1 10 5 https://repository.horizon.ac.id/files/original/a3738ffd195d0efb36fda1c8ab2273d5.pdf 2628e57d7d1e7d22b965480332d581d9 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. 6 NO. 1 (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 Enhancing Mental Health Disorders Classification UsingConvolutional Variational Autoencoder Subject The topic of the resource CVAE, Mental Health, Classification, Deep Learning Description An account of the resource This research investigates the application of Convolutional Variational Autoencoder (CVAE) for multi-class classification of mental health disorders. The study utilizes a diverse dataset comprising five classes: Normal, Anxiety, Depression, Loneliness, and Stress. The CVAE model effectively captures spatial dependencies and learns latent representations from the mental health disorder data. The classification results demonstrate high precision, recall, and F1 scores for all classes, indicating the model's robustness in distinguishing between different disorders accurately. The research contributes by leveraging the unique capabilities of CVAE, combining convolutional neural networks and variational autoencoders to enhance the accuracy and interpretability of the classification process. The findings highlight the potential of CVAE as a powerful tool for accurate and efficient mental health disorder classification. This research paves the way for further advancements in deep learning techniques, supporting improved diagnosisand personalized healthcare in mental health Creator An entity primarily responsible for making the resource Sri Hasta Mulyani1, Mohammad Diqi2,Annisa Rahmidini3, Erlin Ifadah4, I Wayan Ordiyasa5, Marselina Endah Hiswati Source A related resource from which the described resource is derived https://ijicom.respati.ac.id/index.php/ijicom/article/view/65/53 Date A point or period of time associated with an event in the lifecycle of the resource August 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/22ba7a2f5b3ba35cb8382bc42ca0252a.pdf 8219e87d5b98ac4e65e8e42fb574a492 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. 6 NO. 1 (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 Line Crossing Detector System for Real-TimeOver-Taking Vehicle Detection Subject The topic of the resource YOLOv8n, Vehicle Detection, Line Crossing Detector, CNN Description An account of the resource This study introduces a novel method for detecting overtaking vehicles by integrating Virtual Line Detection with the YOLOv8n algorithm. The objective is to enhance road safety by accurately identifying and tracking vehicles as they overtake, which is crucial for preventing. The research demonstrates the effectiveness of this approach, achieving a detection accuracy rate of 80.95% using line-crossingdetection techniques. This high level of accuracy underscores the potential of the system to reliably identify overtaking maneuvers in traffic conditions. Furthermore, this innovative method holds promising implications for enhancing safety riding by providing real-time alerts to drivers and preventing infrastructure loss resulting from traffic incidents. Ourfindings suggest that integrating advanced detection algorithms like YOLOv8n with virtual line detection can be a viable solution for modern traffic safety challenges Creator An entity primarily responsible for making the resource Ahmad Nanda Yuma Rafi1, Mohamad Yusuf Source A related resource from which the described resource is derived https://ijicom.respati.ac.id/index.php/ijicom/article/view/72/54 Date A point or period of time associated with an event in the lifecycle of the resource August 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/44237205be68b2a1994595543922d528.pdf 8df5e6980611504313f03952013c5877 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. 6 NO. 1 (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 Brain Tumor Detection and Classification Using Fine-Tuned CNN with ResNet50 and EfficientNet Subject The topic of the resource Brain Tumor, CNN, MRI, ResNet50, EfficientNet, Fine-Tune Description An account of the resource Brain tumors have become a leading cause of mortality worldwide. Detecting and classifying brain tumors accurately at the initial stages is challenging due to their complex and varying structure. In this study, an improved fine-tuned model based on Convolutional Neural Networks (CNN) with ResNet50 and U-Net is proposed. The model works on the publicly available TCGA-LGG and TCIA dataset, which consists of 120 patients. The fine-tuned ResNet50 model outperforms the CNN model in brain tumor classification anddetection using MRI images. Accurate and timely diagnosis of brain tumors is critical for successful treatment of the disease. Early detection not only aids in the development of better medication, but it can also save a life in the long run. The domain of brain tumor analysis has efficiently applied medical image processing ideas, particularly