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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 15 Documents
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Search results for , issue "Vol. 24 No. 3 (2025)" : 15 Documents clear
Usability Test on the System Determination Decision Support ReleaseProduct Towards Contribution Level Decision Maker Daniati, Erna; Sucipto, Sucipto; Wardani, Anita Sari; Pradhana, Akmal Hisyam
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i3.3789

Abstract

The core problem addressed in this research is the usability challenges of a decision support system for determining product release, which can hinder decision-makers’ effectiveness and user satisfaction. The purpose of this research is to evaluate the usability of the system and assess its impact on the effectiveness of decision-makers in determining product releases. The method used is a usability test involving direct user interaction with the system, where decision-makers performed predefined tasks. Usability metrics, including task completion time, error rate, and user satisfaction levels, were collected and analyzed to evaluate system performance. The result of this study is that the system facilitates efficient decision-making to a moderate extent. However, specific usability issues, such as navigation complexity and information overload, were identified, which reduced some users’ ability to operate the system seamlessly. Improvements in navigation and information presentation significantly enhanced user experience and decision-making quality. The research concludes that enhancing the usability of decision support systems is essential for maximizing their contribution to decision-making processes. Addressing specific challenges, such as simplifying navigation and optimizing information presentation, can substantially improve decision-maker satisfaction and the overall utility of the system. This study emphasizes the importance of usability-focused design in facilitating effective organizational decision-making.
Enhancing Lung Cancer Prediction Accuracy UsingQuantum-Enhanced K-Medoids with Manhattan Distance Solikhun, Solikhun; Pujiastuti, Lise; Wahyudi, Mochamad
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i3.4190

Abstract

Lung cancer is a leading cause of cancer-related deaths worldwide, and early detection plays a crucialrole in improving treatment outcomes. This study proposes an enhancement of the K-Medoids clusteringmethod by integrating a quantum computing approach using Manhattan distance to improveprediction accuracy for lung cancer diagnosis. The research was conducted using a publicly availablelung cancer dataset consisting of 309 patient records with 14 diagnostic attributes. Comparative experimentswere carried out between the classical K-Medoids and the quantum-enhanced K-Medoids, withperformance evaluated based on clustering accuracy, precision, recall, and F1-score. The results showthat the quantum-based method has the same accuracy as the classical method, namely 88%. Thissuggests that quantum-based clustering can match the accuracy of classical methods after adequatetraining, although consistency and parameter stability remain areas for further refinement. Furtherresearch is recommended to test the model on larger datasets and to explore real-world deployment inclinical decision support systems.
Comparison of Random Forest Support Vector Machine and Passive Aggressive Models on E-nose-Based Aromatic Rice Classification Sumanto, Budi; Nurrahma, Salima
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i3.4291

Abstract

Accurate classification of aromatic rice types is crucial for maintaining quality and meeting consumer preferences. The purpose of this study is to classify MentikWangi, PandanWangi, and C4 rice based on their volatile content using e-nose. C4 rice, as a popular non-aromatic variety, was included as a comparison for sensor response analysis. The research method involved preprocessing the e-nose gas sensor readings, including feature extraction, baseline manipulation, and missing value checking, to ensure data quality. The classification was performed using Random Forest, Support Vector Machine, and Passive-Aggressive methods. The results showed that the Random Forest model achieved the highest accuracy of 97%, followed by the Support Vector Machine at 95% and Passive Aggressive at 90%. The model evaluation utilized a Confusion Matrix and Receiver Operating Characteristics, which confirmed that Random Forest was the best-performing model. This study concludes that e-nose-based classification effectively differentiates between aromatic rice types, providing significant potential for objective and efficient quality assessment and offering valuable insights for further research in areas such as food technology, agricultural science, and chemical analysis.
A Novel CNN-Based Approach for Classification of Tomato Plant Diseases Sholihin, Miftahus; Bin Md. Fudzee, Mohd Farhan; Anifah, Lilik
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i3.4464

Abstract

Tomatoes are one of the most widely cultivated and consumed crops globally, but they are highly susceptibleto various diseases that can significantly reduce yield and quality. Early detection of thesediseases is crucial for effective management and prevention. The objective of this study is to developan accurate early detection system for tomato diseases using deep learning to support effective cropmanagement. The research method employed is a modified Convolutional Neural Network trainedon the PlantVillage dataset, which consists of 21,000 images across 10 disease classes. The studyevaluates three training scenarios using different epoch values (25, 50, and 75) to optimize modelperformance. Data preprocessing included image resizing and augmentation, followed by ConvolutionalNeural Network training and validation. The study’s results showed that increasing epochsimproved the model’s accuracy: 98.18% at 25 epochs, 98.53% at 50 epochs, and 99.19% at 75 epochs.Precision, recall, and F1-score also increased, from 90.95% at 25 epochs to 95.80% at 75 epochs, indicatingenhanced model reliability. However, longer training times were required as the epoch countincreased. This research concludes that a modified Convolutional Neural Network can accuratelyclassify tomato diseases, providing a reliable and practical tool for early disease detection. The proposedsystem has the potential to be integrated into mobile applications for real-time use in the field.It contributes to sustainable agriculture by enabling timely disease intervention and improving cropproductivity.
Leveraging Vector Quantized Variational Autoencoder for Accurate Synthetic Data Generation in Multivariate Time Series Diqi, Mohammad; Utami, Ema; Kusrini, Kusrini; Wibowo, Ferry Wahyu
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i3.4514

