung cancer at a molecular level using gene expression data. The goal is to build a model that can reliably distinguish cancerous from non-cancerous samples based on genomic features. Methods: This study uses the GSE4115 gene-expression dataset consisting of 187 bronchial epithelial samples and 22,215 gene features. The Moth-Flame Optimization (MFO) algorithm was implemented to select the most informative subset of genes from this high-dimensional dataset. A Support Vector Machine (SVM) classifier was then trained using multiple kernels, with hyperparameter tuning performed to identify the optimal configuration for each kernel. Results: Experimental results show that the Polynomial kernel achieved the highest performance using 286 MFO-selected features, reaching an accuracy of 0.84 and an F1-score of 0.85. These results confirm that combining MFO with SVM improves classification performance compared to using raw gene data without feature selection. Novelty: This study provides the first application of MFO-based feature selection for lung cancer prediction in smokers using the GSE4115 dataset. The findings demonstrate the value of nature-inspired optimization for handling high-dimensional genomic data and offer a promising direction for developing early computational detection methods.
Rainfall Prediction at Ahmad Yani Meteorological StationUsing Integration ARIMA and LSTM
Purpose: Predicting rainfall using ARIMA, LSTM, and Hybrid ARIMA-LSTM models to obtain accuracy values ​​on data at the Ahmad Yani Semarang station. Methods: This study implements the ARIMA, LSTM, and hybrid ARIMA-LSTM models to determine which of these models produces the most significant predictions using rainfall data at the Ahmad Yani Meteorological Station in Semarang. This method proves whether using the hybrid ARIMA-LSTM, which is a combination of the two models, is able to provide greater accuracy compared to the ARIMA/LSTM model. The results of these predictions can certainly help relevant stakeholders to improve rainfall accuracy, especially at the Ahmad Yani Meteorological Station. Result: By utilizing the power of statistical models (ARIMA) with deep learning (LSTM), the results of these two models provide higher accuracy compared to each model, as seen from the accuracy of the best ARIMA model using RMSE 15.8 and MAE 8.7, the best LSTM model RMSE 14.65 and MAE 9.06, while in the HYBRID ARIMA-LSTM model the best RMSE is 14.1 and MAE 9.06. Novelty: This research adds to the knowledge regarding the accuracy or combination of ARIMA and LSTM models which are rarely used, especially in the world of meteorology or rainfall. By utilizing the ARIMA model which is able to read linear patterns and the LSTM model which reads non-linear patterns, the accuracy of rainfall increases and can help related stakeholders.
Hybrid Feature Selection for Effective Heart Disease Detection: A Multi-Algorithm Machine Learning Approach
Purpose: This research aims to develop an effective early detection model for heart disease with data balancing and hybrid feature selection. The study seeks to enhance predictive accuracy and minimize errors, providing a robust model for clinical decision support systems. Methods: The study used the Heart Failure Prediction dataset derived from Kaggle. A novel hybrid framework was implemented, integrating SMOTEENN (Synthetic Minority Over-sampling Technique + Edited Nearest Neighbors) for data balancing and a Hybrid Feature Selection (HFS) method combining Chi-square and Backward Elimination. Eight machine learning algorithms, including Logistic Regression, Naïve Bayes, Decision Tree, K Nearest Neighbor, Random Forest, Gradient Boosting, Support Vector Machine, and XGBoost. Performance was assessed based on accuracy, precision, recall, f1-score, specificity, AUC Score, fallout and miss rate. Result: The proposed framework significantly improved classification performance across all algorithms. The Random Forest model emerged as the optimal classifier, achieving an accuracy of 99.44%, AUC Score of 99.98%, and a specific reduction in miss rate to 0.92% (from 10.03% baseline). The HFS method successfully reduced the feature space by 54%, identifying 'ExerciseAngina', 'FastingBS', 'ST_Slope', 'ChestPainType', and 'Sex' as the most critical predictors. The model outperformed standard approaches and recent state-of-the-art benchmarks by over 10% in accuracy. Novelty: This study introduces a synergistic integration of SMOTEENN with hybrid feature selection. The combination significantly improves model performance in early heart disease detection.
Development of VR-Based Virtual Tours for South Sorong Regency Using the Analysis, Design, Development, Implementation, and Evaluation Model
Purpose: The limited availability of digital documentation and interactive promotional media has reduced public awareness of tourism destinations in South Sorong Regency. This study aims to develop and evaluate a Virtual Reality (VR)-based virtual tour as a solution to enhance user understanding, engagement, and visitation intention. Methods: This study employed a Research and Development approach using the Analysis, Design, Development, Implementation, and Evaluation (ADDIE) model. Three tourism destinations were captured using 360° panoramic video and integrated into an interactive VR-based virtual tour. The virtual tour was evaluated with 97 respondents. Respondents completed a pre-test before using the virtual tour and a post-test after the experience to measure changes in understanding and interest, analyzed using the N-Gain method. Usability and acceptance were assessed through Likert-scale user questionnaires and percentage-based feasibility analysis. Media and material experts evaluated technical quality and content accuracy; their feedback was used for virtual tour media revision before final validation. Results: The results show improved user understanding and engagement, indicated by an N-Gain score of 0.64, categorized as moderate to high effectiveness. User evaluation yielded a feasibility score of 93%. Expert validation confirmed readiness, with feasibility scores of 89% from media experts and 94% from material experts. Novelty: This study introduces a systematic ADDIE-based engineering framework for multi-destination VR virtual tour development, integrating effectiveness analysis, usability evaluation, and expert validation as a scalable informatics solution for digital tourism promotion in underdeveloped regions.
