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INDONESIA
MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer
Published by Universitas Bumigora
ISSN : 18584144     EISSN : 24769843     DOI : 10.30812/matrik
Core Subject : Science,
Computer Science & IT
MATRIK adalah salah satu Jurnal Ilmiah yang terdapat di Universitas Bumigora Mataram (eks STMIK Bumigora Mataram) yang dikelola dibawah Lembaga Penelitian dan Pengabadian kepada Masyarakat (LPPM). Jurnal ini bertujuan untuk memberikan wadah atau sarana publikasi bagi para dosen, peneliti dan praktisi baik di lingkungan internal maupun eksternal Universitas Bumigora Mataram. Jurnal MATRIK terbit 2 (dua) kali dalam 1 tahun pada periode Genap (Mei) dan Ganjil (Nopember).
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 454 Documents
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Eco Cycle Mobile: A Design Thinking Approach for Sustainable User Interface and User Experience Design Rini Indriati; Ning Inge Halimah Silva; Delilla Anandita; Sucipto
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 3 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i3.6042

Abstract

The Eco Cycle system is a mobile-based circular economy platform designed to increase public participation in sustainable waste management activities. The development of a mobile-based Eco Cycle application is urgent because it addresses usability and accessibility issues and supports sustainable behavior change through interactive, educational, and collaborative digital experiences. The objective of the research is to develop a User Interface and User Experience prototype for a circular- conomybased Eco Cycle mobile application to increase public participation in sustainable waste management, environmental engagement, and circular-economy activities. The research method employs a Design Thinking approach comprising Empathy, Definition, Ideation, Prototype, and Testing. The application prototype integrates several key features: ECOSell and ECOBuy for waste buying and selling services, ECOducation for environmental education, ECOmmunity for community collaboration, ECOPlanner for waste management planning, and ECOPoint as a participation-based system. The results of the study indicate that a usability evaluation using the User Experience Questionnaire and an open-ended questionnaire yielded positive user experiences across all UEQ dimensions. The highest scores for the attractiveness (1.87) and stimulation (1.80) dimensions were categorized as very good. The efficiencydimension scored 1.85, dependability 1.53, and novelty 1.26, categorized as good. Carity scored 1.71, categorized as above average. Additional usability analysis demonstrated high task completion rates and positive user perceptions of the ease of transactions, system resources, and environmental interaction features. The study concludes that the Design Thinking approach is effective for developing a user-centric mobile application that supports environmentally friendly behavior and increases community participation in circular-economy-based waste management.
Operational Weakness Mapping of Machine Learning–Based IntrusionDetection Systems under Realistic Deployment Scenarios Fathoni Mahardika; Ema Utami; Kusrini; Ferry Wahyu Wibowo
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 3 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i3.6147

Abstract

As machine learning-based intrusion detection systems increasingly support information security risk management, prior systematic literature review findings indicate that many studies still emphasize benchmark accuracy while paying limited attention to robustness, interpretability, and operational feasibility. This study aims to map the operational weaknesses of machine learning-based intrusion detection systems under realistic deployment stressors. A directed replication and scenario-based stresstesting approach was applied using four public intrusion detection datasets, namely CICIDS2017, CICIDS2018, UNSW-NB15, and RanSMAP. The data were obtained from public repositories, converted to binary labels, cleaned by removing identifiers and non-numeric attributes, imputed with median values, scaled with MinMax normalization, and split into training and testing subsets. Supervised models, including Random Forest and XGBoost, were compared with unsupervised baselines, including Isolation Forest, LOF/kNN-distance, and DBSCAN, across scenarios covering baseline benchmarking, class imbalance, telemetry degradation, drift, parameter sensitivity, and micro-batch inference. The results show that supervised models achieved near-perfect baseline performance but degraded sharply under minor Gaussian noise, with F1-score dropping to 0.16 for Random Forest and 0.41 for XGBoost. Unsupervised models showed limited detection capability and high sensitivity to parameters. Although micro-batch inference achieved high throughput, alert burden remained a practical concern. These findings demonstrate that benchmark accuracy alone is insufficient for deployment readiness and that IDS evaluation should include robustness, interpretability, and alert-management analysis.
Optimized BiLSTM and GRU Models Using QHBM for Forex Price Prediction Febrianto Alqodri; Triyanna Widiyaningtyas; Didik Dwi Prasetya
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 3 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i3.6226

