1 10 33 https://repository.horizon.ac.id/files/original/1b639c7c0ee0477f8aa8c231b4d45a1f.pdf 22a4c1292d8807e903e5d0e868826211 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 5 (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 ResNet50-Driven Quality Inspection for Recorder Musical Instrument Subject The topic of the resource defect detection; musical instrument; neural network; recorder; ResNet50 Description An account of the resource The manufacturer of a recorder musical instrument requires high-quality product. The aim is to produce precise tones and an aesthetic look at customer satisfaction. A major challenge encountered by manufacturers is traditional visual inspection. Human error is a major factor, notably over extended work periods and the subjective judgment of quality control personnel. This paper reports on the development of a machine vision system for detecting abnormal patterns on the inner surface of a recorder musical instrument. An industrial-grade camera with a resolution of 1280 × 1024, paired with industrial lighting, was utilized. Due to its tube-shaped construction of the object, the bright-field imaging technique is applied to illuminate the interior. ResNet50 was selected as a feature extractor due to its balance between accuracy and efficiency. In addition, a Neural Network served as the classifier. A total of 1,118 images were collected as training data and 304 images as testing data. Thetraining and testing data were separate sets that were taken independently, preventing any risk of data leakage. The test results indicated that the model performed exceptionally well in classification, achieving an accuracy of 95.7%, precision of 95.45%,sensitivity of 96.07%, and specificity of 95.36%. Moreover, the area under the curve of the Receiver Operating Characteristic (ROC AUC) score in test data reached 0.9906, reflecting the model's ability to separate features from the two classes. These findings suggest that the proposed method offers an alternative to subjective visual inspection. Future research may examine diverse deep learning architectures to further enhance performance while achieving faster classification. Creator An entity primarily responsible for making the resource Rizki Putra Prastio1, Rodik Wahyu Indrawan2, Vanesia Tasib3, Zhilaan Abdurrasyid Rusmawan Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/7058/1131 Publisher An entity responsible for making the resource available Robotics and Artificial Intelligence Engineering, Faculty of Advanced Technology and Multidiscipline,Universitas Airlangga, Surabaya, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource September 29, 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/55d8fe4a298104df9a1a69bdbc65a6e6.pdf 22a4c1292d8807e903e5d0e868826211 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 5 (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 Addressing Class Imbalance in Oil Palm Disease and Micronutrient Deficiency Detection Using Meta-Learned Transfer Metric Learning Subject The topic of the resource class imbalance; MW-FixMatch; MetaTMLDA; oil palm; transfer metric learning; Description An account of the resource Class imbalance is a major challenge in oil palm disease and nutrient deficiency detection, where healthy samples dominate while diseased or deficient cases are underrepresented, often leading to biased models with high false-negative rates. To address this issue, this study proposes MetaTMLDA (Meta-Learned Transfer Metric Learning with Distribution Alignment), a hybrid framework that combines Transfer Metric Learning (TML) with MW-FixMatch. TML learns discriminative and domain-invariant features, while MW-FixMatch employs a meta-learned weighting mechanism to adaptively reweight samples, improving sensitivity to minority classes and enhancing robustness against pseudo-label noise. Experiments on four public datasets—Ganoderma Disease Detection, Palm Oil Leaf Disease, and Leaf Nutrient Detection for Boron and Magnesium—demonstrated that the proposed method consistently outperforms TML-DA, MW-FixMatch, SMOTE, Random Undersampling, and Biased SVM. On the smaller datasets (Ganoderma and Palm Oil Leaf Disease), MetaTMLDA achieved accuracy of 0.976, precision 0.951, recall 0.915, Cohen’s Kappa 0.912, and macro F1-score 0.933 for Ganoderma, and accuracy of 0.980, precision 0.972, recall 0.957, Kappa 0.911, and macro F1-score 0.964 for Palm Oil Leaf Disease. On the larger datasets (Boron and Magnesium), the model reached near-perfect accuracy of 0.995, with precision up to 0.967, recall up to 0.973, Kappa above 0.919, and macro F1-scores up to 0.969, highlighting its robustness and balanced predictive performance. These findings confirm that MetaTMLDA effectively addresses both class imbalance and domain shift, providing a scalable solution for precision agriculture through earlier and more reliable detection of oil palm health issues. Creator An entity primarily responsible for making the resource Hartono1, Erianto Ongko2 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6857/1168 Publisher An entity responsible for making the resource available Department of Informatics, Faculty of Engineering, Universitas Medan Area, Medan, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource October 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/ca8f23879d89482fbeaf0e4e7fc7e2cd.pdf 22a4c1292d8807e903e5d0e868826211 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 5 (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 Interdisciplinary Analysis of Machine Learning Applications: Focus on Intent Classification Subject The topic of the resource academic research, intent classification, language models; machine learning; natural language processing Description An account of the resource Given the rapid growth of machine learning publications on platforms such as arXiv, there is a need for systematic approachesto understand their objectives and