Optimization of Mineral Fuel Export Forecasting Using Attention-based Long Short-Term Memory
Purpose: This study aims to optimize the forecasting of the Net Value of Indonesia's mineral fuel exports using the Attention-based Long Short-Term Memory (LSTM) model, supported by Dropout and Recurrent Dropout techniques that are combined to produce an optimal model. Methods: Modeling uses an LSTM architecture equipped with an Attention mechanism, as well as Dropout and Recurrent Dropout. The research procedure uses the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology. The research material used is the Indonesian mineral fuel export dataset with HS code 27 from 2014 to 2025. Model was built using the Random Search method to optimize hyperparameters such as the number of neurons (units), activation functions (Tanh, ReLu), and optimizers (Adam, Nadam, RMSprop). Result: The Attention-based LSTM model with Dropout and Recurrent Dropout techniques achieved a MAPE of 7.76%, which was better than the other models tested. Attention analysis shows that lag 12 has the greatest dominance, while lags 11 to 10 also contribute significantly, indicating an annual seasonal pattern. Projections for the next 12 months show a moderate decline in Net Value, in line with seasonal trends and historical data. Novelty: The main contribution of this research is the optimization of an Attention-based LSTM model using a combination of Dropout and Recurrent Dropout techniques, which is effective in forecasting Indonesia's mineral fuel export values because it is able to capture annual seasonal patterns, thereby improving the accuracy and stability of the forecast results.
Optimized LSTM Model Using Simulated Annealing for Autoignition Temperature (AIT) Prediction as a Hazard Indicator
Purpose: Autoignition Temperature (AIT) is the lowest temperature at which a substance will spontaneously ignite in normal air without any external ignition source. AIT is an important safety parameter in industries that handles flammable materials. Measuring AIT with conventional method is unfortunately slow, costly, and dangerous. As an alternative, an AIT prediction model can be developed using in silico approaches, specifically based on machine learning. Methods: One of the methods that can be used is Long Short-Term Memory (LSTM) since it is good at modeling the complex relationships that is involved, but unfortunately it is difficult to tune manually due to their numerous hyperparameters. Therefore, an automated strategy can be used to find the best hyperparameters for the architecture. This study aims to develop an AIT prediction model as a hazard indicator using an LSTM model optimized with Simulated Annealing (SA). Result: The experiment showed that the SA-LSTM model which uses a cooling schedule of Delta T = 0.7 outperformed the unoptimized baseline model. Novelty: The optimization raised the R2 on test data from 0.5682 to 0.5939 while also lowering the RMSE from 74.35 K to 72.10 K and the MAPE from 9.29% to 8.87%. These results confirmed that optimizing LSTM with SA gave a more robust tool for hazard indicator.
Copyright (c) 2025 Scientific Journal of Informatics
Ultra-Low-Cost Hybrid OCR–LLM Architecture for Production Grade E-KTP Extraction
Purpose: The purpose of this study is to be able to avoid limitations of inexpensive ID card data extraction services and preserve privacy, which can simultaneously achieve reliable operation even under an environment with minimum infrastructure, in particular if no dependency on GPU-based servers are required. Method: The proposed approach is a microservice pipeline with three stages: (1) local lightweight pre-processing on devices, (2) Tesseract CPU-based OCR. js, (3) fast text tokenization through a small premature external LLM. The system is developed as TypeScript backend utilizing the Hono framework with all image processing taking place locally in order to keeping user data private. Result: The result of the experimental evaluations with real ID card samples is that the system can run stably in low-performance VPS (1 vCPU, 1 GB RAM) with operation cost approximately IDR 2.5047 per extraction process and its accuracy level is acceptable for use in a production environment. Moreover, the results indicate that system latency is dominated by LLM inference at the cloud. Novelty: The main contribution and novelty of this study is that we demonstrate, for the first time, a cost-effective (privacy-preserving) OCR-LLM hybrid pipeline without demanding expensive GPU models at large scale which makes our system suitable under limited storage and resource constraints on-premises or edge environments in small organizations including micro-SaaS services.
Robust Human Gait Recognition with Convolutional Neural Network based on Gait Energy Image
Purpose: Human gait recognition is one of the developments in artificial intelligence technology. Gait recognition is a biometric recognition technique that uses no direct interaction with an object, allowing for identification of individuals based on their gait. However, this recognition faces challenges, including varying camera angles (00 - 1800), so this requires a more in-depth introduction. Methods: Therefore, based on the references, this study proposes using the Gait Energy Image (GEI) and Convolutional Neural Network (CNN) features for in-depth extraction and recognition of each image in the Casia B Dataset, which is then compared with the results of previous studies. Result: The results of this study, with the division of the Casia B Dataset 80% as training data and 20% as testing data and 11 camera angles between 00 - 1800 produced an accuracy rate of 99.48%. Novelty: So the accuracy achieved with this deep learning technique exceeds that of previous research using conventional methods and this gait pattern recognition technique can be used to be implemented in a biometric recognition system based on human gait patterns.
