1 10 33 https://repository.horizon.ac.id/files/original/89f12ffcce44df8c9275c3d759e0cb04.pdf cdaf471db42c7d3883280f0727092031 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 A Comparative Evaluation of YOLOv9 and DETR Models in Traffic Object Detection for Intelligent Surveillance Systems Subject The topic of the resource intelligent systems; DETR; object detection; YOLOv9; traffic surveillance Description An account of the resource Object detection plays a crucial role in traffic surveillance, particularly in urban environments characterized by high vehicle density, diverse weather conditions, and limited computational resources. Although YOLOv9 and DETR have demonstrated strong performance in general object detection tasks, there is a lack of comparative research evaluating their effectiveness under specific challenges of traffic surveillance. These challenges include the need for real-time processing, accurate detection of small or partially occluded objects, and adaptability to complex traffic scenarios. This study addresses this gap by conducting a comparative evaluation of YOLOv9 and DETR using a custom traffic image dataset, with training iterations varied from 10 to 50 epochs to observe performance development. Evaluation metrics included mean average precision, precision, recall, F1-score, inference time, and object count per image. The results indicated that DETR achieved the highest accuracy across all metrics at the final training stage and detected up to 22 objects per image. However, the average inference time exceeded seven seconds per image, limiting the real-time applicability. Conversely, YOLOv9 achieved competitive accuracy with a significantly faster inference time of approximately 0.43 seconds per image. These findings provide practical insights into the trade-off between accuracy and processing efficiency, and offer guidance for model selection in operational traffic surveillance systems Creator An entity primarily responsible for making the resource Mohamad Jamil1, Nani Nagu, Muhammad Said Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6556/1169 Publisher An entity responsible for making the resource available Department of Informatics Engineering, Universitas Khairun, Ternate, Maluku Utara, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource Department of Informatics Engineering, Universitas Khairun, Ternate, Maluku Utara, Indonesia 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/8fb109fa1efa6030fbdae1834356c91f.pdf 9ae3c716129b1affc03efe1fc8989060 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 A Data-Driven Comparison of Linear Mixed Model and Mixed Effects Regression Tree Approaches for Dairy Productivity Analysis Subject The topic of the resource hierarchical data; indonesian dairy cow milk productivity survey 2024; linear mixed model (LMM); mixed effects regression tree (MERT); SDGs: goal 2 and 3 Description An account of the resource Dairy productivity studies often involve hierarchical and longitudinal data structures that violate the assumptions of linearregression. This study compares two modeling approaches, Linear Mixed Model (LMM) and Mixed Effects Regression Tree (MERT), in predicting dairy productivity based on the 2024 National Dairy Productivity Survey data. Predictive performance was evaluated using MSEP, RMSEP, MAD, and MAPE, with MERT consistently outperforming LMM in accuracy and robustness. Permutational Multivariate Analysis of Variance (PERMANOVA) test results reinforced this finding, yielding a pseudo-F value of 224.7 and a p-value of 0.001, indicating statistically significant differences in model performance. Key predictors of MERT model included farm altitude, the previous week’s milk production, and the amounts of concentrate feed given, which are part of significant predictor variables in LMM. This finding underscores MERT’s superiority in modeling complex agricultural datasets while providing interpretable insights through data-driven segmentation. The study advocates policy focus in sustainable milk production as well as the availability of high quality of feed and altitude-based dairy farms location to improve milk productivity. Should these focuses implemented by the industry, combined with the MBG Program, Indonesia would be progressing better towards achievement of SDGs Goal 2 and 3. Creator An entity primarily responsible for making the resource Achmad Fauzan1,2, Fatkhurokhman Fauzi3,4, Rhendy K P Widiyanto5,6Khairil Anwar Notodiputro7, Bagus Sartono8 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6751/1144 Publisher An entity responsible for making the resource available Study Program of Statistics and Data Science, School of Data Science, Mathematics and InformaticsIPB University, Bogor, Indonesia Contributor An entity responsible for making contributions to the resource October13, 2025 Format The file format, physical medium, or dimensions of the resource FAJAR BAGUS W 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/668b38b110bf700c267726247870c007.pdf dbf91f97f5a1e0b132ff0ac0575a000c 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 Application of Reinforcement Learning to Solve Rubrik’s Cube with Markov Decision Process Subject The topic of the resource Reinforcement Learning, Rubik’s Cube; Markov Decision Process Description An account of the resource The Rubik's Cube is a tricky puzzle that can be arranged in countless ways, making it hard for both people and computers to