contributions. This study aimed to analyze scientific intentions across domains, identify research trends, and evaluate the impact of external contextual enrichment on automatic intent classification. We perform a cross-domain comparison of research objectives, methodological designs, and application scenarios in machine learning publications, focusing on computer science and biology. We propose IntentBERT-Wiki, an enhanced BERT model enriched with contextual knowledge from Wikipedia, designed for intent classification in scientific documents. Our dataset comprises annotated sentences extracted from arXiv articles, categorized according to established rhetorical role taxonomies. The model’s performance is evaluated using standard classification metrics and compared to a baseline BERT model. Experimental results show that IntentBERT-Wiki achieves F1-scores of 95.9% in computer science and 87.4% in biology, with corresponding accuracies of 96.5% and 91.4%, outperforming the baseline. These findings demonstrate that Wikipedia-based contextual enrichment can significantly improve intent classification accuracy, enhance the organization of academic discourse, and facilitate cross-domain knowledge transfers. This study contributes to the understanding of how machine learning research is framed across disciplines and provides a scalable framework for scientific content analysis Creator An entity primarily responsible for making the resource Nabila Khouya1*, Asmaâ Retbi2, Samir Bennani3 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6899/1161 Publisher An entity responsible for making the resource available Rime Team, Mohammadia School of Engineers (EMI), Mohammed V University in Rabat, Morocco Date A point or period of time associated with an event in the lifecycle of the resource October 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/5f1e9d4077999f2df5b2387c282ae0f2.pdf 1d7f47f833375bbc82adf9380face182 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 5 (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 Optimization Techniques and Programming for Developing Cost-Effective and Balanced Diet Schedules for Preschoolers Subject The topic of the resource binary programming; integer programming; linear programming; nutrition; optimization Description An account of the resource Proper nutrition is important for the growth, motor and cognitive development of young children since the foods consumed determine how well-rounded a child's diet is. However, preschool menu planning is complex because it requires balancing multiple constraints such as cost, dietary guidelines, and food variety. This study introduces a computational approach to menu planning for preschools through Linear Programming (LP), Integer Programming (IP), and Binary Programming (BP). This study highlights algorithmic design, constraint modelling, and computational efficiency in solving optimization problems, rather than focusing primarily on dietary outcomes. The models were tested using Malaysian food database to evaluate both feasibility and efficiency. The findings indicate that all models successfully fulfilled the Recommended Nutrient Intakes (RNI 2017) for children aged 4 to 6, ensuring adequate levels of energy, protein, calcium, carbohydrates, and fat. In terms of cost, the LP model was the most economical at RM4.20 per day, but impractical due to fractional servings. The IP model produced a more realistic balance between cost and practicality at RM4.40 per day. The BP model generated the most diverse and implementable menus at RM5.00 per day, though at a higher cost. Overall, these optimization methods provide decision-support tools for enhancing the efficiency of preschool menu planning. Creator An entity primarily responsible for making the resource Norliana Mohd Lip1, Suliadi Firdaus Sufahani2, Nur Islami Mohd Fahmi Teng3 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6650/1160 Publisher An entity responsible for making the resource available Department of Mathematics, Faculty of Computer and Mathematical Sciences, University Teknologi MARA, Negeri Sembilan, Malaysia Date A point or period of time associated with an event in the lifecycle of the resource October 26, 2025 Contributor An entity responsible for making contributions to the resource FAAJAR 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/8db5f07f470ad1db6f20d0044e97e24f.pdf 22a4c1292d8807e903e5d0e868826211 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 5 (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 Optimization of Accuracy Improvement through Modified ShuffleNet Architecture in Rice Classification Subject The topic of the resource CNN model; optimization; MobileNet; rice classification; RestNet; ShuffleNe Description An account of the resource Accurate rice classification is essential to determine the quality and market value of rice. Traditional methods of rice classification are often time-consuming and error-prone, so a more efficient and accurate solution is needed. This study aims to optimize rice classification using Convolutional Neural Networks (CNN) combined with the ShuffleNet architecture, which offers high computational efficiency without sacrificing accuracy. The dataset used comes from Kaggle, containing 8750 rice grain images divided into five classes: Arborio, Basmati, Ipsala, Jasmine, and Karacadag. The uniqueness of this study is the application of ShuffleNet Proposed in rice classification, which provides improved performance compared to basic CNN models such as MobileNet, ShuffleNet, and RestNet. The results showed that the MobileNet model achieved 80% accuracy, RestNet 94%, and ShuffleNet achieved 100% accuracy with precision, recall, and F1 values also 100%. However, the ShuffleNet model experienced overfitting when tested with new data, resulting in an accuracy of only 20%. To overcome this, further optimization was carried out on the model. The results of statistical tests (paired