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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 437 Documents
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A MOORA-Based Decision Support Framework for Ranking Healthcare Service Performance Using Patient Perception Data Manurung, Samuel; 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; Evi Triandini; Dandy Pramana Hostiadi
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 Thanri, Yan Yang; Iriani, Juli Iriani; Tanti, Lili Tanti; Zaidi, Luthfi 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 Falih, Zain Ammar; Satria, Deki; Yasma, Vandha Pradwiyasma Widartha
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.
Cosine Similarity as a Distance Metric for Javanese Script Image Recognition Classification Priyambodo, Aji Priyambodo; Prihati, Prihati; Danang, Danang; Farhan bin Mohamed
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.v25i2.4123

Abstract

Javanese character (Hanacaraka) recognition presents significant challenges due to the intricate patterns and variations in character features. Addressing these issues is crucial for digitizing cultural heritage and supporting educational applications. This study aims to evaluate the effectiveness of cosine similarity as a distance metric for classifying Javanese characters, comparing its performance against traditional Euclidean and Manhattan distance metrics. The research used a feature-extraction technique based on the histogram of oriented gradients and evaluated cosine similarity across different classification models. Model performance was assessed using precision, recall, F1-score, and accuracy metrics. The results showed that cosine similarity, when combined with a support vector machine, achieved an accuracy of 99.84%, significantly outperforming other distance metrics. When applied to another classification model, cosine similarity improved accuracy to 90%, demonstrating its robustness in handling complex patterns. Parameter optimization was performed using a grid-based search, and model reliability was assessed through cross-validation. Compared with previous studies that primarily relied on deep learning, this research offers an alternative method that balances efficiency and accuracy while maintaining high interpretability. The findings establish a new benchmark for Javanese character recognition and highlight the potential of cosine similarity in broader applications. Future research can expand this study by incorporating more diverse feature extraction techniques, larger datasets, and hybrid approaches to further enhance recognition performance.
Teacher-Assessed Mi-Robot Training Improves Linguistic and Kinesthetic Stimulation for Children with Special Needs Taryudi, Taryudi; Djatmiko, Wisnu; Kusuma, Murti; Lindayani, Linlin
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.v25i2.5051

Abstract

The increasing need for effective learning support for children with special needs highlights the urgency of integrating assistive technologies that enhance linguistic and kinesthetic stimulation and support teachers in instructional delivery. Conventional methods often struggle to provide consistent and engaging stimulation, particularly in digital or distance learning contexts. The objective of this study was to evaluate teachers’ acceptance and perceived usefulness of the Mi-Robot for linguistic and kinesthetic stimulation. This research method is a descriptive mixed-methods study involving 20 special education teachers from 5 elementary schools in Bandung, Indonesia. Teachers received structured training in the use of Mi-Robot. Data were collected using the Mi-Robot Acceptance Scale based on the Technology Acceptance Model and an open-ended usability evaluation form. The results indicate that Mi-Robot aligns with the school curriculum and demonstrates high perceived usefulness, ease of use, positive attitudes, and strong behavioral intentions among teachers. Qualitative findings indicate that Mi-Robot effectively supports linguistic and kinesthetic stimulation through its content, functionality, and cost-effectiveness. In conclusion, Mi-Robot demonstrates strong potential as an assistive educational technology for special education in both classroom and distance-learning settings.
Evaluating the Basa-Samawa Platform through WebQual 4.0 and Importance–Performance Analysis Shinta Esabella; Susilawati, Tri; Andriani, Titi; Hidayatullah, Muhammad; Gunawan, Gunawan; Yana Karisma
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.v25i2.5391

Abstract

The rapid technological development in the era of Industry 4.0 has significantly influenced the education sector, including the implementation of digital learning methods such as e-learning. This research aims to develop and evaluate a responsive web-based e-learning application for learning the Samawa (Sumbawa) regional language, in response to the declining interest of younger generations in regional languages and the increasing threat of language extinction. The methodology is a quantitative descriptive approach using WebQual 4.0 and Importance-Performance Analysis (IPA) to measure user perceptions and satisfaction with the e-learning system. Data were collected through questionnaires distributed to 170 respondents, including 25 teachers and 145 students in Sumbawa Regency, using simple random sampling. The questionnaire covered three key variables: usability, information quality, and interaction quality. The results of the validity and reliability tests confirmed that the instrument was appropriate for data collection. Descriptive statistics indicated that users generally rated the e-learning application positively, although there were discrepancies between perceived performance and expectations, particularly regarding usability and interaction.IPA quadrant analysis revealed that several indicators, especially related to navigation and content accuracy, fall into the ”main priority” category and require immediate improvement. Overall, the average performance score (3.76) was slightly below the expected average (3.84), indicating the need for further refinement in certain features. The integration of WebQual 4.0 and IPA effectively identified user needs and can serve as a framework for the continuous improvement of regional language e-learning systems.
Development of an Attention-Based Convolutional Neural Network-Long Short-Term Memory Model for Real-Time Ergonomic Analysis of Sitting Posture Tendra, Gusrio Tendra; Jollyta, Deny Jollyta; Sumijan
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.v25i2.5678