Optimizing Early Breast Cancer Classification Using Hybrid SVM-ANN with Ridge Embedded Feature Selection
Purpose: This study aims to enhance early breast cancer detection by systematically evaluating multiple machine learning (ML) algorithms and feature selection strategies. The goal is to identify the most effective combination of classifiers and feature selection methods for accurately distinguishing malignant from benign breast tumors, thereby improving diagnostic reliability and clinical decision support. Method: The Wisconsin Breast Cancer Dataset containing 699 samples described by nine diagnostic features was used. Tumor classes were encoded as 0 (malignant) and 1 (benign). The analysis was conducted in two stages. First, five ML algorithms—K-Nearest Neighbors (KNN), Logistic Regression, Support Vector Machine (SVM), Artificial Neural Network (ANN), and a hybrid SVM–ANN—were evaluated to establish baseline performance. Second, two feature selection approaches (wrapper and embedded) were applied to four ML models and the optimized hybrid classifier. The embedded approach employed Ridge-based feature selection to identify the most discriminative attributes and improve model generalization. Results: The hybrid SVM–ANN combined with Ridge Embedded feature selection achieved the best performance, with an accuracy of 97.86%, precision of 96.5%, recall of 96.5%, and an F1-score of 96%. This configuration outperformed all other algorithms and feature selection techniques, affirming the effectiveness of hybrid integration and embedded feature optimization. Novelty: The novelty lies in the integration of an SVM–ANN hybrid model with Ridge-based embedded feature selection for breast cancer classification. Unlike prior works that rely primarily on conventional filter or wrapper techniques, this approach demonstrates superior accuracy and robustness. The proposed framework provides a promising pathway for developing more reliable ML-based diagnostic tools in oncology.
Designing A Mobile-Based Attendance Application Using Location-Based Service for Activities Mosque
Purpose: The purpose of this study is to design a mobile-based attendance application that uses Location-Based Services to support community-based religious institutions, namely mosques, which are simple to understand for all age groups, make it easier for participants to take attendance, and make it easier for administrators to manage participant data, schedules, and attendance. Methods: Interviews and direct observations were conducted to identify problems and determine the new system required. This system utilizes the concept of geolocation by utilizing GPS and geofencing for location validation, which is described using UML and implemented in programming. The completed system was tested through blackbox testing for its features and GPS accuracy testing to determine the stability of the location tracking system. Result: Blackbox testing results show that the functions integrated into the attendance application can run 100% as expected. The GPS accuracy system performance test results obtained are sufficient for semi-outdoor and outdoor spaces with 100% valid results. Novelty: The uniqueness of this research lies in its contribution to community-based religious institutions, namely mosques, in digital attendance recording, as well as providing GPS accuracy evaluation results provided by the system to determine the most appropriate radius range. Further developments that can be made to the system include improving security procedures, as this application directly accesses the user's location, making improvements to the security system and fake location detection very important for attendance applications.
Improving the Custom Color Ordering Process with Artificial Intelligence for a Manufacturing Firm
Purpose: Practitioners who try to apply AI in Business Process Management (BPM) often face a gap between theory and real-world implementation, because most prior work stays at a high level and offers limited replicable implementation blueprints or measurable evidence of value. This study addresses that gap by reengineering a custom color ordering workflow, following the BPM lifecycle and integrating two AI components: machine learning for palette extraction and generative AI for reference image previews. Methods: The as-is process was mapped in BPMN 2.0 and analyzed using root-cause and value-added analysis. A to-be process was then designed using BPM redesign levers (task elimination, resequencing, integration, and enabling technology). An integrated web platform prototype was built and tested to measure four Process Performance Indicators (PPI): Cycle Time, Response Time, administrative staff hours per order, and Automation Ratio. Result: Prototype tests showed significant improvement. Response Time reduced from 8 days to 8 minutes (-99.4%). The overall Cycle Time reduced from 92 to 74 days (-19.5%). The administrative workload per order was reduced from 88 to 0.4 staff hours (-99.5%). The redesigned workflow automated 8 of 15 activities, increasing the Automation Ratio from 0% to 53.3%. Novelty: This study delivers a practical, replicable roadmap for embedding AI capabilities into an operational workflow while staying aligned with BPM principles and tracking measurable PPI. The blueprint can help organizations respond faster to customers, improve throughput, and reduce administrative burden, so staff can focus on higher-value work.
