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Hairani
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matrik@universitasbumigora.ac.id
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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 17 Documents
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Search results for , issue "Vol. 25 No. 1 (2025)" : 17 Documents clear
Comparative Analysis of TF-IDF and Modern Text Embedding for the Classification of Islamic Ideologies on Indonesian Twitter Siti Ummi Masruroh; ⁠Cong Dai Nguyen; Doni Febrianus
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 1 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i1.5600

Abstract

The ideological polarization that has emerged on social media platforms like Twitter, particularly regarding discussions on Islamic ideologies in Indonesia, has led to the rapid spread of da’wah. However, it has also created challenges in effectively classifying tweets into distinct Islamic ideologies, such as Liberal Islam and Moderate Islam (Wasathiyyah). The lack of effective methods for accuratelyclassifying such nuanced content presents a significant challenge. To address this problem, the research aimed to develop and evaluate a machine learning model that compares the effectiveness of traditional word vectorization methods (TF-IDF) with modern text embedding models (Nomic Embed v2). The study utilized the Knowledge Discovery in Databases (KDD) framework, scraped relevant data using the Twitter API, and annotated the dataset based on ideology. Preprocessing techniques such as case folding, stopword removal, and symbol removal were applied to the dataset. Classification was carried out using an SVM model, and cross-validation was employed to assess the model’s accuracy. The findings indicate that the embedding model improved the accuracy by providing nuanced semantic context for the tweets, suggesting that modern semantic models can outperform traditional methods inclassifying complex, context-dependent texts.
Cyber Threat Detection and Automated Response Using Wazuh and Telegram API Yuri Ariyanto; Yan Watequlis Syaifudin; M. Hasyim Ratsanjani; Ali Ridho Muladawila; Triana Fatmawati; Pramana Yoga Saputra; Chandrasena Setiadi
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 1 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i1.5610

Abstract

Cyber threats are becoming more widespread, notably those that use SSH to brute-force their way in or engage in Distributed Denial of Service attacks. These attacks can make networked systems very hard to reach, keep their data safe, and protect their privacy, especially for small and medium-sized organizations that can’t afford pricey professional security solutions. This research aims to develop an automated, cost-effective, and scalable cyber threat detection and response system for small and medium-sized organizations unable to afford commercial-grade security solutions. The methodology follows the structured Prepare, Plan, Design, Implement, Operate, Optimize lifecycle, leveraging open-source technologies, primarily the Wazuh Security Information and Event Management platform, augmented with custom detection rules and a Random Forest-based classification module to distinguish Normal, Brute Force, and Distributed Denial of Service traffic patterns. Experimental results demonstrate a Mean Time to Detect of 4.7 seconds for Brute Force and 7.3 seconds for Distributed Denial of Service, with a Mean Time to Respond of 8.2 seconds and under 10 seconds, respectively. The system achieved 98.4% detection accuracy and a 1.5% false positive rate across 100 controlled tests using THC Hydra and slowhttptest. Integration of Wazuh dashboard analytics with real-time Telegram alerts enhances situational awareness and enables prompt, automated incident response, validating open-source frameworks as viable defenses in resource-constrained environments.
Developing the Adaptive Digital IT Governance Framework for Next-Generation IT Governance Bambang Saras Yulistiawan; Rifka Widyastuti; RR Octanty Mulianingtyas; Galih Prakoso Rizky A; Hengki Tamando Sihotang
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 1 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i1.5628

Abstract

The increasing complexity of digital transformation requires an adaptive, measurable, and contextaware IT governance model. However, existing frameworks such as COBIT, ITIL, TOGAF, and ISO/IEC 38500 tend to be partial and prescriptive, failing to address strategic, operational, and innovative needs holistically. This study proposes the Adaptive Digital IT Governance Framework, anovel governance model synthesized from eleven leading IT frameworks and structured into three integrated domains: Govern, Manage, and Adapt. Employing a Design Science Research methodology, the model was developed through a systematic framework analysis, conceptual domain formulation, iterative implementation mapping, and the design of a maturity assessment instrument. The results demonstrate that the Adaptive Digital IT Governance Framework offers a modular, scalable, and value-driven governance solution suited for diverse organizational contexts. Theoretical contributions include extending the IT governance paradigm by integrating strategic alignment, agile governance, and digital sustainability. Practically, the framework provides actionable guidance for designing, assessing, and enhancing digital governance systems across sectors. Unlike previous cross-framework synthesis efforts, the Adaptive Digital IT Governance Framework explicitly introduces the Adapt domain, operationalizing governance agility, innovation capability, and sustainability measurement. This makes the Adaptive Digital IT Governance Framework the first modular, maturity-oriented framework that simultaneously integrates strategy, operations, and adaptability, positioning it as a next-generationmodel to support organizational resilience and sustainable digital transformation.
Improving Detection Accuracy of Brute-Force Attacks on MariaDB Using Standard Isolation Forest: A Comparative Analysis with RotatedVariant Hartono; Khusnul Khotimah; Rokin Maharjan
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 1 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i1.5817

