1 10 28 https://repository.horizon.ac.id/files/original/18bbbea9619077aea7cb62c343156bb6.pdf 2c192a304f6d2730e8dac7d52279bf54 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 3 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Automated Indonesian Plate Recognition: YOLOv8 Detection and TensorFlow-CNN Character Classification Subject The topic of the resource YOLO; TensorFlow; optical character recognition (OCR); indonesian license plate detection; deep learning Description An account of the resource The precise identification and reading of Indonesian vehicle number plates are important in many areas, including the enforcement of law, collection of charges, management of parking areas, and safety measures. This study integrates the implementation of the YOLOv8 object detection algorithm with three OCR methods: EasyOCR, TesseractOCR, and TensorFlow. YOLOv8 is capable ofidentifying license plates from images and videos at a high speed and reliability under different conditions and therefore is used in this study to perform plate detection in images and videos. After licenses are detected, OCR techniques are performed to segment and read the letters. Both EasyOCR and TesseractOCR performed moderately well on static images achieving accuracy rates of 70% and 68% respectively, but both suffered significantly lower performance in video scenarios. Of the 100 video frames, EasyOCR was able to correctly identify characters in 61 frames and TesseractOCR in 58 frames, while the TensorFlow-based model outperformed the other two with 75 correct recognitions. Furthermore, easy OCR and static images as input while the TensorFlow-based models completed them with 100% accuracy. This observation can be explained by its design, which utilizes a CNN with ReLU activation and Softmax outputs, trained on 10,261 annotated characters and was enhanced with five different data augmentation techniques. The model shows strong performance in its ability to handle dynamic conditions such as motion blur, changing light conditions, and rotation of the plate angle. The results underscore the drawbacks of one-size-fits-all OCR applications in real-world usecases and stress the need for bespoke model training, as well as hierarchical contouring, in the context of automatic license plate recognition (ALPR). This study provides additional insights into ALPR systems by delivering a robust, scalable, and real-time tool for plate and character recognition, which is essential for intelligent transportation systems Creator An entity primarily responsible for making the resource Windu Gata1*, Dwiza Riana2, Muhammad Haris3, Maria Irmina Prasetiyowati4, Dika Putri Metalica5 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6310/1066 Publisher An entity responsible for making the resource available Computer Science, Faculty of Information Technology, Universitas Nusa Mandiri, Jakarta, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource June 15, 2025 Contributor An entity responsible for making contributions to the resource The precise identification and reading of Indonesian vehicle number plates are important in many areas, including the enforcement of law, collection of charges, management of parking areas, and safety measures. This study integrates the implementation of the YOLOv8 object detection algorithm with three OCR methods: EasyOCR, TesseractOCR, and TensorFlow. YOLOv8 is capable ofidentifying license plates from images and videos at a high speed and reliability under different conditions and therefore is used in this study to perform plate detection in images and videos. After licenses are detected, OCR techniques are performed to segment and read the letters. Both EasyOCR and TesseractOCR performed moderately well on static images achieving accuracy rates of 70% and 68% respectively, but both suffered significantly lower performance in video scenarios. Of the 100 video frames, EasyOCR was able to correctly identify characters in 61 frames and TesseractOCR in 58 frames, while the TensorFlow-based model outperformed the other two with 75 correct recognitions. Furthermore, easy OCR and static images as input while the TensorFlow-based models completed them with 100% accuracy. This observation can be explained by its design, which utilizes a CNN with ReLU activation and Softmax outputs, trained on 10,261 annotated characters and was enhanced with five different data augmentation techniques. The model shows strong performance in its ability to handle dynamic conditions such as motion blur, changing light conditions, and rotation of the plate angle. The results underscore the drawbacks of one-size-fits-all OCR applications in real-world usecases and stress the need for bespoke model training, as well as hierarchical contouring, in the context of automatic license plate recognition (ALPR). This study provides additional insights into ALPR systems by delivering a robust, scalable, and real-time tool for plate and character recognition, which is essential for intelligent transportation systems. Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/8ba50e4522c399406706207f1b47ac20.pdf b9384acd3b47c4bd89614938e420b367 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 3 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource The Effect of Hyperparameters on Faster R-CNN inFace Recognition Systems Subject The topic of the resource face recognition;faster R-CNN;hyperparameter optimization;deep learning;grid search Description An account of the resource Facial recognition remains a significant challenge in the advancement of computer vision technologies. This research seeks todevelop a facial recognition system utilizing the Faster R-CNN architecture, with performance enhancement achieved through hyperparameter optimization. This research utilizes the "Face Recognition Dataset" from Kaggle, which comprises 2,564 face images across 31 classes. The development process involves creating bounding boxes using the LabelImg application and implementing the Grid Search method. The Grid Search is applied with predefined hyperparameter combinations (3 epochs [10, 25, and 50] × 3 learning rates [0.001, 0.0001, and 0.00001] × 3 optimizers [SGD, Adam, and RMS], resulting in 27 models). The evaluation of the model was conducted using accuracy, precision, recall, and F1-score as performance metrics. The experimental findings indicate that hyperparameter selection has a substantial impact on model performance. Among the tested configurations, the combination of a learning rate of 0.00001, 50 training epochs, and the Adam optimizer achieved thehighest accuracy, resulting in an 8.33% improvement over the baseline model. The results indicate that hyperparameter optimization enhances the ability of the model to recognize faces. Compared to conventional models, theFaster R-CNN performs better in detecting faces more accurately. Future research could further enhance the face recognition efficiency andaccuracy by exploring other deep learning architectures and more advanced hyperparameter optimization techniques Creator An entity primarily responsible for making the resource Jasman Pardede1*, Khairul Rijal2 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6405/1061 Publisher An entity responsible for making the resource available Department of Informatics, Faculty of Industrial Technology, Institut Teknologi Nasional, Bandung, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource May, 28, 2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/40c47546e0bbeec6c93ce995f42bfbca.pdf 54877d49de2f23442359b73d6703ec35 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 3 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Classification of Retinoblastoma Eye Disease on Digital Fundus Images Using Geometric Features and Machine Learning Subject The topic of the resource retinoblastoma; digital fundus images; classification; geometric features; machine learning Description An account of the resource Medical image analysis is essential for detecting retinoblastoma tumors due to the ability of the method to assist doctors in examining the morphology, density, and distribution of blood vessels. The classification of normal and retinoblastoma-affected retinas is a preliminary stepin treating retinoblastoma tumors. Therefore, this research aimed to propose the new development of a method to classify normal and retinoblastoma-affected retinas using geometric feature extraction and machine learning. The workflow consisted of (1) Fundus image data collection for retinoblastomas, (2) image segmentation, (3) feature extraction process, (4) building a classification model using machine learning, (5) splitting testing and training data, (6) classification process using machine learning methods, and (7) evaluation of classification results using a confusion matrix. The results showed that the segmentation method used could detect retinoblastoma areas and extract geometric features. The SVM method achieved an accuracy of 0.96 while the RF andDT had 0.55 and 0.63, respectively. Moreover, the comparison with previous research showed that the method proposed had a 4% improvement in classification performance. This led to the conclusion that the classification using geometric features combined with the SVM on digital fundus images of retinoblastoma eye disease produced the best results Creator An entity primarily responsible for making the resource Arif Setiawan Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6337/1058 Publisher An entity responsible for making the resource available Department of Information System, Faculty of Engineering, Muria Kudus University, Kudus, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource May 24, 2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/a4f795004adbb600ad3884e28a15bd92.pdf a0c0496549579640b1e969befe972af3 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 3 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Enhancing Areca Nut Detection and Classification Using Faster R-CNN: Addressing Dataset Limitations with Haar-like Features, Integral Image, and Anchor Box Optimization Subject The topic of the resource areca nut classification; deep learning; faster R-CNN; haar-like features; integral image; object detection Description An account of the resource The classification and detection of areca nuts are essential for agriculture and food processing to ensure product quality and efficiency. The manual classification of areca nuts is time-consuming and prone to human error. For a more accurate and efficient automated approach, a deep learning-based framework was proposed to address these challenges. This study optimizes the Faster R-CNN by integrating Haar-like features and integral images to enhance object detection. However, dataset limitations, including low image