Abstract

This study addresses the challenge of generating high-quality synthetic financial time series data, acritical issue in financial forecasting due to limited access to complete and reliable historical datasets.The aim of this research was to compare the performance of the standard Variational Autoencoder andthe Vector Quantized Variational Autoencoder (VQ-VAE) in generating synthetic multivariate time seriesdata using the Adaro Energy Indonesia stock dataset. The VQ-VAE incorporates a discrete latentspace to improve the structure and control of the data generation process, whereas the standard VAEutilizes a continuous latent space. This research method was based on the implementation of bothmodels, followed by a quantitative evaluation using statistical metrics, including mean absolute error(MAE), mean squared error (MSE), root mean squared error (RMSE), and R² score. This researchshowed that the VQ-VAE outperformed the standard VAE in replicating the statistical characteristicsof stock prices, as shown by lower error values and higher R² scores across all tested features. The discretelatent space of the VQ-VAE led to the generation of more structured and statistically consistentsynthetic data. The implications of these findings suggest that the VQ-VAE model is highly suitablefor financial forecasting applications and indicate the potential for future enhancements throughintegration with hybrid models, such as attention mechanisms or generative adversarial networks.
Investigating Liver Disease Machine Learning Prediction Performancethrough Various Feature Selection Methods Wafi, Ahmad Zein Al; Rochim, Febry Putra; Bezaleel, Veda
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i3.4531

Abstract

Given the increasing prevalence and significant health burden of liver diseases globally, improving the accuracy of predictive models is essential for early diagnosis and effective treatment. The purpose of the study is to systematically analyze how different feature selection methods impact the performance of various machine learning classifiers for liver disease prediction. The research method involved evaluating four distinct feature selection techniques—regular, analysis of variance (ANOVA), univariate, and model-based on a suite of classifiers, including decision forest, decision tree, support vector classifier, multi-layer perceptron, and linear discriminant analysis. The result revealed a significant and variable impact of feature selection on model accuracy. Notably, the ANOVA method paired with the multi-layer perceptron achieved the highest accuracy of 0.801724, while the univariate method was optimal for the decision forest classifier (0.741379). In contrast, model-based selection often degraded performance, particularly for the decision tree classifier, likely due to the introduction of noise and overfitting. The support vector classifier, however, demonstrated robust and consistent accuracy across all selection techniques. These findings underscore that there is no universally superior feature selection method; instead, optimal predictive performance hinges on tailoring the selection technique to the specific machine learning model. This study contributes practical, evidence-based insights into the critical interplay between feature selection and model choice in medical data analysis, offering a guide for improving classification accuracy in liver disease prediction. Future work should explore more sophisticated and hybrid feature selection methods to enhance model performance further.
Improving the User Interface and Experience of a Student PortalThrough the Eight Golden Rules Sumari, Arwin Datumaya Wahyudi; Perdana, Fatiha Eros; Nugraheny, Dwi; Lovrencic, Sandra
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i3.4542

Abstract

One of the crucial web-based academic service facilities in higher education is the Student Portal. Based on a survey of student users, the existing Student Portal at the Institut Teknologi Dirgantara Adisutjipto (Design A) is visually unappealing. It therefore requires improvement in terms of User Interface (UI) design. The purpose of this study is to enhance the UI and UX of the Student Portal. The method used involved applying the Eight Golden Rules method and the Maze tool to design the UI. The resulting new design (Design B) and the current one (Design A) were tested using A/B testing. This study involved a sample of 41 student users from the Informatics Study Program, as they were considered familiar with UI/UX, along with four staff users selected to represent the overall population of Student Portal users. The instrument that is used to evaluate both designs is the System Usability Scale (SUS). The test results show that Design A received an average score of 55.0, which falls into the ”OK” category with a grade of D. In contrast, Design B, which incorporates the Eight Golden Rules method, achieved an average score of 75.0, placing it in the ”GOOD” category with a grade of B. In conclusion, the application of the Eight Golden Rules method led to a 36.4% improvement inthe UI and UX of the Student Portal.
Optimized YOLOv8 Model for Accurate Detection and Quantificationof Mango Flowers Mardiana, Ardi; Bastian, Ade; Tarsono, Ano; Susandi, Dony; Safari Yonasi
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i3.4614

