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Hairani
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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
New Approach K-Medoids Clustering Based on Chebyshev Distance with Quantum Computing for Anemia Prediction Mochamad Wahyudi; Solikhun Solikhun; Lise Pujiastuti; Gerhard-Wilhelm Weber
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.v25i1.4180

Abstract

Anemia is a condition where the number of red blood cells or hemoglobin levels is below normal, reducing the blood’s ability to carry oxygen, which can lead to symptoms such as fatigue, weakness, and shortness of breath.This study aims to utilize a quantum computing approach to improve the performance of the K-Medoids method by calculating the Chebyshev Distance to predict anemia. The method used is the K-Medoids clustering method with the calculation of the Chebyshev Distance and quantum computing. A comparative analysis of these methods is carried out with a focus on their performance, especially the accuracy of the test results. This study was conducted using a dataset of medical records of patients with anemia. The dataset was taken from Kaggle. This dataset includes five attributes used to predict anemia disease patterns. The dataset was tested using the classical method and K-Medoids with a quantum computing approach that utilizes the Chebyshev Distance calculation. The results of this study reveal a new alternative model for the K-Medoids algorithm with the Chebyshev Distance calculation influenced by the integration of the quantum computing framework. Specifically, the simulation test results show the same accuracy as the classical K-Medoids method and the K-Medoids method with a quantum computing approach with Chebyshev Distance calculations with an accuracy of 80%. The conclusion of this study highlights that the performance of the K-Medoids method with a quantum computing approach with Chebyshev Distance calculations can be implemented to predict anemia using the clustering method.
Proliferative Diabetic Retinopathy Detection Using Convolutional Neural Network with Enhanced Retinal Image Wilda Imama Sabilla; Mamluatul Hani'ah; Ariadi Retno Tri Hayati Ririd; Astrifidha Rahma Amalia
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.v25i1.4976

Abstract

Proliferative Diabetic Retinopathy (PDR) is the most severe stage of Diabetic Retinopathy (DR), carrying the highest risk of complications. Automatic detection can help provide earlier and more accurate PDR diagnosis, but prediction accuracy may decline due to limitations in retinal images. Therefore, image enhancement techniques are often applied to improve DR classification. This study aims to detect PDR from retinal images using Convolutional Neural Networks (CNNs) and to evaluate the impact of three enhancement methods. This research method is based on a CNN architecture, including ResNet34, InceptionV2, and DenseNet121, as well as enhancement methods such as CLAHE, Homomorphic Filtering (HF), and Multiscale Contrast Enhancement (MCE). The results of this research show that CNN performance varies across architectures and enhancement methods. The highest performance was achieved using ResNet34 with HF, yielding an accuracy of 0.976, precision of 0.934, and recall of 0.904. CLAHE generally improved performance across architectures, achieving the best average accuracy of 0.953, whereas MCE decreased classification accuracy. Overall, the findings highlight the importance of selecting appropriate enhancement methods to improve PDR detection accuracy. Implementing such systems in clinical screening could help reduce the risk of vision impairment among diabetic patients.
Machine Learning for Open-ended Concept Map Proposition Assessment: Impact of Length on Accuracy Reo Wicaksono; Didik Dwi Prasetya; Ilham Ari Elbaith Zaeni; Nadindra Dwi Ariyanta; Tsukasa Hirashima
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.v25i1.5044

Abstract

Open-ended concept maps allow learners to freely connect concepts, enriching understanding by linking new and prior knowledge. However, manually assessing proposition quality is time-consuming and subjective. This study proposes an automatic classification model for proposition quality assessment using term frequency–inverse document frequency (TF-IDF), a text representation method based on word frequency, and several machine learning algorithms. Two datasets were used are Relational Database with an average 5 words per proposition and Cybersecurity Authentication with an average 10 words per proposition. Comparative experiments with Support Vector Machine (SVM), a supervised classification algorithm, K-Nearest Neighbor, Random Forest, and Long Short-Term Memory (LSTM), a recurrent neural network for sequence data, revealed that SVM with RBF kernel achieved the highest performance on shorter propositions 87% accuracy, Cohen’s Kappa 0.76, while LSTM showed greater strength in handling longer propositions 85% accuracy, Cohen’s Kappa 0.69. These findings suggest that proposition length influences model effectiveness. The proposed approach can reduce the burden of manual assessment, increase the objectivity of evaluation, and support more efficient implementation of concept maps in education.
Assessing the Semantic Alignment in Multilingual Student-Teacher Concept Maps Using mBERT Nadindra Dwi Ariyanta; Didik Dwi Prasetya; Ilham Ari Elbaith Zaeni; Tsukasa Hirashima; Reo Wicaksono
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.v25i1.5046

