1 10 5 https://repository.horizon.ac.id/files/original/e3be9ecd22b336126f86bd2d10011f74.pdf 0caa68a7ebb1e53b9381653b91690cd9 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. 1 (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 IDS-GAN: Stepping up Intrusion Detection Methodusing GAN Algorithm Subject The topic of the resource Detection, IDS, Deep Learning, GAN Description An account of the resource Many computer networkthreats causethe security aspectto becomethe most critical problem. Theintrusion detection system is a widely used practical security tool to prevent malicious traffic from penetrating networks and systems. To solve the issue, we construct a novel algorithm using Generative Adversarial Networks (GAN)to address the IDS security problem.In this paper, we propose an intrusion detection model using GANby analyzing the extracted features of the network. To build our detection model, we collect the dataset, conduct pre-processing, trainour modelwith several hyper-parameters to get the bestaccuracy, and then test the model using the new data. Based on experimental results, the proposed model can producea 0.00539error rateand indicate a more accurate modelto detect anomaliesin the network traffic Creator An entity primarily responsible for making the resource H.Y.Fan1, SarahAnjani Source A related resource from which the described resource is derived https://ijicom.respati.ac.id/index.php/ijicom/article/view/55/45 Date A point or period of time associated with an event in the lifecycle of the resource August 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/7cab401fd14a5b23ca77edd6eb18d2b1.pdf e03b80a117d0311bbd87111dfc80115e 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. 1 (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 Robust Stock Price PredictionusingGated Recurrent Unit (GRU) Subject The topic of the resource Deep Learning, GRU,Stock Price,Prediction Description An account of the resource Forecasting the direction of price movement of the stock market could yield significant profits. Traders use technical analysis, which is the study of price by scrutinizing past prices, to forecast the future price of the nickel stock price. Therefore,in this study, we proposeGated Recurrent Units (GRU) to predict nickel stock price trends. This research aimsto produce an accurate nickel stock price trend prediction model. The research method utilizedhistorical data on nickel stock prices from Yahoo Finance. The research results show that the model developed accurately predictednickel stock price trends. From the RMSE, MAE, and MSE analysisresults, the RMSE value was 0.0123, the MAE value was 0.0089, and the MSE value was 0.0002 on the test data. Creator An entity primarily responsible for making the resource Hamzah1,Poly Endrayanto Eko Chrismawan2,, Sugeng Winardi3, Rainbow Tambunan Source A related resource from which the described resource is derived https://ijicom.respati.ac.id/index.php/ijicom/article/view/56/46 Date A point or period of time associated with an event in the lifecycle of the resource August 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/1a3be0abd16ce2bfc24e47a26203fd3e.pdf 9e53b344be54f924753fe4a8c8767a6f 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. 1 (2023) Still Image A static visual representation. Examples include paintings, drawings, graphic designs, plans and maps. Recommended best practice is to assign the type Text to images of textual materials. 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 Implementation of Deep Learning for Classification of MushroomUsing the CNN Algorithm Subject The topic of the resource Mushroom, Classification, Deep Learning, CNN Description An account of the resource Mushroomsare a type of low-level plant that lacks chlorophyll. One of the advantages of fungiis that they are commonly utilized as food items in the community. This paper discussedthe implementation of CNN for the classification of mushrooms. The project aims to develop a robust system that can automate the labor-intensive task of mushroom classification. The CNN model will be trained on a large dataset of annotated mushroom images, learning to extract meaningfulfeatures and patterns for accurate categorization.To evaluate the performance of the developed system, a comprehensive set of metrics, including accuracy, precision, recall, and F1 score, will be used. The dataset will be split into training, validation, and testing sets to assess the model's generalization abilityto unseen data. Based on the experimental result, the average accuracy rate in the Agaricus Portobello test was % -99.89 %, % -99.89 % in the Amanita Phalloides test, % -99.59 % in the Cantharellus Cibarius test, % -98.89 % in the Gyromitra Esculenta test, % -99.96 % in the Hygrocybe Conica, and % -99.93 % in the Omphalotus Orealius. Creator An entity primarily responsible for making the resource Imam Mahfudz I’tisyam1, R. Nurhadi Wijaya2, Rike Pradila Source A related resource from which the described resource is derived https://ijicom.respati.ac.id/index.php/ijicom/article/view/42/43 Date A point or period of time associated with an event in the lifecycle of the resource August 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/7613429e75a5decd85fd3ce2c8dd2450.pdf c15fa367c3649aa6169244d1b265bb36 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. 