Abstract

The foreign exchange market is highly volatile and complex, making accurate price prediction challenging. This study aims to develop an optimized deep learning framework for predicting daily closing prices of seven major currency pairs (AUDUSD, EURUSD, GBPUSD, USDCAD, USDCHF, USDCNY, and USDJPY) by integrating Bidirectional Long Short-Term Memory (BiLSTM) and GatedRecurrent Unit (GRU) models with optimization strategies. Historical data from the Federal Reserve Economic Data were evaluated using Fixed Date Split and Walk Forward Validation (WFV), where WFV consistently achieved better performance than the fixed date. To enhance model performance, hyperparameter optimization was conducted using the Queen Honey Bee Migration (QHBM) algorithm, a metaheuristic approach inspired by the migration behavior of queen bees, divided into two characteristics: high learning rate and low learning rate. The optimized models achieved performance improvements of approximately 10-70% in MAPE and RMSE compared to the baseline models, while maintaining high R2 values. The results indicate that optimal configurations are pair-specific, wheremost currency pairs perform best with a high learning rate and high unit settings, while AUDUSD achieves superior performance with a low learning rate and low unit configuration. This study contributes a novel integration of WFV and QHBM-based optimization. Adaptive deep learning models with proper validation significantly improve forecasting accuracy, robustness, and generalization forfinancial decision-making and algorithmic trading applications.
Retinal Blood Vessel Segmentation Using Attention V-Net with Scale-Aware Evaluation Hendra Wijaya; Erwin Erwin; Annisa Darmawahyuni; Sinta Bella Agustina
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 3 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i3.6373

Abstract

Retinal blood vessel segmentation remains a significant challenge, especially for small blood vessels with diameters less than 3 pixels in the DRIVE dataset and less than 4 pixels in the STARE dataset, owing to their low contrast and narrow structures. The aim of this study is to improve small retinal blood vessel segmentation performance through an Attention V-Net architecture that integrates attention-gating mechanisms into the skip connections of a V-Net backbone to strengthen the feature representation of thin vascular structures. The research method involves training and evaluating the proposed model on the DRIVE and STARE datasets using a scale-aware evaluation framework based on pixel-pitch calibration, classifying blood vessels into small and large categories, and measuring performance using accuracy, sensitivity, specificity, precision, Dice coefficient, and IoU. The results show that for small vessel segmentation, the method achieves sensitivities of 0.7033 and 0.6984, Dice scores of 0.4720 and 0.4699, and IoUs of 0.3096 and 0.3079 on the DRIVE and STARE datasets, respectively. For large vessels, sensitivities of 0.9219 and 0.8851, Dice scores of 0.8031 and 0.8179, and IoUs of 0.6719 and 0.6933 are obtained. Global evaluation yields accuracies of 0.9475 and 0.9602, sensitivities of 0.8727 and 0.8719, and Dice scores of 0.8080 and 0.8268. In conclusion, Attention V-Net demonstrates consistent segmentation performance across vessel scales, and the scale-aware evaluation framework effectively reveals the performance gap between small and large vessel segmentation, providing a more clinically relevant assessment than conventional global evaluation for early diagnosis of retinal diseases.
Designing a Cloud-Based Smart-EduVerse Management System forPrescriptive Quality Governance in Higher Education: AMixed-Methods Study Rochmawati Rochmawati; Anik Nur Handayani; Tran Thi Hao; Soubin Sisavath
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 3 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i3.6647

Abstract

The purpose of this study is to develop a Cloud-Based Smart-EduVerse Management System (S-EMS) architecture as a digital quality mapping framework designed to strengthen Sustainable Competitive Advantage (SCA) in Indonesian State Universities with Legal Entity (PTNBH) status amid the VUCA era. This study employed a mixed-methods approach using a concurrent embedded design with qualitative dominance. The research involved 22 PTNBHs, with qualitative data collected through in-depth interviews, observations, and document analysis, while quantitative data were obtained through questionnaires. Both datasets were integrated during the interpretation stage using constant comparative analysis and descriptive statistics to formulate the system architecture and governance requirements. The results indicate that S-EMS consists of three cloud-based functional layers: (1) a prescriptive datamapping layer integrating institutional quality indicators, (2) a cloud-hosted knowledge asset management module for organizing human and intellectual capital, and (3) an adaptive governance dashboard supporting real-time cross-campus decision-making. Quantitative findings show that 19 of the 22 universities (86.4%) have integrated digital infrastructure, while strategic collaboration and adaptive quality assurance practices were implemented across nearly all participating institutions. These findings demonstrate that the proposed architecture reduces data fragmentation, improves interoperability, and supports evidence-based governance, facilitating a transition from compliance-based to impact-based institutional management. This study contributes a validated cloud-based Smart-EduVerse Management System (S-EMS) framework integrating digital quality mapping, knowledge asset management,and prescriptive governance to strengthen sustainable competitive advantage in higher education.
Financial Crisis EarlyWarning System Using Time-Varying Dependence Structures and Hybrid Backpropagation Neural Network with Long Short-Term Memory Imelda Saluza; Lastri Widya Astuti; Herlambang Saputra; Alvi Syahrini Utami; Devni Prima Sari
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 3 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i3.5686