IoT-Integrated Computerized Maintenance Management System (CMMS) for Optimizing Maintenance Efficiency in Smart Manufacturing
Purpose: The manufacturing industry continues to face persistent challenges in maintaining equipment reliability and maintenance efficiency, particularly in scheduling, spare parts control, and real-time monitoring of machine conditions. This study aims to develop an Internet of Things (IoT)-based Computerized Maintenance Management System (CMMS) to improve maintenance effectiveness, minimize equipment downtime, and support the realization of smart manufacturing in the automotive sector. Methods: The system was developed using the ADDIE model, consisting of analysis, design, development, Implementation, and Evaluation. and was integrated with IoT sensors to acquire real-time machine temperature and operational status data. and integrated with IoT sensors to collect real-time data on machine temperature and operational status. The collected data were processed in a centralized database and presented through a web-based CMMS application comprising work order management, preventive and corrective maintenance, inventory control, and analytical reporting modules. System functionality was validated using black-box testing, while performance evaluation was conducted by comparing Mean Time Between Failures (MTBF), Mean Time to Repair (MTTR), and maintenance efficiency before and after system implementation. Result: The results indicate that all evaluated equipment experienced performance improvements following CMMS implementation, characterized by increased MTBF and reduced MTTR. On average, overall maintenance efficiency increased by approximately 368%, demonstrating significant reductions in downtime and improvements in maintenance responsiveness supported by real-time condition data. Novelty: The novelty of this study lies in the integration of IoT technology into CMMS that emphasizes the utilization of real-time machine condition data not only for monitoring purposes but also to support maintenance planning, work order management, and data-driven decision-making within a single application. The findings provide empirical evidence that effective data utilization strategies within CMMS implementations can significantly enhance maintenance efficiency and support smart maintenance practices aligned with Industry 4.0 principles.
Evaluating the Implementation of the FinOps Framework for Cloud Infrastructure Cost Management: A Case Study of Technology Companies in Indonesia
Purpose: The management of cloud infrastructure costs has become increasingly relevant in light of the rapid rate of cloud service adoption, especially in Indonesia's technology space, where there is tremendous economic pressure. Cloud infrastructure cost management solutions such as the FinOps Framework are clearly intended to provide a greater degree of accountability for financial governance and to enable cloud spending optimization when implemented holistically. In this research, we want to evaluate how FinOps adoption maturity is in several technology companies, and to find the gap between FinOps Framework principles and their real-world implementation. Methods: A mixed-methods case study was employed. To gather primary data, the first step was to interview six FinOps practitioners using semi-structured interviewing methods. Thematic analysis was used to identify thematic similarities across responses. Following the development of a simplified three-point maturity score system based on the FinOps framework, we compared each participant's maturity state in reference to the variations outlined in the FinOps literature. Result: From the interviews, there was an emphasis on key processes, such as cost ownership, budgeting, and governance, which were quite centralized, reactive, and somewhat informal, although everyone was using technical optimization approaches such as rightsizing. Our quantitative analysis corroborated this, with a cohort average maturity score of only 1.53 out of 3. This indicates a general maturity score in the "Crawl" or first stage, indicating shortcomings related to the process element of the basic practices not being integrated in a systematic way. Novelty: This research provides meaningful and helpful insights into the real challenges of tech companies in Indonesia implementing FinOps. Our research provides a clear baseline for companies working locally by measuring maturity levels and making recommendations at the end. More importantly, beyond technical optimization, it clearly identifies the need to formalise policies and to improve cooperation across functional areas. This mixed methods approach should be extended to other research by increasing sample size and allowing for a deeper exploration of the cultural context.
Optimizing Fair and Efficient Group Formation in Community Service Program Using Particle Swarm Optimization
Purpose: The rapid expansion and administrative complexity of community service programs (Kuliah Kerja Nyata/KKN) have made manual group formation increasingly inefficient, inconsistent, and prone to imbalance. This creates an urgent need for an automated, fair, and reliable optimization method capable of handling large-scale grouping constraints. This study aims to evaluate the performance of the Particle Swarm Optimization (PSO) algorithm in generating optimal KKN group formations, focusing on computational efficiency, convergence behavior, and solution quality. Methods: PSO was implemented to form 27 KKN groups using 10 independent runs. Performance metrics included execution time, optimal iteration counts, initial fitness scores, and best final scores. Each run was analyzed to observe convergence patterns and stagnation behaviors. Result: The results indicate that PSO is highly efficient, with very fast execution times and rapid convergence, often reaching optimal solutions in the first iteration. However, performance varied: some groups achieved low optimal scores (95–97), while many stagnated at extremely high scores (100000.0) with no improvement. This shows that PSO’s effectiveness depends heavily on problem characteristics and initialization. Novelty: This study identifies and explains stagnation patterns in PSO when applied to discrete, constraint-heavy academic group formation problems, an area rarely examined in prior research. The analysis provides insight into PSO’s strengths and limitations and highlights the need for improved parameter tuning and initialization strategies. The findings serve as a foundation for developing more robust optimization approaches for fair and efficient KKN group formation.