1 (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 FaceGAN: Robust Face Recognitionusing Generative Adversarial Networks (GAN) Algorithm Subject The topic of the resource Prediction, Deep Learning GAN, Face Classification Description An account of the resource Generative Adversarial Networks (GANs)are a type of neural network that can generate synthetic images that are often indistinguishable from real ones. The article explores GAN to augment existing datasets or generate new onesfor training classifiers. The competitive training process of GANs results in a generator network that can produce increasingly realistic imagesto create more diverse and balanced datasets for training classifiers.The article discusses several successful applications of GANs in image classification, including object recognition, face classification, and medical image analysis. The datasets used in this article are CelebA and FER2013. The CelebA dataset consists of 202,599 celebrity images with 40 attributes, such as gender, age, and facial hair. The FER2013 dataset consists of 35,887 images of faces with sevenotheremotions, including anger, disgust, fear, happiness, sadness, surprise, and neutral.The dataset is divided into training, validation, and test sets.We resized the images to 64x64 pixels and normalizedthe pixel values between -1 and 1, then trained a GANmodel usingthe dataset. We evaluate the performance of our approach and compare it with several state-of-the-art methods, including Support Vector Machines (SVM) and Convolutional Neural Networks (CNN).We evaluate the performance of our approach and compare it with several state-of-the-art methods, including Support Vector Machines (SVM) and Convolutional Neural Networks (CNN),with the results that our approach outperforms SVM and CNN methods on both datasets, achieving a classification accuracy of 89.2% on CelebA and 72.5% in FER2013.Meanwhile, classification accuracy on SVM was 82.3% on CelebA and 65.4% on FER2013. Classification accuracy on CNN is 87.9% on CelebA and 70.8% on FER2013 Creator An entity primarily responsible for making the resource Maryama Kurnia Amri1, Bambang Sugiantoro Source A related resource from which the described resource is derived https://ijicom.respati.ac.id/index.php/ijicom/article/view/57/47 Date A point or period of time associated with an event in the lifecycle of the resource August 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/121931584f5fba2a102c5d932f1bd415.pdf d282234cc4dccb5d080aab265fe38d11 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. 1 (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 ConFruit: Effective Fruit Classification Using CNN Algorithm Subject The topic of the resource Fruit, Classification, Deep Learning, Convolutional Neural Network (CNN Description An account of the resource Fruit is a type of food rich in nutrients, vitamins, and minerals, making it highly beneficial for daily consumption. However, the wide variety of available fruits can make it challenging for consumers to select and purchase the right ones. In recent years, numerous studies have explored fruit classification methods to address this issue. To contribute to this field, this study proposes a novel recommendation model based on fruit types to help buyers more easily identify and distinguish fruits. The dataset for this research was sourced from Kaggle, consisting of 3,000 fruit images. Through experiments, we demonstrated that our model, utilizing the CNN algorithm, achieved high accuracy in classifying fruits, enabling consumers to differentiate between various types effectively. The results showed improved accuracy and reduced losses compared to traditional fruit classification studies, highlighting the effectiveness of our approach Creator An entity primarily responsible for making the resource Rani Laple Satria Putra1, M. Hizbul Wathan2 Source A related resource from which the described resource is derived https://ijicom.respati.ac.id/index.php/ijicom/article/view/44/44 Date A point or period of time associated with an event in the lifecycle of the resource August 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