Abstract

Brute-force attacks remain among the most prevalent and persistent cybersecurity threats to database systems, causing unauthorized access, data leakage, and service disruptions. Conventional thresholdbased detection methods often struggle to adapt to evolving and dynamic attack patterns, necessitating more robust anomaly detection approaches. This study aims to develop, evaluate, and compare two unsupervised machine learning algorithms—Standard Isolation Forest (IF) and Rotated Isolation Forest (RIF)—for detecting brute-force attacks targeting databases such as MariaDB. A large-scale raw access log dataset containing millions of entries was pre-processed through data cleaning, normalization, and feature extraction. Behavioural features were engineered for IP-path pairs, including login-attempt frequency, request intervals, and rapid-attempt ratios. The dataset consisted of 1,831,989 benign and 5,126,052 brute-force entries. The Standard IF model was trained using benign data (n estimators = 175, contamination = 0.1, max samples = ’auto’) and evaluated on mixed data, achieving Recall 99.94%, Precision 99.29%, F1-Score 99.61%, AUC 0.9495, and Accuracy 99.28%, with TP = 5,123,224 and FN = 2,828. The RIF model, using Gaussian Random Projection (n components = 5), yielded slightly lower metrics: Recall 99.44%, F1-Score 99.36%, and Accuracy 98.81%. The findings indicate that Standard Isolation Forest provides higher detection accuracy and reliability in identifying brute-force anomalies within large-scale log data. Despite the theoretical advantage of feature rotation in handling complex anomalies, the Standard IF demonstrates superior practical performance and efficiency. Overall, the study confirms the method’s strong potential for integration into automated and real-time cybersecurity monitoring systems.
Evaluating Lecturer Satisfaction on Academic Information System Using Usability and EUCS at Bandung University of Technology Sela Octaviani; Evi Triandini; Dandy Pramana Hostiadi
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 1 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i1.4844

Abstract

Academic Information Systems play a crucial role in supporting academic data management, administrative processes, and informed decision-making within higher education institutions. Despite their widespread adoption, the extent to which these systems effectively meet the needs and expectations of lecturers, their primary users, remains insufficiently explored. Understanding user satisfaction is critical, as it directly influences system acceptance, continued usage, and overall institutional performance. This study aims to evaluate lecturer satisfaction with Academic Information Systems at Bandung University of Technology by integrating two complementary evaluation methods, the System Usability Scale and End User Computing Satisfaction. The integration of these methods enables a more holistic assessment by combining usability measurements with multidimensional user satisfaction indicators. The findings reveal an exceptionally high SUS score of 99.94, classified as Best Imaginable, indicating that lecturers perceive the system as highly usable, intuitive, and supportive of their academic tasks. The EUCS analysis identifies Accuracy, Format, and Ease of Use as significant factors influencing lecturer satisfaction. These variables demonstrate the importance of accurate and reliable information, a well-structured interface, and system features that facilitate efficient task completion. The combined results highlight specific areas requiring strategic improvement, particularly in maintaining data accuracy, enhancing interface design consistency, and strengthening overall usability to accommodate users’ academic workflows. Theoretically, it demonstrates the added methodological robustness gained from combining SUS and EUCS in evaluating academic information systems, thereby ensuring more substantial alignment with user
Middleware Development for Heterogeneous Databases on Multi-Architecture Systems Small Medium Enterprise Christopel H. Simanjuntak; Musfiah Musfiah; Muhammad Bahit; Cristovani W. Lohonauman; Stenly B. Dodie; Khamla Nonalinsavath
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 1 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i1.5545

Abstract

The fisheries industry is highly complex, requiring information technology to support data recording, information management, and sales forecasting. At Kampunglawo, a sales information system has been developed to manage transactions and track shipments of fishery products. However, the sales forecasting systems were developed separately, with different architectures and underlying data structures, necessitating data duplication and restructuring and resulting in inefficiencies. The objective of this research is to develop an Application Programming Interface (API) that connects the two systems, enabling data sharing without redundancy. The methods used were literature review, system requirements analysis, design, implementation, functional and performance testing, and evaluation. The results of this research show that the developed API can synchronize data between the sales and forecasting systems with high efficiency. Testing showed that for 1, 10, and 50 synchronized data sets, the server response ratio was 1:1,057:1,869, with an increase in processing time of only 41.7% for the largest data volume. The conclusion of this research shows that using APIs can reduce processing time and eliminate the need for data restructuring, thereby increasing the efficiency of the company’sinformation system integration.
Comparing K-Means, GMM and BIRCH for Student Academic Performance Data: Evaluation on Two Public Datasets Ricky Aurelius Nurtanto Diaz; Ni Luh Gede Pivin Suwirmayanti; Emy Setyaningsih; I Wayan Budi Sentana
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 1 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i1.5843

Abstract

Academic data contains complex patterns that require appropriate clustering approaches to support informed educational decision-making. However, comparative studies that regularly evaluate various clustering methods for student academic performance, using diverse public data sets and consistent evaluation criteria, are limited. This study aims to identify the most effective clustering algorithm for modeling student academic performance by comparing three techniques: K-Means, GMM, and BIRCH, on two publicly available datasets: the Student Performance Metrics (SPM) Dataset with 16 features and 493 instances, and the Higher Education Students Performance Evaluation (HESPE) dataset with 32 features and 145 instances. Algorithm evaluation was performed using Sum of Squared Errors (SSE), Davies–Bouldin Index (DBI), Silhouette Score, and computational time. The results show that K-Means consistently provides superior clustering quality on both datasets, outperforming the other algorithms in four evaluation criteria, while BIRCH demonstrates superiority in two metrics and achieves the shortest computational time. These findings highlight that clustering effectiveness is strongly influenced by algorithm characteristics and data structure, with K-Means being more suitable for accuracy-oriented clustering and BIRCH for time-critical applications. Overall, this study contributes to educational data mining by providing comparative evidence on algorithm performance and demonstrating how methodological choices influence the interpretation of student performance patterns. In practice, institutions can choose clustering methods that best suit their needs, such as K-Means for precise academic profiling or BIRCH for rapid, large scale analysis, to help students graduate successfully.

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