figure out. While standard solving methods use fixed strategies, this research looks into using reinforcement learning (RL) to create a flexible and effective way to solve it. The goal of this research is to develop an RL-based solver that uses the Markov Decision Process (MDP) system, focusing on speed, efficient moves, and the number of steps needed to solve the cube. The suggested model uses Q-learning and Monte Carlo Tree Search (MCTS) to figure out the best moves at each stage of the game, training through lots of Rubik's Cube simulations. What makes this research unique is the combination of MCTS with Q-learning, which improves decision-making by needing fewer moves than standard methods. The tests show that this model reaches almost perfect solutions with fewer moves, doing better than simple rule-based methods. Also, a web app was created to give live solving techniques based on the cube arrangements that users provide. This research helps grow the use of RL in puzzles like the Rubik's Cube and gives a useful tool for fans who want to get better at solving the cube Creator An entity primarily responsible for making the resource Defni1, Andi Fathul Mukminin2, Ainil Mardhiah3, Titin Ritmi4, Junaldi5, Yuhefizar6, Fibriyanti7 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6552/1135 Publisher An entity responsible for making the resource available Information Technology, Padang State Polytechnic, West Sumatera, Indonesi Date A point or period of time associated with an event in the lifecycle of the resource October 8, 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/a3237ea323d04e935a24d44120fad844.pdf 82cb736ded51dd0e8ad0a4ec67a26428 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 Application of YOLOv8 Algorithm for Coral Reef Disease Detection as an Effort to Prevent Marine Habitat Damage in Batam Subject The topic of the resource coral; detection; disease; YOLOv8 Description An account of the resource Research in 2019 in Batam City showed that out of 19 coral reef fisheries support facilities, 16 were declared not good. Coral reef damage increased from 36.28% to 39.44%. This is due to the threat of coral reef damage due to international shipping lane areas, human activities such as destructive fishing, pollution, sedimentation, and global warming. These threats can cause coral diseases such as black band disease (BBD), brown band disease (BrB), Bleaching Coral, and yellow band disease (YBD). The Underwater Photo Transect (UPT) method collects data in the field in the form of underwater photos and analyzes them to obtain quantitative data. This method has a weakness, namely the low level of accuracy in detecting coral reef diseases. This study proposes coral reef disease detection using the YOLO model YOLO8l, YOLO8x, and YOLO8m. The results of the model evaluation test with a threshold value of 0.5 to 0.95 against the test data show that the three models can detect coral reef diseases with an accuracy of 99%. These results prove that the YOLOv8 model in this study is suitable for the real-time detection of coral reef diseases to replace the Underwater Photo Transect (UPT) method, which has low accuracy. Applying the YOLOv8 method will help Prevent Marine Habitat Damage in Batam City Creator An entity primarily responsible for making the resource Rifa’atul Mahmudah Burhan1, Refli Noviardi2, M Abrar Masril3, Firmansyah Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6062/1145 Publisher An entity responsible for making the resource available Information System, Information Technology, Batam Institute of Technology, Batam, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource October 13, 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/8e18baaaf92e28a08d24f4b673ef0014.pdf 9ae3c716129b1affc03efe1fc8989060 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 Automated Ripeness Detection of Oil Palm Fruit Using a Hybrid GLCM-HSV-KNN Model Subject The topic of the resource HSV: GLCM; KNN; image processing; oil palm fruit ripeness Description An account of the resource Accurately determining the ripeness of oil palm fruit is crucial for ensuring the quality of palm oil. However, traditional manual methods are often time-consuming and less accurate. This study aimed to develop an automated system for detecting the ripeness of oil palm fruit by combining the Hue Saturation Value (HSV) model, Gray Level Co-occurrence Matrix (GLCM), and K-Nearest Neighbor (KNN) algorithms. This system utilizes K-Nearest Neighbors to classify the relationship between color features extracted using the HSV model and texture features derived from GLCM analysis to categorize fruit ripeness. The color features represent the fruit's chromatic characteristics associated with ripeness, while the texture features provide information regarding surface patterns related to ripeness. The color features represent the fruit's color characteristics associated with ripeness, whereas the texture features provide information about the surface patterns related to ripeness. The results indicate that the system can classify oil palm fruit into four distinct categories: Over-Ripe, Ripe, Half-Ripe, and Raw. The dataset was divided with an 80:20 ratio, where 80% was allocated for training data and the remaining 20% for test data. An accuracy rate of 85% was achieved. The results of this study demonstrate that the developed system effectively classifies oil palm fruit images based on ripeness levels. This