Abstract

This study examines the effectiveness of mBERT (Multilingual Bidirectional Encoder Representations from Transformers) in assessing semantic alignment between student and teacher concept maps in multilingual educational contexts, comparing its performance with TF-IDF. Using datasets in both Indonesian and English, the study demonstrates that mBERT outperforms TF-IDF in capturing complexsemantic relationships, achieving 96% accuracy, 96% precision, 100% recall, and a 98% F1 score in the Indonesian dataset. In contrast, TF-IDF achieved higher precision (73%) and accuracy (79%) in the English dataset, where mBERT recorded 54% accuracy, 47% precision, but 90% recall. Semantic alignment was measured using cosine similarity to calculate the cosine of the angle between vectorsrepresenting textual embeddings generated by both models. This method facilitates cross-linguistic semantic comparison, overcoming challenges related to word frequency and syntactic variations. While mBERT’s computational demands and the study’s limited linguistic scope suggest room for improvement, the findings highlight the potential for hybrid models and emphasize the transformative impact of AI-driven tools, such as mBERT, in fostering inclusive and effective multilingual education.
Stochastic Optimization for Hostage Rescue Using Internet of Things and Queen Honey Bee Algorithm Achmad Afif Irwansyah; Aripriharta Aripriharta; Didik Dwi Prasetya
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.v25i1.5065

Abstract

This study proposes a stochastic optimization model to enhance the efficiency of hostage rescue operations using Internet of Things technology and the Queen Honey Bee Migration algorithm. The model aims to reduce response time and energy consumption by leveraging real-time data from IoT sensors to adapt dynamically to field conditions. Simulation tests conducted in a multi-story building environment demonstrated a 40% improvement in response time and a 35% reduction in energy consumption compared to conventional methods. The system also achieved up to 94.8% positioning accuracy using RSSI analysis and demonstrated consistent performance across floors. The results indicate that integrating QHBM and IoT provides a scalable and adaptive solution for mission-critical operations, with potential applications in real-world tactical planning.
Support Vector Machine Optimization for Diabetes Prediction Using Grid Search Integrated with SHapley Additive exPlanations M Safii; Husain Husain; Khairan Marzuki
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.v25i1.5133

Abstract

The high number of diabetes mellitus sufferers has become a global health issue, and a scientific approach is needed to produce accurate and efficient diagnoses, which can then support decision-making in providing solutions for its management. The goal of this research is to develop a machine learning model that can accurately, efficiently, and transparently diagnose diabetes mellitus for use in clinical practice. This research method involves using the Support Vector Machine (SVM) algorithm, optimized with the Grid Search technique, and evaluated interpretively using the SHapley Additive exPlanations (SHAP) method. This research uses a secondary dataset consisting of the parameters Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, Body Mass Index, DiabetesPedigree- Function, and Age. Data preprocessing was carried out by performing normalization using a standard scaler and dividing the data into training and testing sets. The results of this study show that the SVM model achieved an accuracy of 0.7532 with the optimal parameters C: 1, gamma: 0.01, and kernel: rbf. Using SHAP, the analysis shows that the parameters Glucose, Body Mass Index, and Age have a significant impact on the results of diabetes classification. The main finding of this study is that SupportVector Machine optimization with SHapley Additive exPlanations can deliver excellent performance in diabetes prediction while also enhancing model transparency. The study’s implications suggest that the results can serve as a foundation for developing a medical diagnosis system that is straightforward, accurate, and easy to understand for diabetes mellitus.
K-Means-Based Customer Segmentation with Domain-Specific Feature Engineering for Water Payment Arrears Management Andi Hary Akbar; Heri Wijayanto; I Wayan Agus Arimbawa
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.v25i1.5186

Abstract

Indonesian water utilities face persistent challenges in managing payment delinquencies due to diverse customer characteristics, geographic limitations, and inadequate analytical capabilities. Addressing this issue is essential to optimizing revenue collection and supporting sustainable operations. This study aims to develop a data-driven customer segmentation framework using K-means clustering to enhance delinquency management. The framework incorporates six engineered features—Debt Efficiency, Payment Behavior Score, Category Risk Score, Geographic Risk Score, Consumption Intensity, and Financial Risk Score—designed to capture customer payment behavior, consumption patterns, and geographic risk. We applied the model to 1,500 anonymized customer records from PT Air Minum Giri Menang, focusing on those with delinquencies exceeding four months. Risk scoring was based on quintile distribution, and optimal clustering was determined through the elbow method combined with silhouette coefficient analysis. The results produced a two-cluster solution (silhouette score = 0.538), showing statistically significant differences across features (p ¡ 0.001) and medium-to-large effect sizes (Cohen’s d = 0.52–2.12). The segmentation identified medium-risk customers (86.7%) who require preventive management and high-risk customers (13.3%) who need billing intervention. Urban areas exhibited higher delinquency risk (18.4%) than rural areas (2.5%), indicating the need for geographically targeted strategies. All customer data was anonymized following Indonesian data protection protocols. In conclusion, the proposed framework transforms manual billing supervision into an adaptive, data-driven management system, contributing to segmentation research by introducing utility-specific engineered features for Indonesian water utilities.
Detection of Rice Diseases Using Leaf Images with Visual Geometric Group (VGG-19) Architecture and Different Optimizers Lalu Zazuli Azhar Mardedi; Fahry Fahry; Miftahul Madani; Hairani Hairani
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.v25i1.5286

