2 10 14 https://repository.horizon.ac.id/files/original/8bb4e3ee9123db3a0557545e5a4d012f.pdf 7ea8dc7dc9bfe93424150aa02fa3a427 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 8 No 3 (2024) 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 Performance of KNN and C4.5 using Particle Swarm Optimization in Classification of Heart Diseases Subject The topic of the resource machine learning;classification;improving;performance;heart disease Description An account of the resource Heart disease is a majorproblem that must be overcome for human life. In recent years, the volume of medical data related to heart disease has increased rapidly, and various heart disease data have collaborated with information technology such as machine learning in detecting, predicting,and classifying diseases.This research aimsto improve the performance of machine learning classification methods, namely K-Nearest Neighbor (KNN) and Decision Tree (C4.5) with the particle swarm optimization(PSO) feature in cases of heart disease. In this research, a comparison was made of the performance of the PSO-based K-NN and C4.5 algorithms. Following experiments employing PSO optimization to improvethe K-NN and C4.5 algorithms, the findings indicated that the K-NN algorithm performed exceptionally well with PSO, achieving an accuracy of 89.09%, precision of 89.61%, recall of 90.79%, and an AUC value of 0.935 Creator An entity primarily responsible for making the resource Pareza Alam Jusia1, Abdul Rahim2, Herti Yani3, Jasmir Jasmir4 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5710/933 Publisher An entity responsible for making the resource available <span>Informatic Engineering Universitas Dinamika Bangsa, Jambi, Indonesia</span> Date A point or period of time associated with an event in the lifecycle of the resource 01-06-2024 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/3ffc3cebc5e9f7b8a2f927f2fb208601.pdf a8f8f67db874af5dfd28c9cbcaefafc8 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 8 No 3 (2024) 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 Remote Sensing Scene Classification using ConvNeXt-Tiny Model with Attention Mechanismand Label Smoothing Subject The topic of the resource remote sensing scene classification;deep learning; ConvNeXt-Tiny, ECANet; label smoothing regularization Description An account of the resource Remote Sensing Scene Classification (RSSC) is the discrete categorization of remote sensing images into various classes of scene categories based on their image content. RSSC plays an important role in many fields, such as agriculture, land mapping, and identification of disaster-prone areas. Therefore, a reliable and accurate RSSC algorithm is required to ensure the accuracy of land identification. Many existing studies in recent years have used deep learning methods, especially CNN combined with attentionmodules to solve this problem. This study focuses on solving the RSSC problem by proposing a deep learning-based method (CNN) with a ConvNeXt-Tiny model integrated with the Efficient Channel AttentionModule (ECANet)and label smoothing regularization (LSR).The ConvNeXt-Tiny model shows that a persistent superior outperforms the ‘large’ model in convinced metrics.ConvNeXt-Tiny model also has a huge advantage in high-precision positioning and higher classification accuracy and localization precision in a variety of complicated scenarios of remote sensing scene recognition.Experiments in this study also aim to prove that the integration of the attention module and LSR in the basic CNN network can improve accuracy because the attention module can strengthen important features and weaken features that are less useful for classification. The experimental results proved that the integration of ECANetand LSR in the ConvNeXt-Tiny base network obtained a higher accuracyof 0.38% in the UC-Merced dataset, 0.7% in the AID, and 0.4% in the WHU-RS19dataset than the ConvNeXt-Tiny model without ECANetand LSR. The ConvNeXt-Tiny model with ECANetintegration and LSR obtained an Accuracyof 99.00±0.41% in the UC-Merced dataset, 95.08±0.20% in AID, and 99.50±0.31% in the WHU-RS19 dataset Creator An entity primarily responsible for making the resource Rachmawan Atmaji Perdana1*, Aniati Murni Arimurthy2, Risnandar Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5731/940 Publisher An entity responsible for making the resource available Computer Science, Faculty of Computer Science, University of Indonesia, Depok, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 21-06-2024 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/06447dff592adeea235b629e5e8eaeb0.pdf 9c15847bceb03a2743c7cb2a0dc4edf4 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 8 No 3 (2024) 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 Convolutional Neural Network and LSTM for Seat Belt Detection in Vehicles using YOLO3 Subject The topic of the resource intelligent systems; seat belt violation detection; yolo; convolutional