Abstract

Mangoes are widely cultivated and hold significant economic value worldwide. However, challenges in mango cultivation, such as inconsistent flowering patterns and manual yield estimation, hinder optimal agricultural productivity. This study addresses these issues by leveraging the You Only Look Once (YOLO) version 8 object detection technique to automatically recognize and quantify mango flowers using image processing. This research aims to develop an automated method for detecting and estimating mango yields based on flower density, representing the early stage of the plant growth cycle. The methodology involves utilizing YOLOv8 object detection and image processing techniques. A dataset of mango tree images was collected and used to train a CNN-based YOLOv8 model, incorporating image augmentation and transfer learning to improve detection accuracy under varying lighting and environmental conditions. The results demonstrate the model’s effectiveness, achieving an average mAP score of 0.853, significantly improving accuracy and efficiency compared to traditional detection methods. The findings suggest that automating mango flower detection can enhance precision agriculture practices by reducing reliance on manual labor, improving yield prediction accuracy, and streamlining monitoring techniques. In conclusion, this study contributes to the advancement of precision agriculture through innovative approaches to flower detection and yield estimation at early growth stages. Future research directions include integrating multispectral imaging and drone-based monitoring systems to optimize model performance further and expand its applications in digital agriculture.
Enhancing Semantic Similarity in Concept Maps Using LargeLanguage Models Wiryawan, Muhammad Zaki; Prasetya, Didik Dwi; Handayani, Anik Nur; Hirashima, Tsukasa; Pratama, Wahyu Styo; Putra, Lalu Ganda Rady
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i3.4727

Abstract

This research uses advanced models, Generative Pre-trained Transformer-4 and Bidirectional Encoder Representations from Transformers, to generate embeddings that analyze semantic relationships in open-ended concept maps. The problem addressed is the challenge of accurately capturing complex relationships between concepts in concept maps, commonly used in educational settings, especially in relational database learning. These maps, created by students, involve numerous interconnected concepts, making them difficult for traditional models to analyze effectively. In this study, we compare two variants of the Artificial Intelligence model to evaluate their ability to generate semanticembeddings for a dataset consisting of 1,206 student-generated concepts and 616 link nodes (Mean Concept = 4, Standard Deviation = 4.73). These student-generated maps are compared with a reference map created by a teacher containing 50 concepts and 25 link nodes. The goal is to assess the models’ performance in capturing the relationships between concepts in an open-ended learning environment. The results show that demonstrate that Generative Pretrained Transformers outperform other models in generating more accurate semantic embeddings. Specifically, Generative Pre-trained Transformer achieves 92% accuracy, 96% precision, 96% recall, and 96% F1-score. This highlights the Generative Pretrained Transformer’s ability to handle the complexity of large, student-generatedconcept maps while avoiding overfitting, an issue observed with the Bidirectional Encoder Representationsfrom Transformer models. The key contribution of this research is the ability of two complex models and multi-faceted relationships among concepts with high precision. This makes it particularly valuable in educational environments, where precise semantic analysis of open-ended data is crucial, offering potential for enhancing concept map-based learning with scalable and accurate solutions.
Performance Evaluation of Artificial Intelligence Models for Classification in Concept Map Quality Assessment Pratama, Wahyu Styo; Prasetya, Didik Dwi; Widyaningtyas, Triyanna; Wiryawan, Muhammad Zaki; Putra, Lalu Ganda Rady; Hirashima, Tsukasa
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i3.4729

Abstract

Open-ended concept maps generated by students give better flexibility and present a complex analysis process for teachers. We investigate the application of classification algorithms in assessing openended concept maps, with the purpose of providing assistance for teachers in evaluating student comprehension. The method used in this study is experimental methods, which consists of data collection, preprocessing, representation generation, and modelling with Feedforward Neural Network, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, and Logistic Regression. Our dataset, derived from concept maps, consists of 3,759 words forming 690 propositions, scored carefully by experts to ensure high accuracy in the evaluation process. Results of this study indicate that K-NN outperformed all other models, achieving the highest accuracy and Receiver Operating Characteristic-Area Under the Curve scores, demonstrating its robustness in distinguishing between classes. Support Vector Machine excelled in precision, effectively minimizing false positives, while Random Forest showcased a balanced performance through its ensemble learning approach. Decision Tree and Linear Regression showed limitations in handling complex data patterns. FeedforwardNeural Network can model intricate relationships, but needs further optimization. This research concluded that Artificial Intelligence classification enables a better assessment for teachers, enables the path for personalized learning strategies in learning.

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