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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 6 Documents
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Search results for , issue "Vol. 25 No. 2 (2026)" : 6 Documents clear
Dynamic Capability Drives Digital Transformation: SEM Evidence onSustained Competitive Advantage in Emerging Markets Supriyadi; Firmansyah
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 2 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i2.6128

Abstract

This study aims to examine the influence of dynamic capabilities on sustained competitive advantage and to investigate the mediating role of digital transformation in firms in emerging markets. Based on Dynamic Capabilities Theory, digital transformation is conceptualized as a process driven by strategic capabilities, rather than simply technology adoption. Data were collected from 247 top- and middlelevel managers from medium- and large-sized firms in Indonesia. Analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the proposed relationships. The results show that dynamic capabilities have a significant positive influence on digital transformation ( = 0.61, p ¡ 0.001) and sustained competitive advantage ( = 0.29, p ¡ 0.01). Furthermore, digital transformation significantly enhances sustained competitive advantage ( = 0.54, p ¡ 0.001). Mediation analysisconfirmed that digital transformation partially mediates the relationship between dynamic capabilities and sustained competitive advantage (indirect = 0.33, p ¡ 0.001), indicating that digital transformation acts as a strategic transmission mechanism. This model explains 37% of the variance in digital transformation and 58% of the variance in sustained competitive advantage. In conclusion, this study shows that dynamic capabilities are important drivers of sustained competitive advantage, both directly and indirectly through digital transformation. These findings highlight the importance of aligning digital initiatives with organizational capabilities to achieve long-term competitive performance in a dynamic and uncertain environment.
Addressing Class Imbalance in Android Backdoor Malware DetectionUsing Ensemble Models Megantara, Rama Aria; Pergiwati, Dewi; Alzami, Farrikh; Pramunendar, Ricardus Anggi; Prabowo, Dwi Puji; Naufal, Muhammad; Brilianto, Rivaldo Mersis
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 2 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v15i2.6198

Abstract

Backdoor malware represents one of the most critical threats in the Android ecosystem due to its capability to enable covert remote access, escalate privileges, and exfiltrate sensitive data without user awareness. Although the CCCS-CIC-AndMal-2020 dataset is publicly available, prior studies have not specifically formulated Backdoor detection as a binary classification problem under extreme class imbalance, nor systematically evaluated the impact of oversampling and cost-sensitive weighting using imbalance-aware performance metrics. This study proposes a comprehensive detection pipeline that integrates ensemble learning, class imbalance handling strategies, and explainability-based analysis to extract behavioral signatures of Backdoor malware. A two-stage feature selection process is employed to reduce the original 9,502-dimensional feature space to 500 informative features. Subsequently, five classification algorithms are evaluated under three imbalance-handling scenarios using a composite ranking criterion based on F1-score, Area Under the Receiver Operating Characteristic Curve (AUC), Geometric Mean (G-Mean), and Matthews Correlation Coefficient (MCC). The experimental results demonstrate that the Random Forest model combined with Synthetic Minority Oversampling Technique (SMOTE) achieves the best performance, with an F1-score of 0.9043, AUC of 0.9909, G-Mean of 0.9422, and MCC of 0.8948. Furthermore, SHAP analysis identifies 39 Android permissions related to account access, covert communication, and privilege escalation as key behavioral signatures, with the permissions feature group contributing 2.31 times higher discriminative importance than nonpermission features. These findings indicate that interpretable ensemble learning not only improves detection performance but also provides actionable insights for static malware analysis.
A MOORA-Based Decision Support Framework for Ranking Healthcare Service Performance Using Patient Perception Data Samuel Manurung; Indra M Sarkis; Mufria J Purba; Gortap Lumbantoruan
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 2 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i2.6210

Abstract

Healthcare service performance evaluation has become an essential aspect in improving service quality and supporting evidence-based decision-making in healthcare institutions. Increasing patient expectations require healthcare providers to assess and enhance their service performance across multiple dimensions continuously. Therefore, a systematic, objective evaluation approach is needed to measure service quality effectively. This study aims to evaluate healthcare service performance using a multicriteriadecision-making approach based on patient perception data. This research employs a quantitative method, collecting data through structured questionnaires administered to 152 respondents. The instrument consists of 25 indicators derived from five service quality dimensions: tangibles, reliability, responsiveness, assurance, and empathy. Data validity and reliability were tested using Pearson correlation and Cronbach’s Alpha, confirming that the instrument is valid and reliable. Furthermore, data analysis was conducted using the Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) method, including the construction of the decision matrix, normalization, optimization, and ranking. The results indicate that the reliability dimension achieved the highest preference value (A1 = 0.059), followed by empathy (A4) and tangibles (A5) (0.057), while responsiveness obtained the lowestvalue (A2 = 0.052). These findings demonstrate that reliability is the strongest aspect of healthcare service performance, whereas responsiveness requires priority improvement. This study contributes by providing an objective, systematic evaluation framework that integrates patient-perception-based service quality dimensions with the MOORA method to generate measurable performance rankings. The proposed framework offers a practical decision-support tool for healthcare managers in determiningpriority strategies for service quality improvement.
Measuring Instagram Content Effectiveness in Digital Marketing usingthe EPIC Model and Direct Rating Method I Gede Edy Artana Artana; Evi Triandini Evi; Dandy Pramana Hostiadi Dandy
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 2 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i2.4843