Abstract

The digital era has increased the prevalence of musculoskeletal disorders caused by poor sitting posture, posing a significant global health and productivity challenge. This study introduces an attentionbased deep learning model as the analytical engine for a proposed virtual ergonomics monitor, Ergo-Guard. The primary objective is to develop a model that accurately performs real-time Movement Quality Assessment of Sitting Posture for computer users, using only a standard webcam to ensure wide accessibility. This research method is a hybrid architecture that combines a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM), enhanced with an attention mechanism and optimized for three-dimensional skeletal data using the BlazePose Computer Vision approach. This framework merges a One-Dimensional CNN to extract spatial features from static poses with a Bidirectional LSTM network to model temporal postural shifts. An integrated attention mechanism enables the model to dynamically focus on critical ergonomic areas, mimicking an expert’s assessment. For validation, a new OfficePosture dataset was created, containing 500 videos of five common office sitting postures. The results indicate that the proposed model achieves 94.2% classification accuracy,substantially outperforming baselines from a pure CNN (84.6%) and a standard LSTM network (89.2%). Beyond accuracy, the model offers interpretable feedback through visual attention maps. In conclusion, the proposed architecture is an effective solution for monitoring sitting posture and holds considerable promise as an affordable preventive health tool for corporate and educational settings.
Enhancement of Supervised Learning Models for Intrusion Detection Through Mutual Information and Hyperparameter Tuning Jollyta, Deny; Makaruku, Yoakhina Nicole; Hajjah, Alyauma; Marlim, Yulvia Nora
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.v25i2.5760

Abstract

Enhancing the performance of supervised learning algorithms through feature and hyperparameter testing remains challenging for users, particularly when detecting computer network intrusions. There are opportunities to assess whether a supervised learning algorithm performs optimally, depending on the number of features and the choice of hyperparameters. The purpose of this research is to enhance the network intrusion detection performance of three supervised learning algorithms, namely Support Vector Machine (SVM), eXtreme Gradient Boosting, and Random Forest, by using the Mutual Information feature selection approach and hyperparameter tuning. Mutual Information measures the dependency of features on the target. Features with high values are the most informative. Hyperparameters are not learned from the data; they are set before training begins. Hyperparameters are selected in accordance with the requirements of the three algorithms via iterative training and testing on the NSL-KDD dataset. The dataset was split into 80:20, 70:30, and 60:40. The results showed that the fifteen features with the highest mutual information were identified and trained on the data using appropriate hyperparameters. By splitting the data in an 80:20 ratio, the accuracy of Support Vector Machine reached its maximum, increasing from 90% to 98%. In contrast, eXtreme Gradient Boosting and Random Forest reached their maximum, increasing from 97% and 98% to 100%, respectively. The study’s findings advance our understanding of how algorithm performance depends on feature and hyperparameter selection.
Multi-Objective Optimization of IoT-Based Hands-On Learning Using NSGA-II and R-NSGA-II Algorithms Wahyu Prasetyo, Muchamad; Aripriharta; Nur Handayani, Anik
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.v25i2.5779

Abstract

This study aims to optimize Internet of Things-based hands-on learning using a multi-objective approach with Non-dominated Sorting Genetic Algorithm II and Reference Point–based Non-dominated Sorting Genetic Algorithm II. The optimization targets three objectives: learning efficiency, learner engagement, and practical skill improvement. A modeling-based approach is employed, and simulations are conducted to evaluate the effects of key parameters, including the number of Internet of Things devices, practicum duration, and task complexity, on learning outcomes. The results show that Reference Point–based Non-dominated Sorting Genetic Algorithm II achieves higher learning efficiency (0.571) and learner engagement (0.090), producing more balanced solutions across objectives, whereas Non-dominated Sorting Genetic Algorithm II performs better on skill improvement (0.184), particularly for high-complexity tasks. Pareto front visualizations illustrate the distribution of optimal solutions, with Reference Point–based Non-dominated Sorting Genetic Algorithm II demonstrating faster convergence and more consistent solution quality. This study contributes to the design of more efficient, effective, and adaptive Internet of Things-based learning models and provides guidance for educational institutions in selecting optimization methods aligned with specific learning priorities.

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