Improving Sentiment Analysis with a Context-Aware RoBERTa–BiLSTM and Word2Vec Branch
Purpose: We improve the accuracy of Twitter/X sentiment analysis with a hybrid model combining Word2Vec and the Robustly Optimized BERT Pretraining Approach (RoBERTa). However, Twitter/X text is noisy (slang/OOV) and ambiguous, so the performance of the pre-trained transformer decreases. Word2Vec is also limited to local contexts. Integrative studies of both are still limited. The idea is that Word2Vec is strong for slang/novel vocabulary (distributional semantics), while RoBERTa excels in contextual meaning; combining the two mitigates each other's weaknesses. Methods: The Sentiment140 dataset contains 1.6 million balanced tweets. The split is stratified; Word2Vec is trained solely on the training data. RoBERTa is pretrained (frozen in the first stage, then fine-tuned with some layers in the second stage). The Word2Vec and RoBERTa vectors are concatenated and processed using Bidirectional Long Short-Term Memory (BiLSTM) with sigmoid activation. Training utilizes TensorFlow and the Adam optimizer, incorporating dropout and early stopping. The decision threshold is optimized during the validation process. Result: The hybrid model achieved an accuracy of 88.09%, an F1-score of 88.09%, and an Area Under the Curve (AUC) ≈ 95.19% on the Receiver Operating Characteristic (ROC). No overfitting was observed, and the hybrid model outperformed both single baselines. The confusion matrix and ROC curve corroborate the findings. Novelty: The novelty lies in the fusion of distributional and contextual representations with a structured fusion mechanism. Limitations: Computational requirements and hyperparameter tuning are not yet extensive. Further directions: Systematic hyperparameter search and cross-validation across other large sentiment datasets to assess generalization.
Combination Of VADER Sentiment Analysis and SEQ Scale For Evaluating the Usability of The Gojek Application
Purpose: This study aims to analyze user sentiment toward the Gojek mobile application using the Valence Aware Dictionary for Sentiment Reasoning (VADER) method and to evaluate the perceived ease of use of the application using the Single Ease Question (SEQ) instrument. Methods: The research data were obtained by scraping 30,000 user reviews of the Gojek application from the Google Play Store. The reviews were processed through text preprocessing, sentiment classification using the VADER method, and subsequent mapping of sentiment polarity scores to a 1–7 SEQ usability scale. A Spearman rank correlation analysis was conducted to examine the relationship between sentiment scores and derived SEQ values. Result: The results indicate that user sentiment toward the Gojek application is predominantly positive, followed by neutral and negative sentiments. The overall average SEQ score is 4.11, suggesting that the application is generally perceived as fairly easy to use. Furthermore, a strong and statistically significant positive association was found between VADER sentiment scores and SEQ usability scores, indicating that more positive sentiment tends to be associated with higher perceived ease of use. Novelty: This study contributes to the literature by empirically integrating sentiment analysis and usability evaluation using VADER and SEQ within the context of an Indonesian super-app. The findings provide practical insights for digital application developers to identify usability strengths and areas for improvement based on large-scale user feedback.
valence aware dictionary for sentiment resoning
Unveiling IT Governance Effectiveness in e-Puskesmas Using COBIT 2019
Purpose: This study aims to evaluate the effectiveness of IT governance implementation in the e-Puskesmas system at Pengasinan Community Health Center, Bekasi City, using the COBIT 2019 framework. The research focuses on identifying the capability level and governance gaps to ensure risk management and IT service delivery align with institutional objectives. Methods: The study employs a mixed-method approach combining qualitative interviews and quantitative questionnaires. Two COBIT 2019 domains—APO12 (Managed Risk) and DSS02 (Managed Service Requests and Incidents)—were analyzed using capability level measurements and GAP analysis to determine performance alignment with governance standards. Result: The findings reveal that e-Puskesmas achieved a high capability level, with 96.88% for APO12 and 90.47% for DSS02. Despite this success, several weaknesses were identified, including delays in patient registration, data confidentiality issues, and service disruptions. The study provides strategic recommendations to enhance user training, strengthen data security, and implement continuous risk management. Novelty: This research presents one of the first applications of COBIT 2019 in evaluating IT governance for public healthcare systems in Indonesia. It demonstrates how COBIT can serve as a practical tool for assessing digital health governance maturity, ensuring data integrity, and supporting the sustainable transformation of healthcare services.
Comparative Performance Analysis of Deep Learning Models for Cryptocurrency Price Forecasting
Purpose: This research aims to find an accurate cryptocurrency price prediction model to mitigate financial risks caused by high price volatility. This research compares the predictive capabilities of five Deep Learning model, namely LSTM, GRU, BiLSTM, Transformer, and Performer, for predicting cryptocurrency prices with the highest accuracy in the digital financial market. Methods: The methods applied in this research are dataset, preprocessing data, model training, and model evaluation. The dataset used in this study, namely the price per minute data for BTC, ETH, BNB, and XRP, was obtained from Kaggle. Data processing includes normalization using MinMaxScaler and sequence generation through the Sliding Window technique. To validate each deep learning model, and four metrics consisting of MAE, MSE, RMSE, and MAPE are used for evaluation. Result: The Transformer model created the best results for the lowest MAPE value across all datasets, the smallest being BTC and ETH at 0.20%, BNB at 0.29%, and XRP at 0.36% demonstrating high accuracy and generalization. The BiLSTM was ranking second since it captured effectively the bidirectional temporal dependencies; the GRU was moderate but stable in its performance. The data showed that the accuracy of LSTM and Performer varied. Novelty: This study offers a comprehensive comparison of various Deep Learning models in detail, enabling it to find the best model for predicting cryptocurrency prices with high accuracy. This study provides valuable insights for the development of advanced deep learning-based price forecasting systems in the field of digital financial analysis.