Abstract

Instagram has become known as one of the leading platforms in digital marketing, making it essential to systematically evaluate the effectiveness of the content presented to ensure successful communication strategies. This study aims to measure the effectiveness of Instagram content produced by Kayana Creative using the EPIC Model and the Direct Rating Method, two complementary evaluative approaches that assess content quality and audience reception. The research involved 100 respondents who follow the Kayana Creative Instagram account. Data collected using a Likert-scale questionnaire and analyzed quantitatively to assess the four EPIC dimensions. Empathy, Persuasion, Impact, and Communication, as well as the overall evaluation through the Direct Rating Method. The results indicate that Instagram content is effective, with an average EPIC score of 4.04. The Communication dimension scored highest, indicating that the audience clearly understood the content’s messages. Using the Direct Rating Method, respondents provided an overall score of 80.48, further confirming that the content effectively captures attention and delivers a positive user experience. This study provides practical contributions for developing content that is more relevant, communicative, and engaging, and theoretical contributions by reinforcing the use of the EPIC Model and the Direct Rating Method as complementary evaluative tools for assessing content effectiveness on social media within the digital marketing context.
Performance Comparison of Decision Tree, KNN, and Naive Bayes for Air Quality Classification Yan Yang Thanri; Juli Iriani Iriani; Lili Tanti Tanti; Luthfi Zaidi Zaidi
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 2 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i2.5121

Abstract

Air quality degradation has become a critical environmental and public health issue, necessitating accurateand reliable classification models to support effective monitoring systems. This study aims toconduct a comparative analysis of four machine learning algorithms-Decision Tree, k-Nearest Neighbor (kNN), Naive Bayes, and Stochastic Gradient Descent (SGD)-for classifying air quality using environmental parameters, including particulate matter ≤ 2.5 μm (PM2.5), carbon monoxide (CO), temperature, humidity, nitrogen dioxide (NO2), and sulfur dioxide (SO2). The methodology employssupervised learning, where each model is trained and evaluated using classification accuracy, area under the receiver operating characteristic curve (AUC), F1-Score, precision, recall, and Matthews Correlation Coefficient (MCC), supported by ROC curve and confusion matrix analyses. The results show that the Decision Tree algorithm achieves the best overall performance, attaining a classification accuracy of 93.8% with a balanced precision, recall, and F1-Score, indicating strong and consistent predictive capability. The kNN and Naive Bayes models record the highest AUC values (0.980 and 0.982, respectively), demonstrating excellent class separability, although their accuracy and F1-Score are lower than those of the Decision Tree. In addition, the SGD model, implemented with a modified Huber loss function and L2 regularization, provides interpretable feature-weight analysis, identifyingPM2.5 and CO as dominant indicators of the Hazardous air quality class, while temperature and humidity significantly influence the Fair and Good classes. Based on the comprehensive evaluation, the Decision Tree algorithm is recommended as the most reliable model for accurate air quality classification, whereas the SGD model is particularly suitable for feature contribution analysis to enhance interpretability. These findings offer practical insights for selecting appropriate machine learning models in air quality monitoring and decision-support systems.
Enhancing Customer Complaint Management through AI-Based Business Process Improvement Zain Ammar Falih; Deki Satria; Vandha Pradwiyasma Widartha Yasma
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 2 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i2.5825

Abstract

The rapid advancement of digital technology has transformed business process management, particularly in the telecommunications sector, where manual customer complaint handling often causes inefficiencies such as delays, ticket backlog, and human error. The purpose of this study is to investigate how artificial intelligence can enhance the efficiency and effectiveness of customer complaint handling by redesigning workflows through process automation. This study employs a qualitative descriptive approach combined with business process analysis, with data collected through observations, in-depth interviews with 32 participants, and document reviews. NVivo software was used to code interview data, while Bizagi Modeler was used to visualize both the existing and proposed business processes. The results indicate several bottlenecks in the existing complaint handling process, including manual first call resolution activities, inefficient complaint classification, redundant coordination between units, and low customer confirmation rates. To address these issues, the proposed improved process introduces artificial intelligence–based solutions, such as automated first-call resolution, ticket classification using natural language processing, intelligent ticket routing, and automated customer confirmation systems. These improvements are projected to reduce complaint-handling time by 25–40 percent, minimize service-level agreement violations, and optimize resource allocation. This study concludes that integrating artificial intelligence into customer complaint handling processes significantly improves efficiency, accuracy, and service quality, while also supporting organizational digital transformation. Furthermore, the findings make theoretical contributions to the business process management literature and provide practical insights for implementing artificial intelligence–driven automation in large-scale telecommunications environments.

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