1 10 5 https://repository.horizon.ac.id/files/original/41bcf22a3699ee4af91d6300ead626fb.pdf 056cc4eb870a7466c69d4badd61d4ac3 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. 5 NO. 2 (2023) 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 Vehicle Detection in Challenging Datasets: YOLOv5sand Frozen Layers Analysis Subject The topic of the resource YOLOv5s, Image Detection, Transfer Learning, Imbalance Dataset, CNN Description An account of the resource Small datasets and imbalanced classes often cause problems when used as primary research material. In the case of classification and object detection, some researchers proposed Transfer Learning (TF) with several frozen layers. Moreover, YOLO (You Only Look Once) is one of the algorithms that works in real-time object detection. In this research, we focused on evaluating the YOLOv5s version of detecting vehicles in small and imbalanced datasets. The original YOLOv5s were trained and compared with YOLOv5s with the freezing layers method (10 and 24 frozen layers). The experimental results of original YOLOv5s were precision score of 0.779, recall value of 0.933, mAP@0.5 of 0.93 and mAP@0.5:0.95 of 0.684 while YOLOv5s with 10 frozen layers where precision score was decreased to 0.639, but the other value increase with recall value of 0.939, mAP@0.5 of 0.951 and mAP@0.5:0.95 of 0.732. Overall, the version with 10 frozen layers demonstrated superior performance in addressing the challenges of small and imbalanced datasets, particularly excelling in recall and mAP metrics 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/64/51 Date A point or period of time associated with an event in the lifecycle of the resource December 2023 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/f8c98d15938d8fbe91658d8ba32e38f2.pdf 579698ba96c90a9a04cc5a532017883e 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. 5 NO. 2 (2023) 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 Effective Ransomware Attacks Detection Using CNN Algorithm Subject The topic of the resource Class Imbalance; Cybersecurity; Machine Learning Algorithms; Ransomware Detection; Social Media Description An account of the resource This study identified ransomware threats in social media platforms by evaluating the performance of Assessing different machine-learning algorithms in various aspects of detecting and classifying ransomware content. The primary problem revolves around the need to enhance cybersecurity within the dynamic landscape of social media, where users are increasingly susceptible to malicious attacks. The research objectives involve assessing the effectiveness of different algorithms, including Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Decision Trees, K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), and Gradient Boosting (GBoost), in distinguishing between ransomware and benign content. A dataset consisting of 6,245 records with 15 features is employed to achieve this. The methods encompass data preprocessing, algorithm implementation, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The research results revealed significant variations in algorithm performance, with Decision Tree and GBoost exhibiting exceptional accuracy while class imbalance challenges and model optimization issues were identified. These findings provide valuable insights into the complex realm of ransomware detection in social media, offering a foundation for future research and cybersecurity improvements in the digital space Creator An entity primarily responsible for making the resource Huang J1, Catherin H2 Source A related resource from which the described resource is derived https://ijicom.respati.ac.id/index.php/ijicom/article/view/62/50 Date A point or period of time associated with an event in the lifecycle of the resource December 2023 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/dd11f05d8ce86cbf02fb91c302ecf820.pdf 61f3152175a74a9ff4ec85441b81a87c 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. 5 NO. 2 (2023) 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 Study of Detecting Twitter Spam using Deep Learning Subject The topic of the resource Twitter,Spam Detection,Convolutional NeuralNetworks(CNN), Deep Learning Description An account of the resource AbstractThis study addresses the escalating challenge of Twitter spam detection by leveraging the power of Convolutional Neural Networks (CNNs). With the proliferation of spam content on social media platforms, traditional machine learning algorithms have exhibited limitations in discerning intricate patterns within sequential data. The research problem centers on the need for amore robust and effective approach to distinguish spam tweets from legitimate content. The primary objective is to evaluate the performance of CNNs in comparison to baseline algorithms, including SVM, Decision Tree, KNN, Gaussian Naive Bayes, and Gradient Boosting. The research approach involves thorough data preprocessing, followed by model training and assessment using metrics like Confusion Matrix and Classification Report. The outcomes indicate that the CNN model outperforms the baseline algorithms, exhibiting superior levels of accuracy, precision, recall, and F1-score. These results highlight the promise of CNNs in reshaping the landscape of Twitter spam detection, presenting a more precise and effective approach to tackle the spread of spam content across social media platforms. Thisresearch contributes valuable insights for the development of advanced machine learning techniques in the domain of online security and spam detection Creator An entity primarily responsible for making the resource M Amman Said1, Yaser Ahmad2 Source A related resource from which the described resource is derived https://ijicom.respati.ac.id/index.php/ijicom/article/view/61/52 Date A point or period of time associated with an event in the lifecycle of the resource December 2023 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/d25aa634cbdc4462dd963afa58e7b01e.pdf 2325cc5e7c306d3368e1e47c59ccc3b9 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. 5 NO. 2 (2023) 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 Comparative Approachfor Intrusion Detection using CNN Subject The topic of the resource ntrusion Detection,CNN,MLAlgorithms,Network Security,Performance Evaluation Description An account of the resource n the realm of computer network security, the role of intrusion detection is crucial for safeguarding systems against various threats. However, with the advancement of intrusion techniques, traditional detection methods have demonstrated constraints in recognizing novel attacks. This study tackles the urgent challenge of enhancing intrusion detection by employing Convolutional Neural Networks (CNN) algorithms, contrasting them with different machine learning methodologies like Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), Decision Trees, and Gradient Boosting (GBoost). The primary aim is to assess and compare the effectiveness of these algorithms utilizing an extensive dataset acquired from Kaggle, comprising 25,192 data entries and 42 attributes. Through the assessment of metrics such as accuracy, precision, recall, and F1-score, the findings reveal a nuanced profile of the strengths and weaknesses of each approach. Remarkably, CNN demonstrated remarkable accuracy, prompting further inquiry into its performance Creator An entity primarily responsible for making the resource Muhamad Aziz, Wakhid A Source A related resource from which the described resource is derived https://ijicom.respati.ac.id/index.php/ijicom/article/view/58/48 Date A point or period of time associated with an event in the lifecycle of the resource December 2023 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/eca6da43df0a070b098c9c0f0ddb8574.pdf 9e7498590e242576f10859fe28f24553 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. 5 NO. 2 (2023) 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 Cardiovascular Diseases Classification using CNN Algorithm Subject The topic of the resource Cardiovascular Disease Detection, Machine Learning Algorithms, CNN, SVM, Medical Diagnosis Description An account of the resource This study focuses on using machine learning algorithms to detect cardiovascular diseases, addressing the critical need for accurate and timely diagnosis of these conditions, which are significant contributors to global morbidity and mortality. The research aims to evaluate the performance of various machine learning algorithms such as Convolutional Neural Network (CNN), Support Vector Machine (SVM), Decision Tree, K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), and Gradient Boosting in categorizing patients into 'yes' or 'no' groups for cardiovascular diseases based on a thorough dataset. The methodology includes data preprocessing, feature selection, and model training and assessment. The results indicate that CNN and SVM demonstrate strong and balanced performance, whereas the Decision Tree shows high sensitivity but potential overfitting. These outcomes offer valuable insights for algorithm selection and model improvement in the detection of cardiovascular diseases, setting the groundwork for further research to enhance diagnostic accuracy, clinical relevance, and healthcare outcomes Creator An entity primarily responsible for making the resource Romana H,Juwita Sampe R Source A related resource from which the described resource is derived https://ijicom.respati.ac.id/index.php/ijicom/article/view/60/49 Date A point or period of time associated with an event in the lifecycle of the resource December 2023 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