quality, inconsistent lighting, clutteredbackgrounds, and annotation inaccuracies, affect the model performance. In addition, the small dataset size and class imbalance hindered generalization. The Faster R-CNN model was trained with and without Haar-like Features and Integral Image enhancement.Performance was evaluated based on training loss, accuracy, precision, recall, F1-score, and mean average precision (mAP). The effects of the dataset limitations on detection performance were also analyzed. The optimized model achieved better stability, with a final training loss of 0.2201, compared to 0.1101 in the baseline model. Accuracy improved from 62.60% to 73.60%, precision from 0.6161 to 0.7261, recall from 0.3094 to 0.4194, F1-score from 0.2307 to 0.3407, and mAP from 0.1168 to 0.2268. Despite these improvements, dataset constraints remain a limiting factor. While the integration of Haar-like features and integral images into faster R-CNN contributes to detection accuracy, the study also reveals that high-resolution images, precise annotations, and dataset scale significantly amplify model performance Creator An entity primarily responsible for making the resource Yovi Pratama1*,2, Errissya Rasywir2, Suyanti3, Agus Siswanto3, Fachruddin3 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6496/1099 Publisher An entity responsible for making the resource available Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, Johor 81310, Malaysia Date A point or period of time associated with an event in the lifecycle of the resource June 25, 2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/165b83d1410f0c2180ed5d0892ff96ce.pdf 7e4bd774aee8bb85a7e213ddbbb97143 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 3 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Automatic Classification of MultilanguageScientific Papersto the Sustainable Development Goals Using Transfer Learning Subject The topic of the resource multilingual model; multilabel text classification; scientific papers; SDGs research Description An account of the resource The classification of scientific papers according to their relevance to Sustainable Development Goals (SDGs) is a critical task in identifying the research development status of goals. However, with the growing volume of scientific literature published worldwide in multiple languages, manual categorization of these papers has become increasingly complex and time-consuming. Furthermore, the need for a comprehensive multilingual dataset to train effective models complicates the task, as obtaining such datasets for various languages is resource intensive. This study proposes a solution to this problem by leveraging transfer learning techniques to automatically classify scientific papers into SDG labels. By fine-tuning pretrained multilingual models mBERT on SDG publication datasets in a multilabel approach, we demonstrate that transfer learning can significantly improve classification performance, even with limited labelled data, compared to SVM. Our approach enables the effective processing of scientific papers in different languages and facilitates the seamless mapping of research to the relevance of SDGs, the four pillars of SDGs, and the 17 goals of SDGs. The proposed method addresses the scalability issue in SDG classification and lays the groundwork for more efficient systems that can handle the multilingual nature of modern scientific publications. Creator An entity primarily responsible for making the resource Lya Hulliyyatus Suadaa1*, Anugerah Karta Monika2, Berliana Sugiarti Putri3, Yeni Rimawat Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6560/1093 Publisher An entity responsible for making the resource available Politeknik Statistika STIS, Jakarta, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource June 23, 2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/ab0e0759e6954e5ce460a328fa56ebce.pdf 8c97291e95d84eade8d63ee8726f376b Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 3 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Minangkabau Language Stemming: A New Approach with Modified Enhanced Confix Stripping Subject The topic of the resource enhanced confix stripping; minangkabau language; morphological; natural language processing; stemming Description An account of the resource Stemming is an essential procedure in natural language processing (NLP), which involves reducing words to their root forms by eliminating affixes, including prefixes, infixes, and suffixes. The employed method assesses the efficacy of stemming, which differs according to language. Complex affixation patterns in Indonesian and regional languages such as Minangkabau pose considerable difficulties for traditional algorithms. This research adopts the enhanced fixed-stripping method to tackle these issues by integrating linguistic characteristics unique to Minangkabau. This study has three phases: data acquisition, pseudocode development, and algorithm execution. Testing revealed an average accuracy of 77.8%, indicating the algorithm's proficiency in managing Minangkabau’s intricate morphology. Nevertheless, constraints persist, particularly with irregular affixationpatterns. Possible improvements could include adding more datasets, improving the rules for handling affixes, and using machine learning to make the system more flexible and accurate. This study emphasizes the significance of customized