t-test and Wilcoxon test) show significant differences between ShuffleNet Proposed and other models, which proves that the improvements applied to this model provide significant improvements. The implications of this study can improve the efficiency and accuracy of rice classification, which has the potential to improve the quality and market value of rice in the agricultural industry Creator An entity primarily responsible for making the resource Abdullah Ahmad1, Dedy Hartama2, Agus Perdana Windarto3, Anjar Wanto Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6411/1159 Publisher An entity responsible for making the resource available Student of Magister Informatika, STIKOM Tunas Bangsa, Pematangsiantar City, North Sumatra, IndonesIA Date A point or period of time associated with an event in the lifecycle of the resource October 26, 2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGSU 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/d642be127c7070515728c7339a29b36b.pdf daf431ddd69811b17d22fbe617914d20 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 5 (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 Improving Classification Performance on Imbalanced Stroke Datasets Using Oversampling Techniques Subject The topic of the resource borderline-SMOTE; imbalance data; SMOTE; stroke prediction; XGBoost method Description An account of the resource Stroke is the second leading cause of death globally and significantly contributes to long-term disability. While machine learning techniques have been increasingly used for early stroke detection, their performance is often limited by imbalanced data distributions that bias classification outcomes. This study aims to investigate the effectiveness of three oversampling techniques—SMOTE, Borderline-SMOTE, and SVM-SMOTE—in improving stroke classification performance on imbalanced datasets. Oversampling methods are applied to balance class distributions, followed by the implementation of Random Forest and XGBoost classifiers for stroke prediction. Experimental results demonstrate that oversampling techniques substantially improve classification performance, particularly in the Matthews Correlation Coefficient (MCC) and Area Under the Curve (AUC) metrics. Among the tested methods, Borderline-SMOTE yields the best performance, achieving accuracies of 96.45% with Random Forest and 96.41% with XGBoost. Moreover, it increases MCC by 87.51% and AUC by 45.40% for Random Forest, and MCC by 76.52% and AUC by 41.81% for XGBoost, compared without oversampling. The results demonstrate that Borderline-SMOTE effectively addresses data imbalance, enhances model robustness, and improves the detection of minority stroke cases in classification task Creator An entity primarily responsible for making the resource Muhammad Innuddin1*, Hairani Hairani2, M. Thonthowi Jauhari3, Lalu Zazuli Azhar Mardedi4 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6859/1158 Publisher An entity responsible for making the resource available Department of Computer Science, Faculty of Engineering, Universitas Bumigora, Mataram, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource October 26, 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/48f8c8f719ead6970081ebc2b5ef9e93.pdf daf431ddd69811b17d22fbe617914d20 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 5 (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 From Serial to Parallel: Enhancing Needleman-Wunsch Performance through GPU-Based Computing Subject The topic of the resource global sequence alignment; GPU computing; Needleman-Wunsch algorithm Description An account of the resource The increasing demand for faster bioinformatics analysis calls for more efficient approaches for sequence alignment. In this study, we demonstrate that a GPU-based implementation of the Needleman-Wunsch algorithm can achieve up to 14.8× speedup compared to its traditional CPU-based serial counterpart, without compromising alignment accuracy. By leveraging the parallel processing capabilities and shared memory of an NVIDIA GeForce RTX 3060 Laptop GPU, we significantly accelerated global sequence alignment tasks. Using clinically relevant genes such as NRAS, BRCA1, BRCA2, and Saccharomyces cerevisiae from NCBI ensures realistic alignment challenges and biological significance. Performance evaluation across a wide range of sequence lengths demonstrates the scalability and efficiency of the parallel approach. More importantly, this study provides a unique contribution by showing that a commodity GPU, such as the NVIDIA GeForce RTX 3060 Laptop, can serve as a practical alternative when high-performance computing clusters are unavailable or prohibitively expensive, thereby offering an accessible and cost-effective pathway to high-throughput bioinformatics workflows. Creator An entity primarily responsible for making the resource Yustina Sri Suharini1, Wisnu Ananta Kusuma2, Sri Nurdiati3, Irmanida Batubara4 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6620/1156 Publisher An entity responsible for making the resource available 2Department of Computer Science, School of Data Science, Mathematics and Informatics, IPB University, Bogor, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource October 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/f0c10e0bd270ea432569f5439a608ba8.pdf daf431ddd69811b17d22fbe617914d20 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 5 (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 Sentiment Classification on Indodax Using Term Frequency, FastText, and Neural Attention Models Subject The topic of the resource fasttext embedding; hybrid feature representation; lexicon-based labeling; sentiment analysis; BiLSTM attention Description An account of the resource The rapid growth of mobile-based investment platforms such as Indodax has triggered a surge in user-generated reviews that reflect public perception and sentiment. This study aimed to develop and