on MR images. This paper presents segmentation using Convolutional Neural Networks (CNN) architecture with ResNet50 and EfficientNet as backbones. Creator An entity primarily responsible for making the resource Muhammad Ali Sultan1, Christopher Marco Angelo2, Muhammad Alkam Alfariz3, Dinda Fatimah Kautsarina4Dhani Amanda Putra5, Muhammad Sharji Ashfaq6, Hadi Santoso7, Genoveva Ferreira Sores Source A related resource from which the described resource is derived https://ijicom.respati.ac.id/index.php/ijicom/article/view/80/55 Date A point or period of time associated with an event in the lifecycle of the resource August 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/6cb5a3a7df10cae95fb3e79662368cb3.pdf 09e2ef995736935b95c739a1138e063f 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. 6 NO. 1 (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 Stock Price Prediction in Indonesia's Mining Sector Using a Hybrid Conv1D-LSTM Model Subject The topic of the resource Deep Learning, Conv1D, LTSM, MiningStock Price,Prediction Description An account of the resource In the investment world, the ability to predict stock price movements is a key factor for success among investors and analysts. This study introduces a novel approach for forecasting stock prices in Indonesia's mining sector using a hybrid model that combines Convolutional Neural Networks (Conv1D) and Long Short-Term Memory (LSTM) networks. The volatile nature of stock markets and the unique characteristics of the mining industry demand accurate prediction models. Our research demonstrates that the Conv1D-LSTM model can extract patterns from stock price data more effectively than traditional models, thanks to Conv1D's feature extraction capabilities and LSTM's sequence learning strengths. By employing historical stock data from several leading mining companies in Indonesia, our model achieved a 15% higher prediction accuracy compared to conventional methods. These results highlight the significant potential of artificial intelligence in assisting investors to make more precise and informed decisions. We hope this research will pave the way for broader adoption of technology in the financial sector, especially in predicting complex and challenging market dynamics Creator An entity primarily responsible for making the resource Hamzah Source A related resource from which the described resource is derived https://ijicom.respati.ac.id/index.php/ijicom/article/view/85/56 Date A point or period of time associated with an event in the lifecycle of the resource August 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/ac4fda3675bfeff3602262ed72ca3c8e.pdf eac4cd5b8fa2be23d2107661ad51a21d 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. 6 NO. 1 (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 EstablishingCNNfor Network Intrusion Detection: A Comparative Approach Subject The topic of the resource Intrusion Detection,CNN,DLAlgorithms,Network Security,Performance Evaluation Description An account of the resource Intrusion detection plays an important role in protecting systems from various threats. However, as intrusion techniques become more sophisticated, traditional detection methods have shown limitations in identifying new attacks. This research addresses the pressing issue of improving intrusion detection by utilizing Convolutional Neural Networks (CNN) algorithms, compared to various other machine learning techniques such as Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), Decision Trees, and Gradient Boosting (GBoost). The main objective is to evaluate and compare the performance of these algorithms using a comprehensive dataset sourced from Kaggle, which includes 25,192 data and 42 features. Using metrics such as accuracy, precision, recall, and F1-score, the results show a complex pattern in the strengths and weaknesses of each. Surprisingly, CNN achieved exceptional accuracy, raising questions that require further investigation. Notably, KNN stands out as the best-performingmachine learning algorithm. Contextualized within existing research, this study advances the understanding of the role of machine learning in intrusion detection, providing valuable insights for practical implementation. The findings reinforce the relevance of adapting to the evolving network threat landscape while raising interesting questions for future research. In conclusion, this research provides a comparative analysis of intrusion detection techniques, offering a basis for making informed decisions to improve network security and mitigate evolving threats. Creator An entity primarily responsible for making the resource M. Hizbul Wathan1*, Muhamad Aziz Source A related resource from which the described resource is derived https://ijicom.respati.ac.id/index.php/ijicom/article/view/69/57 Date A point or period of time associated with an event in the lifecycle of the resource August 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