solutions for regional languages and provides insights into the advancement of NLP in various linguistic environments. The findings underscore the progress made in processing Minangkabau text while also emphasizing the need for further research to address current issues Creator An entity primarily responsible for making the resource Fadhli Almu’iini Ahda1, Aji Prasetya Wibawa2*, Didik Dwi Prasetya3,Danang Arbian Sulistyo4, Andrew Nafalski Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6511/1092 Publisher An entity responsible for making the resource available Elecrtrical Engineering and Informatics, Universitas Negeri Malang, Malang, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource June 23, 2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/06f17bbfe401175b039c82286c46ab04.pdf 1b2ed06164fa5b8b4087456b172cdf2e Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 3 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Stunting Prediction Modeling in Toddlers Using a Machine Learning Approach and Model Implementation for Mobile Application Subject The topic of the resource machine learning; mobile application; stunting prediction Description An account of the resource Children’s health and development are critical for maintaining national productivity and independence, with stunting being a major concern. Stunting, a form of malnutrition, impairs growth and development, affecting millions of people globally, including a significant number in Indonesia. This study addresses the challenge of stunting by developing a predictive model using machine learning techniques to forecast stunting risks based on public health data. The literature review section discusses the factors that influence stunting, and these factors are used as features to builda stunting prediction model. Then the features were used to build a model with three machine learning algorithms Extreme Gradient Boosting (XGBoost), Random Forest, and K-Nearest Neighbor (KNN) to build and evaluate models that predict stunting. The models were trained and assessed using public datasets and the most effective algorithm was integrated into a mobile application for practical use. The results indicate that the XGBoost model outperforms the other models with an accuracy of 85%, making it the optimal choice for implementation in a mobile application. The next-best model is selected to be implemented through a mobile application so that users can directly use the model that has been built. This application aims to enhance early detection and intervention efforts for stunting, potentially improving child health outcomes and contributing to long-term productivity by building predictive models and implementing the models into a mobile application. This study contributes to the implementation of models built using public data for application in mobile applications Creator An entity primarily responsible for making the resource Eko Abdul Goffar1,2*,Rosa Eliviani1, Lili Ayu Wulandhari2 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6450/1091 Publisher An entity responsible for making the resource available Departement of Informatics Management, Astra Polytechnic, Jakarta, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource June 22, 2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/ce2d04e1e1473a399d2feb001bc5ffe9.pdf cfe84a045b49efc0a65db90cc5f4e4e6 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 3 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A New Framework for Dynamic Educational Marketing Segmentation in Student Recruitment: Optimizing Fuzzy C-Means with Metaheuristic Techniques Subject The topic of the resource dynamic educational marketing;fuzzy C-Means; metaheuristic optimization; RFM; student recruitment Description An account of the resource An effective educational marketing strategy requires accurate school segmentation to enhance new student recruitment. Traditional segmentation methods such as K-means are often used, but they have limitations in capturing the flexibility of school characteristics. Fuzzy C-Means (FCM) offers a more adaptive approach by allowing each school to simultaneously have a degree of membership in several clusters. However, the performance of FCM highly depends on determining parameters such as the number of clusters (k) and the level of fuzziness (m), which are not always optimal when determined manually. This study develops a new framework for dynamic educational marketing segmentation in student recruitment by optimizing FCM using three metaheuristic techniques: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Differential Evolution (DE). Performance was evaluated using theFuzzy Silhouette Index (FSI). The experimental results showed that DE yielded the best results with the highest FSI value (0.8023), producing eight main clusters based on the Recency, Frequency, and Monetary(RFM) model. Based on the clustering results, apersonalized and adaptive marketing strategy was designed to enhance the effectiveness of student recruitment. The proposed framework enhances segmentation accuracy and supports the implementation of dynamic data-driven marketing in the context of higher education. This study also opens new directions for educational data mining research and machine-learning-based marketing strategies. Creator An entity primarily responsible for making the resource Rizal Bakri1*, Bobur Sobirov2, Niken