evaluate sentiment classification models that can accurately classify Indonesian user reviews on the Indodax app into negative, neutral, and positive sentiments. A dataset of 11,000 reviews was collected via web scraping from the Google Play Store. Reviews were preprocessed, labeled using a lexicon-based unsupervised method, and balanced using oversampling. Two models were built: a Bidirectional LSTM (BiLSTM) with attention mechanism using FastText embeddings, and a Feedforward Neural Network (FFNN) using a hybrid feature vector combining TF-IDF and FastText. The evaluation was performed using accuracy, classification report, confusion matrix, and PCA visualization. The FFNN model outperformed the BiLSTM-Attention model with an accuracy of 97.07% compared to 96.00%. Both models demonstrated strong performance in classifying three sentiment classes, though the FFNN showed better separation in PCA space and higher macro-average metrics. This study demonstrates the effectiveness of combining statistical and semantic feature representations for sentiment classification in Indonesian text. The proposed approach is particularly valuable for low-resource languages and informal user-generated content Creator An entity primarily responsible for making the resource Dedy Hartama1, Ginanti Riski2 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6871/1155 Publisher An entity responsible for making the resource available Department of Information Systems, 2Department of Informatics Engineering, STIKOM Tunas Bangsa, Pematangsiantar, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource October 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/106e4ef0d40fa154891c86dfdcf99598.pdf daf431ddd69811b17d22fbe617914d20 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 5 (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 Hyperparameter Tuning with Optuna to optimize the YOLOv11n Model for Weed Detection Subject The topic of the resource image augmentation; Optuna; residential weed detection; YOLO nano; YOLOv11n Description An account of the resource Accurate weed detection is essential for maintaining the cleanliness and aesthetic appeal of residential yards. This study aimed to optimize YOLOv11n, a lightweight object detection model, to achieve high precision in weed identification under real-world conditions. The novelty of this study lies in the application of Optuna, an automatic hyperparameter optimization framework, to enhance model performance while maintaining computational efficiency for resource-limited devices such as drones and IoT systems. The research involved data augmentation techniques including crop (0–20% zoom), hue (±20°), saturation (±30%), brightness (±20%), exposure (±15%), and mosaic augmentation. These augmented images were used to train four YOLO nano variants (v5n, v8n, v11n, v12n), which were evaluated using standard metrics: Precision, Recall, F1-Score, and mean Average Precision (mAP). Among the models tested, YOLOv11n with Custom Optuna configuration delivered the highest performance, achieving a 94.6% F1-score and 97.8% mAP@0.5. These results demonstrate that the optimized YOLOv11n model can support accurate and efficient real-time weed detection in household environments, particularly on edge devices with limited hardware capabilities. This makes it a viable solution for practical implementation in precision agriculture and smart gardening. Creator An entity primarily responsible for making the resource Candhy Fadhila Arsyad1, Pulung Nurtantio Andono2, Moch Arief Soeleman Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6682/1153 Publisher An entity responsible for making the resource available Faculty of Computer Science, Universitas Dian Nuswantoro, Semarang, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource [October 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/dcd82b105c4c5a809c52c6c9c1070cc3.pdf daf431ddd69811b17d22fbe617914d20 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 5 (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 Early Stroke Disease Prediction Based on Lifestyle Factors Applied with Machine Learning Subject The topic of the resource lifestyle; machine learning; preprocessing; stroke; variable reduction Description An account of the resource Stroke prediction has many supporting features and variables. Some forecasts focus more on health or elements that are already present. Predicting stroke risk by identifying habitual factors provides more advantages for preventive action. In addition, the complexity of features or variables is a concern in predicting stroke risk. In this study, we used a public dataset from Kaggle with 10 features or variables. In this study, we propose to collaborate algorithms and preprocessing in feature selection using Pearson Correlation and Principal Component Analysis (PCA) dimension reduction to unravel the complexity of variables and data processing computing. This aims to predict stroke risk more simply. The results of the experiment show that feature selection using Pearson Correlation between features and labels produces maximum results using 5 features out of 10 provided features. This approach produces the best performance on the Naïve Bayes, Iterative Dichotomiser Tree (ID3), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Logistic Regression with 100% accuracy and reduces features by 50% to support the reduction of the complexity of prediction variables and data processing computing Creator An entity primarily responsible for making the resource Suastika Yulia Riska1, Lia Farokhah Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6495/1152 Publisher An entity responsible for making the resource available 2Informatic Departement, Technology and Desain Faculty, Institut Teknologi dan Bisnis Asia, Malang, IndonesiA Date A point or period of time associated with an event in the lifecycle of the resource October 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