system supports a sustainable automated palm oil production model through accurate ripeness detection, thereby reducing reliance on manual methods and enhancing consistency and productivity in palm oil processing. These findings indicate that the proposed hybrid method is feasible for integration into an automated classification system to support decision-making in oil palm harvesting. Creator An entity primarily responsible for making the resource Mirfan1, Billy Eden William Asrul2, Mila Jumarlis3, Juliani4 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6683/1143 Publisher An entity responsible for making the resource available Department of Computer Science, Faculty of Computer Science, Universitas Handayani Makassar, Makassar, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource October 12, 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/21e42433a211ee46ec348000a9675eb0.pdf 1f7b4842f4f6c4b3afb8bf897934c73e 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 Breast Cancer Histopathological Image Classification with Convolutional Neural Networks Models Subject The topic of the resource breast cancer; histopathological image classification; deep learning; convolutional neural network; support vector machine Description An account of the resource Early diagnosis and treatment can reduce mortality rates by preventing the progression of breast cancer. Owing to convolutional neural networks (CNN),breast cancer diagnosis can be performed faster and more objectively than humans using thousands of histopathological images. This study aimed to evaluate and compare the rapid and effective diagnostic performance of CNN models on breast tumor images, utilizing transfer learning through pre-training and fine-tuning on novel datasets. The study was performed in two ways on BreakHis and BACH datasets. First, fine-tuned VGG16, VGG19, Xception, InceptionV3, ResNet50, and InceptionResNetV2 models were used for classification. Second, these CNN models were used as feature extractors and support vector machines (SVMs) as classifiers. The success of all models in tumor classification was interpreted using performance metrics, such as accuracy, precision, recall, F1 score, and AUC. The models showing the best performance as a result of the analyses were as follows: InceptionResNetV2+SVM model with an accuracy of 99.3%, precision of 99.0%, recall of 100.0%, F1 score of 99.5%, AUC of 98.9% for BreakHis dataset; and InceptionResNetV2 model with accuracy of 96.7%, precision of 93.8%, recall of 100.0%, F1 score of 96.8%, AUC of 96.7% for the BACH dataset. As a conclusion, it has been seen that the CNN methods have good generalization abilities and can respond to clinical needs. Creator An entity primarily responsible for making the resource sil Unaldi1, Leman Tomak Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6420/1130 Publisher An entity responsible for making the resource available Department of Biostatistics and Medical Informatics, Faculty of Medicine, Ondokuz Mayis University, Samsun, Türkiye Date A point or period of time associated with an event in the lifecycle of the resource September29, 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/9da6740c76ded7220b2837976ea8a603.pdf 1f7b4842f4f6c4b3afb8bf897934c73e 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 Breast Cancer Histopathological Image Classification with Convolutional Neural Networks Models Subject The topic of the resource breast cancer; histopathological image classification; deep learning; convolutional neural network; support vector machine Description An account of the resource Early diagnosis and treatment can reduce mortality rates by preventing the progression of breast cancer. Owing to convolutional neural networks (CNN),breast cancer diagnosis can be performed faster and more objectively than humans using thousands of histopathological images. This study aimed to evaluate and compare the rapid and effective diagnostic performance of CNN models on breast tumor images, utilizing transfer learning through pre-training and fine-tuning on novel datasets. The study was performed in two ways on BreakHis and BACH datasets. First, fine-tuned VGG16, VGG19, Xception, InceptionV3, ResNet50, and InceptionResNetV2 models were used for classification. Second, these CNN models were used as feature extractors and support vector machines (SVMs) as classifiers. The success of all models in tumor classification was interpreted using performance metrics, such as accuracy, precision, recall, F1 score, and AUC. The models showing the best performance as a result of the analyses were as follows: InceptionResNetV2+SVM model with an accuracy of 99.3%, precision of 99.0%, recall of 100.0%, F1 score of 99.5%, AUC of 98.9% for BreakHis dataset; and InceptionResNetV2 model with accuracy of 96.7%, precision of 93.8%, recall of 100.0%, F1 score of 96.8%, AUC of 96.7% for the BACH dataset. As a conclusion, it has been seen that the CNN methods have good generalization abilities and can respond to clinical needs Creator An entity primarily responsible for making the resource Isil Unaldi1, Leman Tomak2 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6420/1130 Publisher An entity responsible for making the resource available Department of Biostatistics and Medical Informatics, Faculty of Medicine, Ondokuz Mayis University, Samsun, Türkiye Date A point or period of time associated with an event in the lifecycle of the