Abstract

Financial crises have a significant impact on economic stability, making early detection crucial for policymakers and financial institutions. This study aims to develop a Financial Crisis EarlyWarning System by integrating time-varying dependency structures with machine learning techniques for proactive systemic risk detection. This study uses monthly macro-financial indicators from January 2020 to May 2025. The methodology includes rolling copula estimation to capture dynamic tail dependencies, feature engineering to transform copula parameters into systemic risk indicators, and predictive modeling using Backpropagation Neural Networks and Long Short-Term Memory. To ensure robust validation, the dataset is split chronologically (70% training, 30% out-of-sample testing). The results show that Frank Copula optimally captures nonlinear tail dependencies, particularly between stock indices and exchange rates. The predictive model achieves high out-of-sample detection accuracy (Backpropagation Neural Network: 91.2%; Long Short-Term Memory: 92.5%). Backpropagation Neural Networks successfully identified acute short-term shocks during validated crisis episodes, while Long Short- Term Memory detected long-term structural vulnerabilities and projected severe systemic stress in forward-looking forecasts. Conclusion: Integrating copula-based feature engineering with a sequence learning architecture significantly improves early warning accuracy. This integrated framework provides policymakers, regulators, and market participants with a powerful and proactive tool for financial stability surveillance and crisis mitigation.
Evaluating SHAP and LIME for Trustworthy Automatic Assessment of Concept-Map Proposit Mega Satya Ciptaningrum; Didik Dwi Prasetya; Azlan Mohd Zain
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 3 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i3.5972

Abstract

            Concept maps are effective learning tools for improving students’ deep understanding and critical thinking skills. However, manual assessment of concept map propositions is time-consuming, subjective, and difficult to perform consistently. Although transformer-based automated scoring approaches have shown strong performance, most still operate as black-box models, limiting transparency in educational contexts. This study proposes an ordinal DeBERTa-based automatic scoring model integrated with Explainable Artificial Intelligence for evaluating concept map propositions in database learning. A dataset of propositions scored on an ordinal scale of 0–3 by human raters was used as ground truth. DeBERTa, MiniDeBERTa, and DeBERTaV3 were trained using the CORAL ordinal classification framework. Model performance was evaluated using RMSE, Macro F1-score, and Quadratic Weighted Kappa. SHapley Additive Explanations and Local Interpretable Model-agnostic Explanations were applied to test data for token-level explanations, while faithfulness was evaluated using deletion–insertion AUC, comprehensiveness, and sufficiency. Experimental results showed that the ordinal DeBERTa model achieved an RMSE of 0.523, a Macro F1-score of 0.574, and a QWK of 0.814, indicating low prediction error and strong agreement with human raters. SHAP produced better comprehensiveness (0.228) and lower sufficiency (0.241) than LIME comprehensiveness (-0.145) and sufficiency (0.726), indicating more faithful and semantically consistent explanations. The findings also show that model predictions are influenced not only by individual tokens but also by contextual and relational proposition structures. However, this study is limited to token-level and local contextual explanations. Future work may explore semantically aware perturbations and global explanation analysis to better capture relational semantics and contextual dependencies.  
Logistic Regression-Based Classification of Food Security Vulnerability in East Java Districts Ahmadi Yuli Ananta; Rudy Ariyanto; Rakhmat Arianto; Imam Fahrur Rozi
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 3 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i3.6002