Mapping the Contribution of Kasepuhan Ciptagelar Tourism to Regional Income and Community Economy using the K-Means Clustering Method
Purpose: This study aims to map the contribution of Kasepuhan Ciptagelar tourism to regional income and the community economy as part of the UNESCO Global Geopark Ciletuh–Pelabuhanratu, Sukabumi. The research addresses the problem of limited infrastructure, poor accessibility, and low optimization of local tourism potential that hinders its contribution to regional income. The objective is to provide a data-driven understanding of tourism’s economic and socio-cultural impact. This study aims to map the contribution of Kasepuhan Ciptagelar tourism to regional income and the community economy as part of the UNESCO Global Geopark Ciletuh–Pelabuhanratu, Sukabumi. The research addresses the problem of limited infrastructure, poor accessibility, and low optimization of local tourism potential that hinders its contribution to regional income. The objective is to provide a data-driven understanding of tourism’s economic and socio-cultural impact. Methods: A mixed-method approach was used, integrating quantitative and qualitative analyses. Data were collected through questionnaires and interviews from three main respondent groups: local community (58%), tourists (31%), and government/MSMEs (11%). The K-Means Clustering algorithm was applied to classify perceptions into three contribution levels-high, medium, and low-while an ANOVA test was used to examine statistical differences among clusters. Results: The clustering results revealed three contribution categories: C1 (low) with 186 data points, C2 (medium) with 195 data points, and C3 (high) with 216 data points. The high cluster demonstrated a strong positive contribution to regional income, local economy, and infrastructure development, although challenges remain in socio-cultural sustainability. The ANOVA test confirmed significant differences in economic and infrastructure variables, while destination attractiveness was relatively uniform across clusters. Novelty: This study provides a multidimensional mapping model that integrates socio-economic and participatory data with K-Means clustering to analyze cultural tourism contribution. It introduces a data visualization information system prototype for policymakers to evaluate tourism performance interactively. The research offers new insights into evidence-based strategies for sustainable and community-centered cultural tourism development.
A Hybrid YOLOv8-ResNet50 Architecture for Enhanced Cardiomegaly Prediction from Chest X-rays
Abstract. Objective: This study aims to develop a reliable deep learning architecture for predicting cardiomegaly by integrating the ResNet-50 backbone into the YOLOv8 object detection framework, overcoming the challenges of detecting subtle anatomical variations and low-contrast features often found in chest radiographs. Methods: This study used a publicly available chest X-ray dataset, with rigorous data annotation to establish ground truth for the heart and thoracic cavity regions. Preprocessing included resizing input images to 640×640 pixels, automatic orientation correction, and an 80:20 data split between training and testing. Real-time data augmentation was applied to the training set. The ResNet-YOLOv8 hybrid model was trained for 150 epochs with optimized hyperparameters (learning rate, momentum, weight decay, loss weight), and performance was evaluated using metrics such as mAP, precision, recall, and confusion matrix results. Results: The experimental results show that the proposed architecture achieves high accuracy in detecting cardiomegaly, with mAP50-95 of 0.7578, precision of 0.9955, recall of 0.9962, F1 score of 0.9959, and inference latency of only 4.5 ms/img. This model is more optimal than the standard YOLOv8 variant in both accuracy and computational efficiency. Innovation: The integration of ResNet-50 into YOLOv8 significantly improves feature extraction capabilities for chest X-ray images, enabling the recognition of fine anatomical details with high precision. This innovative hybrid approach advances automated cardiomegaly detection, offering potential for large-scale, real-time implementation in clinical settings and contributing to the development of advanced AI-powered diagnostic tools.
Cardiomegaly, Deep Learning, YOLOv8, ResNet-50, Chest X-ray, Medical Image Analysis, Hyperparameter Tuning.
A Systematic Review and Bibliometric Study of Climate Change Sentiment Analysis: Trends and Approaches
Purpose: This study aims to map research trends in sentiment analysis on the climate change topic from the beginning of 2020 to the middle of 2025 by utilizing a Systematic Literature Review (SLR) method, along with bibliometric analysis. Climate change represents a worldwide challenge that profoundly affects both the environment and human social interactions, making it essential to comprehend public perceptions of this issue thoroughly. The escalating use of social media is driving an increase in research related to sentiment analysis, which is utilized to gain insights into public opinions and emotions. Methods: Data was collected from six leading databases such as Scopus, ScienceDirect, Taylor and Francis, IEEE Xplore, Sage Journals, and ProQuest, resulting in 3,326 articles. After a screening process using the PRISMA 2020 framework, 42 articles were selected for further analysis. Result: The findings suggest that Twitter is the predominant platform for climate change sentiment analysis, referenced in 32 articles, while Sina Weibo is mentioned in nine articles, Reddit in two articles, and both Facebook and YouTube in one article each. Of the four approaches assessed, the leading approaches identified in this research are Machine Learning and Deep Learning. In the Machine Learning category, Naïve Bayes is the predominant approach, appearing in 18 articles, followed by Naïve Bayes, cited in 17 articles. Furthermore, Logistic Regression and Random Forest are each mentioned in 13 articles. In the field of Deep Learning methodologies, 10 articles used Convolutional Neural Networks (CNNs), nine articles featured Bi-LSTMs, six articles featured LSTMs, and 13 articles referenced Transformer-based models, particularly BERT. Furthermore, model validation primarily used cross-validation techniques, and the most referenced evaluation metrics were accuracy, recall, and F1-score in 33 articles and precision in 32 articles. Novelty: The novelty of this research lies in the time of information collection for research on climate change sentiment analysis, spanning 2020 to the middle of 2025. The latest research on a related issue was conducted from 2008 to 2022. Furthermore, this study provides insights into research trends and includes the distribution of articles by country, separating them into Single-Country Publications (SCPs) and Multi-Country Publications (MCPs). This research also presents information on social media platforms, classification approaches, and commonly employed validation and evaluation tools, which differentiate it from prior studies. This analysis is conducted on six leading databases, producing valuable findings for researchers and policymakers.