resource September29, 2025 Contributor An entity responsible for making contributions to the resource Isil Unaldi1, Leman Tomak Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource INDONESIA Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/7d3c07442f3403f16e31d8f83c7c32c6.pdf 828c14bd5ba79a7d17cb41391a0ae934 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 Comparing Data Preprocessing Strategy on T5 Architecture to Classify ICD-10 Diagnosis Subject The topic of the resource ICD-10 classification; medical NLP; preprocessing strategy; T5; text-to -text transforme Description An account of the resource Manual ICD-10 coding in healthcare systems remains time-consuming, error-prone, and inefficient, particularly in resource-constrained settings. This study investigates the effect of various preprocessing strategies on the performance of the Text-to-Text Transfer Transformer (T5) model for primary diagnosis classification using structured clinical data. A total of 7,263 clinical records were collected from two high-density regions in Bali (Badung and Gianyar) between January 2023 and March 2024, then converted into descriptive text prompts for model training. Four experimental scenarios combined variations of input features and label configurations, comparing T5 with Oversampling against T5 with Easy Data Augmentation (EDA) plus Oversampling. Results showed that T5 with Random Oversampling consistently outperformed the EDA-based configuration across all scenarios, with performance gaps ranging from 8% to 19%. Scenario 4, which excluded body system features and the semantically overlapping E860 label, achieved the highest balance, reaching 84.7% accuracy, 85.1% precision, 84.7% recall, and 84.3% F1-score. Conversely, the EDA-based approach reduced training time by up to 72%, indicating a clear trade-off between performance and efficiency. Both configurations frequently misclassified semantically similar codes within the same ICD-10 categories, underscoring the difficulty of distinguishing clinically related diagnoses. Overall, the results suggest that careful selection of preprocessing strategies can enhance transformer-based medical text classification, while striking a balance between model performance and training efficiency. This work may serve as an initial reference for developing more efficient semi-automated medical coding systems in the Indonesian regional healthcare contex Creator An entity primarily responsible for making the resource I Gusti Ngurah Lanang Wijayakusuma1*, Made Sudarma2, I Ketut Gede Darma Putra3, Oka Sudana4, Ni Putu Dian Astutik5 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6919/1149 Publisher An entity responsible for making the resource available Department of Doctoral Engineering, Faculty of Engineering, Universitas Udayana, Bali, 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/becc675171323428bb62d75b69ed3cd1.pdf c4000e56e5041d65b9f7091a7ed9ac75 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 Detection of Grasserie Disease in Silkworms Using Computer Vision and Machine Learning Subject The topic of the resource grasserie; histogram oriented gradient; machine learning; sericulture; silkworm Description An account of the resource One of the major challenges associated with the sericulture industry is silkworm diseases, as they are very difficult to detect in the early stages. Timely identification of infected silkworms is essential to curb the spread of disease and reduce economic damage. This study focuses on diagnosing Grasserie disease, a highly contagious condition that can devastate silkworm populations, leading to substantial financial losses for farmers. To address the shortcomings of expert manual inspections, this study employed camera-captured images of silkworms for automated disease detection. A newly compiled dataset, consisting of 668 healthy silkworms and 574 infected with Grasserie disease are used for this study. The dataset is analyzed with machine learning techniques for image analysis, features are extracted from the pre-processed images using combining Histogram of Oriented Gradients (HOG) and the higher dimensional features are reduced with Kernel Principal Component Analysis (KPCA), and classification using supervised models. The results highlight the effectiveness of this approach in differentiating healthy silkworms from diseased ones. The machine learning model HOG integrated with KPCA and Decision Trees (DT) achieved strong performance, with accuracy, recall, and precision scores of 94.28%, 94.56%, and 92.48%, respectively. While these outcomes are encouraging, further research is needed to develop a practical IoT-based tool that enables sericulture farmers to quickly detect infections and take preventive measures, minimizing unexpected losses. This study marks a crucial advancement in silkworm disease detection, offering a pathway toward greater sustainability and economic stability in the sericulture sector Creator An entity primarily responsible for making the resource Sania Thomas1, Binson V A2*, Sini Rahuman3, Sivakumar K S4 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6705/1166 Publisher An entity responsible for making the resource available Department of Computer Science and Engineering, Saintgits College of Engineering, Kerala, India Date A point or period of time associated with an event in the lifecycle of the resource October 27, 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