Probondani Astuti3, Ansari Saleh Ahmar4, Pawan Kumar Singh Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6515/1090 Publisher An entity responsible for making the resource available Departmentof Digital Business, Faculty of Economics and Business, Makassar State University, Makassar, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource June 22, 2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/f0ebcc06932c7bbf61b65b68dfbdd32f.pdf 54877d49de2f23442359b73d6703ec35 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 3 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Enhancing Stroke Prediction with Logistic Regression and Support Vector MachineUsing Oversampling Techniques Subject The topic of the resource grid search cross-validation; logistic regression; machine learning; stroke disease; support vector machine Description An account of the resource Stroke is a significant health concern that can result in both death and disability, making the early identification of risk factors crucial. Previous studies on stroke prediction have been limited by inadequate handling of class imbalance, lack of comprehensive feature selection, and parameter optimization, with accuracy ratesusually below 80%. This study compares the performance of Logistic Regression (LR) and Support Vector Machine (SVM) algorithms combined with different oversampling methods—SMOTE, Borderline-SMOTE, ADASYN, Random Over Sampling (ROS), and Random Under Sampling (RUS)—on a stroke prediction dataset. Correlation-based feature selection identified age, hypertension, and heart disease as significant predictors. GridSearchCV with 10-fold cross-validation was used for hyperparameter optimization, and performance was evaluated using precision, recall, accuracy, and ROC curves. The results showed that SVM significantly outperformed Logistic Regression across all sampling methods. SVM+ROS achieved the highest performance with perfect recall (100%), precision of 97.18%,and accuracy of 98.56% (AUC: 0.9857), whereas SVM + Borderline-SMOTE offered balanced performance with a recall of 94.99%, precision of 95.06%, and accuracy of 95.17% (AUC: 0.9512). LR + Borderline-SMOTE performed the best with an accuracy of 84.98% (AUC:0.8503), significantly better than previous studies. This improved accuracy shows significant clinical benefits, potentially reducing missed stroke diagnoses by identifying thousands of additional at-risk patients in large-scale screening programs. Healthcare providers should consider implementing SVM with ROS in critical care settings, where potentially missed stroke cases have severe consequences. Simultaneously, SVM with Borderline-SMOTE may be more appropriate for resource-constrained environments. Creator An entity primarily responsible for making the resource Syamsul Risal1*, Fajar Apriyadi2, A. Sumardin3, Andini Dani Achmad4, Annisa Nurul Puteri5 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6431/1089 Publisher An entity responsible for making the resource available Department of Informatics, Universitas Teknologi Akba Makassar, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource June 22, 2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/2a0c36c807df92a3ace4ace9b9b497a3.pdf 54877d49de2f23442359b73d6703ec35 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 3 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A Multi-Objective Particle Swarm OptimizationApproach for Optimizing K-Means Clustering Centroids Subject The topic of the resource centroid; k-means; multiobjective particle swarm optimization; the sum of square within; the sum of square between Description An account of the resource The K-Means algorithm is a popular unsupervised learning method used for data clustering. However, its performance heavily depends on centroid initialization and the distribution shape of the data, making it less effective for datasets with complex or non-linear cluster structures. This study evaluates the performance of the standard K-Means algorithm and proposes a Multiobjective Particle Swarm Optimization K-Means (MOPSO+K-Means) approach to improve clustering accuracy. The evaluation was conducted on five benchmark datasets: Atom, Chainlink, EngyTime, Target, and TwoDiamonds. Experimental results show that K-Means is effective only on datasets with clearly separated clusters, such as EngyTime and TwoDiamonds, achieving accuracies of 95.6% and 100%, respectively. In contrast, MOPSO+K-Means achieved a substantial accuracy improvement on the complex Target dataset, increasing from 0.26% to 59.2%. The TwoDiamonds dataset achieved the most desirable trade-off: it had the lowest SSW (1323.32), relatively high SSB (2863.34), and lowest standard deviation values, indicating compact clusters, good separation, and high consistency across runs. These findings highlight the potential of swarm-based optimization to achieve consistent and accurate clustering results on datasets with varying structural complexity Creator An entity primarily responsible for making the resource Aina Latifa Riyana Putri1*, Joko Riyono2, Christina Eni Pujiastuti3, Supriyadi4 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6533/1086 Publisher An entity responsible for making the resource available Data Science, Telkom University, Purwokerto, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource June 21, 2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT