MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer
Matrik : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer is peer-reviewed journal dedicated to interchange for the results of high quality research in all aspect of science, engineering and information technology. The journal publishes state-of-art papers in fundamental theory, experiments and simulation, as well as applications, with a systematic proposed method, sufficient review on previous works, expanded discussion and concise conclusion. Matrik follows the open access policy that allows the published articles freely available online without any subscription.
Machine Learning for Open-ended Concept Map Proposition Assessment: Impact of Length on Accuracy
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.
Copyright (c) 2025 Reo Wicaksono, Didik Dwi Prasetya, Ilham Ari Elbaith Zaeni, Nadindra Dwi Ariyanta, Tsukasa Hirashima http://creativecommons.org/licenses/by-sa/4.0
New Approach K-Medoids Clustering Based on Chebyshev Distance with Quantum Computing for Anemia Prediction
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.
Copyright (c) 2025 Mochamad Wahyudi, Solikhun Solikhun, Lise Pujiastuti, Gerhard-Wilhelm Weber http://creativecommons.org/licenses/by-sa/4.0
Flood Vulnerability Mapping in Cepu Subdistrict Using Mamdani Fuzzy Inference System for Disaster Risk Reduction
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.
Copyright (c) 2025 Joko Handoyo, Anton Yudhana, Sunardi Sunardi http://creativecommons.org/licenses/by-sa/4.0
K-Means-Based Customer Segmentation with Domain-Specific Feature Engineering for Water Payment Arrears Management
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.
Copyright (c) 2025 Andi Hary Akbar, Heri Wijayanto, I Wayan Agus Arimbawa http://creativecommons.org/licenses/by-sa/4.0
Support Vector Machine Optimization for Diabetes Prediction Using Grid Search Integrated with SHapley Additive exPlanations
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 Support Vector 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.
Copyright (c) 2025 M Safii, Husain Husain, Khairan Marzuki http://creativecommons.org/licenses/by-sa/4.0
Comparative Analysis of TF-IDF and Modern Text Embedding for the Classification of Islamic Ideologies on Indonesian Twitter
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 accurately classifying 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 in classifying complex, context-dependent texts.
Copyright (c) 2025 Siti Ummi Masruroh, ⁠Cong Dai Nguyen, Doni Febrianus http://creativecommons.org/licenses/by-sa/4.0
Detection of Rice Diseases Using Leaf Images with Visual Geometric Group (VGG-19) Architecture and Different Optimizers
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.
Copyright (c) 2025 Lalu Zazuli Azhar Mardedi, Fahry Fahry, Miftahul Madani, Hairani Hairani http://creativecommons.org/licenses/by-sa/4.0
Optimizing Random Forest for IoT Cyberattack Detection using SMOTE: A Study on CIC-IoT2023 Dataset
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.
Copyright (c) 2025 Guntoro Guntoro, Lisnawita Lisnawita, Loneli Costaner http://creativecommons.org/licenses/by-sa/4.0
Developing the Adaptive Digital IT Governance Framework for Next-Generation IT Governance
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, a novel 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-generation model to support organizational resilience and sustainable digital transformation.
Copyright (c) 2025 Bambang Saras Yulistiawan, Rifka Widyastuti , RR Octanty Mulianingtyas , Galih Prakoso Rizky A, Hengki Tamando Sihotang http://creativecommons.org/licenses/by-sa/4.0
Assessing the Semantic Alignment in Multilingual Student-Teacher Concept Maps Using mBERT
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 complex semantic 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 vectors representing 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.
Copyright (c) 2025 Nadindra Dwi Ariyanta, Didik Dwi Prasetya, Ilham Ari Elbaith Zaeni, Tsukasa Hirashima, Reo Wicaksono http://creativecommons.org/licenses/by-sa/4.0
Stochastic Optimization for Hostage Rescue Using Internet of Things and Queen Honey Bee Algorithm
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.
Copyright (c) 2025 Achmad Afif Irwansyah, Aripriharta, Didik Dwi Prasetya http://creativecommons.org/licenses/by-sa/4.0
Improving Detection Accuracy of Brute-Force Attacks on MariaDB Using Standard Isolation Forest: A Comparative Analysis with RotatedVariant
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.
Copyright (c) 2025 Hartono, Khusnul Khotimah, Rokin Maharjan http://creativecommons.org/licenses/by-sa/4.0
Proliferative Diabetic Retinopathy Detection Using Convolutional Neural Network with Enhanced Retinal Image
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.
Copyright (c) 2025 Wilda Imama Sabilla, Mamluatul Hani'ah, Ariadi Retno Tri Hayati Ririd, Astrifidha Rahma Amalia http://creativecommons.org/licenses/by-sa/4.0
Cyber Threat Detection and Automated Response Using Wazuh and Telegram API
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.
Copyright (c) 2025 Yuri Ariyanto, Yan Watequlis Syaifudin, M. Hasyim Ratsanjani, Ali Ridho Muladawila, Triana Fatmawati, Pramana Yoga Saputra, Chandrasena Setiadi http://creativecommons.org/licenses/by-sa/4.0
Middleware Development for Heterogeneous Databases on Multi-Architecture Systems Small Medium Enterprise
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’s information system integration.
Copyright (c) 2025 Christopel H. Simanjuntak, Musfiah, Muhammad Bahit, Cristovani W. Lohonauman, Stenly B. Dodie, Khamla Nonalinsavath http://creativecommons.org/licenses/by-sa/4.0
Evaluating Lecturer Satisfaction on Academic Information System Using Usability and EUCS at Bandung University of Technology
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
Copyright (c) 2025 Sela Octaviani, Evi Triandini, Dandy Pramana Hostiadi http://creativecommons.org/licenses/by-sa/4.0
Comparing K-Means, GMM and BIRCH for Student Academic Performance Data: Evaluation on Two Public Datasets
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.
Copyright (c) 2025 Ricky Aurelius Nurtanto Diaz, Ni Luh Gede Pivin Suwirmayanti, Emy Setyaningsih, I Wayan Budi Sentana http://creativecommons.org/licenses/by-sa/4.0