Abstract

Rice is a major food commodity in Indonesia that plays a vital role in maintaining national food security. However, rice productivity often declines due to pest and disease attacks, especially when the disease is not detected early. Currently, the process of identifying rice diseases is generally still carried out manually by farmers or experts through direct observation, which is subjective, time-consuming, and prone to identification errors. To overcome these limitations, a technology-based solution is needed that is able to detect rice diseases automatically, quickly, and accurately. This study aims to develop a rice disease detection system based on leaf images using a deep learning approach with the Visual Geometric Group (VGG-19) architecture. The research method used is experimental by comparing the performance of the VGG-19 architecture using three different types of optimizers, namely Adaptive Moment Estimation (ADAM), Root Mean Square Propagation (RMSProp), and Stochastic Gradient Descent (SGD), to obtain the best accuracy in rice disease classification. The findings show that the combination of VGG-19 with the ADAM optimizer produces the highest accuracy of 96.45%, followed by RMSProp at 95.96% and SGD at 87.08%. These findings indicate that the selection of optimizers plays an important role in improving the performance of deep learning models, especially in detecting rice diseases based on leaf images.
Optimizing Random Forest for IoT Cyberattack Detection using SMOTE: A Study on CIC-IoT2023 Dataset Guntoro Guntoro; Lisnawita Lisnawita; Loneli Costaner
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.v25i1.5382

Abstract

The growing number of Internet of Things devices has led to an increased risk of complex and diverse cyberattacks. However, a significant challenge in this domain is the imbalanced class distribution in most Internet of Things datasets, cautilizing classification algorithms to be biased towards the majority class, hindering effective threat detection. This study addresses this issue by leveraging the Random Forest algorithm optimised by the Synthetic Minority Oversampling Technique. This research aims to develop an effective model for detecting cyberattacks in Internet of Things environments by resolving class imbalance issues inside of the CIC-IoT2023 dataset. The methodology involves several stages, comprising data preprocessing and applying Synthetic Minority Oversampling Technique for data balancing. The balanced dataset was then used to train a Random Forest model, by its performance evaluated utilizing accuracy, precision, recall, F1-score, and Cohen's Kappa metrics. The results demonstrate the model's effectiveness, achieving an accuracy of 99.01%, an F1-score of 98.96%, and a Cohen's Kappa of 98.92%. This marks a notable improvement in performance, particularly in detecting minority classes, compared to the model trained devoid of Synthetic Minority Oversampling Technique, that struggled to identify several less common attack types. The outcomes suggest that combining Random Forest by Synthetic Minority Oversampling Technique can significantly enhance the development of intrusion detection systems by improving detection accuracy for all 33 attack types and reducing the risks associated by undetected threats. In conclusion, this study advances Internet of Things cybersecurity by presenting an effective and efficient method for addressing data imbalance in attack detection. Future research should focus on evaluating the model's robustness utilizing more complex datasets and enhancing its performance for real-time deployment on resource-constrained Internet of Things Devices.
Flood Vulnerability Mapping in Cepu Subdistrict Using Mamdani Fuzzy Inference System for Disaster Risk Reduction Joko Handoyo; Anton Yudhana; Sunardi Sunardi
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.v25i1.5390

Abstract

Floods pose a persistent and serious threat to Cepu Subdistrict, frequently causing significant economic loss, resident displacement, and damage to critical infrastructure. In response to this issue, and aligned with the National Disaster Management Agency's (BNPB) efforts to enhance landscape monitoring, a comprehensive analytical study was conducted. The purpose of this research was to assess and map the flood vulnerability levels across 17 villages in Cepu Subdistrict, categorizing them to facilitate more effective disaster response planning and resource allocation. The research method uses the Mamdani Fuzzy Inference System, an advanced computational approach adept at handling the non-linear relationships between environmental variables. This system allowed for a detailed analysis of the complex interactions among key flood-influencing factors, including rainfall intensity, watershed area, elevation, slope, and population density. The results of the quantitative research obtained from 17 villages in the Cepu Subdistrict show that Ngelo Village has the highest score of 65.16, categorized as a "high" risk level. In contrast, most other villages, such as Ngroto, Karangboyo, and Cabean, fell into the "medium" risk category with varying scores between 55.0 and 63.93. The model's accuracy was validated by evaluation metrics, with a Mean Absolute Error (MAE) of 8.67 and a Root Mean Squared Error (RMSE) of 10.29, indicating satisfactory predictive performance. The conclusion of this study emphasizes the urgent need for comprehensive and adaptive mitigation strategies, including early warning systems and community preparedness programs, to protect Cepu Subdistrict from future flood threats.

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