neural networks, LSTM Description An account of the resource The application of an electronic violation detection system has begun to be implemented in many countries by utilizing CCTV cameras installed at highway and toll road points. However, the development of a violation detection system using data in theform of images that have a high level of accuracy is still a challenge for researchers. Several types of violations detected include the use of seat beltsand the use of cell phones while driving which is influenced by the number of vehicles, vehicle speed and lightingwhich can increase the difficulty in the detection process. This research developed a traffic violation detection system usingYOLO3. The YOLOis used as the basic architecture of CNN which is then combined withLSTM. The dataset was obtained from RoboFlow Universe with a total of 199 front-viewcar images consistingof 82 using seatbelts and 78 not using seatbelts for the training process. The CNN algorithm plays a role in the feature extraction process from input image data, while LSTM plays a role in the prediction process. Furthermore, the performance evaluation of the CNN+LSTM algorithm will be measured using the value of accuracy to measure the performance of the training process and testing process. In measuring the performance of the training process,it will be compared with several basic detection models used, such as CNN, VGG16, ResNet50, MobileNetV2, YOLO3, and YOLO3+LSTM. The test results show that YOLO3+LSTM has higher accuracy compared to the others at 89%. Next, in the testing process, the CNN+LSTMmodel will be compared with the basic method, namely CNN. The test results show that the CNN+LSTM models havehigher accuracy at 89%. Meanwhile, in the basic CNN model, the resulting accuracy was 85% Creator An entity primarily responsible for making the resource Erika Devi Udayanti1,Etika Kartikadarma2, Fahri Firdausillah Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5784/936 Publisher An entity responsible for making the resource available Facultyof Computer Science, Universitas Dian Nuswantoro, Semarang, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 04-06-2024 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/eda727feadfb6d84b90f2b3f35c3adea.pdf 7f3fc2d68d39a446a5b5674836371027 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 8 No 3 (2024) 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 Middleware Applications Design for Health Information Sharing Subject The topic of the resource healthcare;data sharing;information systems;PHR application;distributed systems Description An account of the resource The interoperability between electronic health records (EHR) and electronic medical records (EMR) from various healthcare facilities for comprehensive patient care is important. However, integrating such systems, including the need for interoperability standards, data privacy, and security, is a highly challenging task, especially since patient rights in data access must be considered. The primary problem addressed is the challenge of integrating electronic health records (EHR) and electronic medical records (EMR) across various healthcare facilities to ensure comprehensive patient care while maintaining data privacy, security, and adherence to patient rights. This work presents an innovative application to consolidate patient health records from various medical facilities. It facilitates seamless data access, improving the efficiency of healthcare delivery. The GGD approach was used in developing the prototype to ensure the delivered product was able to fulfilthe user requirements. Four phases aredivided into six stages used in this method: research, modelling, requirements definition, framework definition, refinement, and support. The evaluation involved two phases, back-end and front-end testing, utilizing white box and black box testing. Whitebox testingdelivers the average frame rendering rate of up to 56 fps, and blackbox testing has shown 100% successful results in the given task. In conclusion, the Med-OID prototype was successfully developed. It integrates and securely transmits medical records across various healthcare services well, demonstrating significant potential to enhance personalized medicine and healthcare coordination. The evaluations underscored the prototype’s robustness and its capability to improve interoperability and data sharing inhealthcare systems. Creator An entity primarily responsible for making the resource Ketut Agus Seputra1,A.A. Gede Yudhi Paramartha2, Gede Aditra Pradnyana3, Kadek Yota Ernanda Aryanto4 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5707/932 Publisher An entity responsible for making the resource available Departmentof Informatics, Facultyof Engineering and Vocation, Universitas Pendidikan Ganesha, Singaraja, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 01-06-2024 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