Abstract

The problem addressed in this study is the limited capacity of district-level food-security monitoring in Indonesia to anticipate deterioration in the following year, particularly when prediction relies on short longitudinal histories and must account for repeated observations, class imbalance, temporal change, and regional variation. The method involved constructing 2,056 temporally ordered prediction instances from Food Security Index data covering 514 Indonesian districts and cities during 2019–2024, representing six regional indicators through their current values and annual changes, and evaluating Logistic Regression, Random Forest, and eXtreme Gradient Boosting through district-grouped crossvalidation, alternative imbalance treatments, and an untouched 2023–2024 out-of-time test; temporal ablation, cluster-robust Logistic Regression, SHapley Additive exPlanations, sensitivity analysis, anddirect assessment in East Java were subsequently conducted. The result showed that Logistic Regression achieved the strongest screening-oriented performance, with a recall of 0.690, an F1-score of 0.450, a Receiver Operating Characteristic Area Under the Curve of 0.615, and a Precision–Recall Area Under the Curve of 0.404, while annual-change features improved F1-score and Precision–Recall Area Under the Curve across all three classifiers. However, performance declined in East Java, where two of four deterioration cases were detected, and 22 false-positive warnings were generated. The implication is that parsimonious temporal features provide useful predictive information beyond current regional conditions, although the model is more appropriate for screening and prioritization than for autonomous administrative classification, while operational use requires local calibration, longitudinal data auditing, threshold assessment, and validation across additional provinces and later annual transitions.
Enhanced Tsunami Vulnerability Assessment via Hybrid Random Forest and XGBoost Stacking Ensemble Antonius Budi Istiadi; Sri Yulianto Joko Prasetyo; Wiwin Sulistyo
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 3 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i3.6243

Abstract

Previous studies in coastal Java have used Random Forest, K-Nearest Neighbors, and Least Absolute Shrinkage and Selection Operator regression for land cover dynamics and statistical analyses, with recent 2024 findings showing that Extreme Gradient Boosting outperforms RF in classification accuracy. However, there is a gap in the model’s ability to simultaneously integrate all built-up land indices using an ensemble stacking algorithm. This study aims to develop a new classification framework based on dynamic stacking ensembles to improve tsunami vulnerability assessment accuracy in the coastal area of Jember Regency, East Java. This research method develops a hybrid RF-XGBoost model by integrating DEM topographic parameters and the built-up index from Landsat 8 imagery (2023–2025), beginning with K-Means clustering to identify homogeneous spatial patterns prior to classification. The novelty of this research lies in the development of a dynamic stacking ensemble method that integrates algorithms (RF and XGBoost) as base learners with multinomial logistic regression as a meta-learner. Unlike conventional single-model methods, this methodology uniquely synergizes built-up land indices combined with integrated topographic analysis to extract physical vulnerability parameters more precisely. This result demonstrated increased prediction robustness, with a high accuracy of 0.976 and an AUC-ROC of 0.5493, which significantly exceeds the class discrimination capabilities of the Random Forest or Extreme Gradient Boosting models alone. The contribution of this research is to propose an ensemble model that enhances predictive resilience and provides a reliable decision-support framework for tsunami risk mitigation planning.
Text–emoji-based tourist perception analysis for sustainable tourismdevelopment on Gili Iyang using SVM and LDA Safinatus Zahroh; Enny Itje Sela; Bambang Moertono Setiawan; Luther A Latumakulita
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 3 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i3.6417

Abstract

Gili Iyang Island is widely recognized as an “Oxygen Island” with strong potential for sustainable tourism development. However, challenges related to accessibility, facilities, and service quality continue to affect tourist experiences and may hinder destination development. Previous tourism sentiment studies have mainly relied on textual data, while emotional cues conveyed through emojis have received limited attention despite their ability to enrich sentiment interpretation. In addition, sentiment classification and topic extraction are commonly performed separately, restricting a comprehensive understanding of tourist perceptions. This study aims to analyze tourist perceptions of Gili Iyang Island by integrating sentiment classification and topic extraction using text and emoji data from Instagram comments. A text mining framework was applied, including data preprocessing, rule-based sentiment labeling, TF-IDF and n-gram feature extraction, SVM-based sentiment classification, and LDA for topic extraction. The dataset consisted of 425 Indonesian-language comments collected between January and December 2025. The SVM model achieved a validation accuracy of 81.18%, indicating satisfactory performance in classifying text- and emoji-based sentiments. Topic modeling identified five dominant discussion themes: environmental conditions, tourist attractions, accessibility, facilities, and social interactions. Mapping these themes to sustainable tourism dimensions revealed that environmental and social aspects attracted greater public attention than economic aspects. Positive sentiment was primarily associated with environmental quality and tourist attractions, while accessibilityand facilities generated relatively more negative perceptions. These findings demonstrate that integrating text–emoji-based sentiment analysis with topic extraction provides a more comprehensive understanding of tourist perceptions and offers valuable data-driven insights to support sustainable tourism planning, destination management, and policy development.

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