Contact, Fulfillment, and Privacy as Key Drivers of Mobile Commerce Success: A SOR-Extended M-S-QUAL Analysis
Purpose: M-commerce has become crucial for facilitating grocery shopping through delivery services, yet challenges like delayed orders and systems failures continue to hinder user satisfaction and loyalty. There are still lack of research that have investigated in depth both mobile apps service quality and paid attention to the perspective of user behavior in a structured way. This study addresses a gap by uniquely integrating the Mobile Service Quality (M-S-QUAL) and Stimulus Organism Response (SOR) to find out the key drivers for enhancing m-commerce grocery shopping services quality, and analyzing the influence of the m-commerce services quality factors as Stimulus in m-commerce apps to satisfaction felt by users as Organism, and their relationship with loyalty and E-WOM as user Response. Methods: Using a quantitative approach, 362 Indonesian m-commerce user responses from online survey were analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM). Outer model and inner model was carried out to test the significance between the construct and the strength of the model. Result: Results show that Contact ( =0.229), Fulfillment ( =0.192), and Privacy ( =0.166) are the most influential factors driving perceived m-commerce service quality that has strong predictive power (R2=0.804). These findings, which repositions these specific dimensions as primary stimulus within the SOR framework significantly impacts user satisfaction as organism and positively drive both loyalty and E-WOM as response. Novelty: This study provides valuable insights and a structured perspective to explain post-adoption user behavior in m-commerce delivery. The study offers novel academic insights and practical strategies for enhancing customer service, delivery reliability, and data protection through user-centered design with more attention to factors such as Contact, Fulfillment, and Privacy (Stimulus) to drive user satisfaction (Organism), loyalty and E-WOM (Response).
Strategy Improvement for IT Governance in Library Services Using an Adaptation of ITIL 4 and FitSM : A COBIT 2019 Evaluation
Purpose: Effective management of IT services in public libraries is critical to maintaining service continuity, optimizing resources, and ensuring user satisfaction. However, current practices face persistent challenges in workforce capability, operational consistency, and problem resolution. This study evaluates IT service governance and develops improvements across four objectives: APO07 (Managed Human Resources), DSS01 (Managed Operations), DSS02 (Managed Service Requests and Incidents), and DSS03 (Managed Problems). Methods/Study design/approach: An integrated approach was applied by combining COBIT 2019 and ITIL 4 with FitSM to fit the scale of public libraries. A capability assessment established the baseline, followed by a gap analysis to identify weaknesses. Adaptive procedures were then formulated by retaining the comprehensive guidance of ITIL 4 and integrating the lightweight, practical principles of FitSM, while excluding elements unsuited for small-scale organizations. Result/Findings: The assessment showed that all four objectives are at capability level 1 and have not reached the “fully achieved” benchmark. Gaps were identified in human resource development, operational standardization, and incident and problem handling. The proposed improvements introduce standardized processes to strengthen workforce capability, stabilize daily operations, and accelerate service request and problem resolution. Novelty/Originality/Value: This study offers a practical governance improvement model tailored for public libraries and similar small-scale public sector organizations. The integration of ITIL 4 and FitSM provides a structured yet simplified framework that supports process standardization and service quality enhancement, addressing the limitations of applying COBIT 2019 in isolation.
FitSM, ITIL 4, COBIT 2019, IT Governance
Which Features Matter Most? Evaluating Numerical and Textual Features for Helpfulness Classification in Imbalance Dataset using XGBoost
Purpose: This study aims to develop and realistically evaluate a reliable model for identifying helpful online reviews, particularly in the context of Indonesian-language texts, which are often informal and challenging. Methods: This study addresses several key challenges in predicting review helpfulness: the relative effectiveness of numerical features from metadata compared with traditional text representations (TF-IDF, FastText) on noisy data; the impact of severe class imbalance; and the limitations of standard validation compared with time-based validation. To address these challenges, we built an XGBoost model and evaluated various feature combinations. A hybrid approach combining SMOTE and scale_pos_weight was applied to handle class imbalance, and the best configuration was further assessed using time-based validation to better simulate real-world conditions. Result: The results show that the model based on numerical features consistently outperformed the text-based model, achieving a peak macro F1-score of 0.7214. Compared to the IndoBERT baseline (F1-score = 0.6400) and the RCNN FastText baseline (F1-score = 0.5317), this indicates that simpler feature-driven models can provide more reliable predictions under noisy review data. Time-based validation further revealed a performance decline of up to 8.06%, confirming the presence of concept drift and highlighting that standard validation tends to yield overly optimistic estimates. Novelty: The main contribution of this research lies in offering a robust methodology while demonstrating the superiority of metadata-based approaches in this context. By quantifying performance degradation through temporal validation, this study provides a more realistic benchmark for real-world applications and highlights the critical importance of regular model retraining.
Smart Monitoring Implementation for Hosting Services Using Zabbix and Autoencoder Models
Purpose: This study answers the increasing need for intelligent server monitoring systems capable of anomaly detection for computing infrastructure performance and reliability. Conventional static threshold-based monitoring systems, such as Zabbix, have limitations in identifying complex and dynamic abnormal patterns. To overcome this limitation, in this research study, the integration of an Autoencoder-based deep learning model with the Zabbix monitoring system is presented. Methods: An experimental approach was followed, wherein important server performance metrics like CPU utilization, memory utilization, and network activity were collected every 15 minutes for 40 days. The Autoencoder was trained on normal operational data to identify frequent behavioral patterns and produce reconstruction errors as anomaly scores. Deviations were alerted as anomalies when these scores exceeded an adaptive threshold. The system was deployed in a virtualized environment, with real-time notifications sent via Telegram for rapid incident response. Results: Experimental results show that the system can effectively detect different anomaly scores. Deviations were alerted as anomalies when these scores exceeded an adaptive threshold. The system was placed in a virtualized production setup, publishing real-time notifications via Telegram for prompt incident different anomaly types with high performance anomaly detection with MAE = 0.0084 and RMSE = 0.0198. Novelty: The primary contribution of this study is the development of hybrid real-time anomaly detection framework that integrates deep learning within open-source monitoring tools to be more efficient, decrease the dependence on manual configuration, and enable computing infrastructures to be more reliable and efficient.
Estimation Model of Nutritional Content Based on Broiler Feed Images Using Convolutional Neural Network and Random Forest
Purpose: This research aims to develop an intelligent model to estimate the nutritional content of broiler chicken feed based on feed images to assist farmers in selecting the best broiler feed and quickly verifying its quality to meet requirements. Methods: The methodology of this research includes literature study, data collection, data preprocessing, image classification, model evaluation, integration of CNN and random forest models, and estimation of nutritional content based on feed images. We collected 99 samples of broiler chicken feed from online stores in various regions of Indonesia, particularly Java. Next, we took pictures with a smartphone and analyzed the nutritional content using near-infrared spectroscopy. Preprocess the data by enhancing the dataset (color space and data augmentation). We use Convolutional Neural Network (CNN) for the classification of broiler feed images. The performance of the CNN model is evaluated using a confusion matrix. We integrate CNN and Random Forest Regressor (RFR) to estimate nutritional content from the features of broiler feed images. Result: The performance evaluation shows that the CNN (VGG-16) model is 0.9744% accurate and the RFR model has the highest R2 value of 0.8018. The benefits of this research include faster, more efficient, and automated feed quality measurement compared to traditional methods; maintaining feed quality standards; and avoiding health risks for livestock. Novelty: This research introduces an intelligent model to estimates the nutritional content of broiler feed images by integrating a CNN model with an RFR.
Performance of SARIMA, LSTM, GRU and Ensemble Methods for Forecasting Nickel Prices
Purpose: There are several forecasting methods, including SARIMA, LSTM, and GRU, which are often claimed to exhibit strong performance in capturing patterns in time series data. However, few studies have conducted direct comparisons among these methods. Therefore, it is necessary to conduct a performance evaluation using empirical data, particularly nickel prices data. This study also aims to improve forecasting performance by combining prediction outputs from deep learning-based models. Methods: This study utilized data on monthly global nickel prices from January 1990 to May 2025. The models developed include SARIMA, LSTM, GRU, and two ensemble approaches: Weighted Averaging and Bayesian Model Averaging (BMA). Model validation was conducted using walk-forward validation with a sliding window approach to evaluate each model’s generalization performance on out-of-sample validation data. The performance was evaluated using MAPE, RMSE, and MAE. Result: The BMA Ensemble approach shows the best performance in forecasting nickel prices, with a MAPE value of 5.39%, RMSE of 1897.84, and MAE of 1133.96. Prediction validation produces MAPE values below 10%, which indicates that the forecasting results are accurate. The ensemble BMA approach is able to produce more accurate and stable predictions compared to other models. Novelty: This study offers a novel approach combining LSTM and GRU through ensemble methods to forecast global nickel prices using monthly historical data from 1990 to 2025. In contrast to previous studies that relied on single models, the proposed method with the ensemble BMA approach demonstrates improved forecasting accuracy and stability.