1 10 5 https://repository.horizon.ac.id/files/original/8585eb184cb1611dd307d0a870e356dc.pdf 6fb4860ba5e33d499a0cd6e2207c4549 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. 4 NO. 1 (2022) 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 DeePNeu: Robust Detection of Pneumonia Symptoms using Faster R-CNN Subject The topic of the resource Detection, Pneumonia, Deep Learning, Faster R-CNN Description An account of the resource Every year, more than 150 million people, primarily children under five, develop pneumonia. Various articles present various methods for detecting pneumonia. However, to accurately analyze chest X-ray images, radiologists need expertise field. The traditional techniques remain shortcomings, including the availability of experts, maintenance costs, and expensive tools. Thus, we present a new intelligence method to detect pneumonia images quickly and accurately using the Faster Region Convolutional Neural Network (Faster R-CNN) algorithm. To build our detection model, we collect data, process it first, train it with various parameters to get the best accuracy, and then test it with new data. Based on the experimental results, it was found that this model can accurately detect pneumonia x-ray images marked with bounding boxes. In this model, it is possible to predict the bounding box that is more than what it should be, so NMS is applied to eliminate the prediction of the bounding box that is less precise to increase accuracy. Creator An entity primarily responsible for making the resource Wayan Ordiyasa1Akhyar Bintang Source A related resource from which the described resource is derived https://ijicom.respati.ac.id/index.php/ijicom/article/view/46/37 Date A point or period of time associated with an event in the lifecycle of the resource August 2022 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/2769fc0b5d36d7bcd455d829d5b05d69.pdf f12875a4d0039e5a0767631568e7af99 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. 4 NO. 1 (2022) 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 Re-Fake: Fake Account Classification in OSN Using RNN Subject The topic of the resource Classification, Fake Accounts, Recurrent Neural Network, Deep Learning Description An account of the resource Online Social Network (OSN) is an application for enabling public communication and sharing information. However, the fake account in the OSN can spread false information froman unknown source. It is a challenging task to detect malicious accounts in a large OSN system. The existence of fake accounts or unknown accounts on OSN can be a severe issue in data privacy-preserving. Various communities have proposed many techniques to deal with fake accounts in OSN, including rule-based black-white techniquesuntil learning approaches. Therefore, in this study,we propose a classification model using the RNN to detect fake accounts accurately and effectively. We conduct this study in several steps, including gathering datasets, pre-processing, extraction, and training our models using RNN. Based on the experiment result, our proposed model can produce a higher accuracy than the conventional learning model Creator An entity primarily responsible for making the resource Romana Herlinda Source A related resource from which the described resource is derived https://ijicom.respati.ac.id/index.php/ijicom/article/view/48/34 Date A point or period of time associated with an event in the lifecycle of the resource August 2022 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/19dc097935fe57eabfd2127914baea48.pdf dad18a408fd2eee5801420f11f557500 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. 4 NO. 1 (2022) 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 Breast cancer Detection using Faster R-CNN Algorithm Subject The topic of the resource Breast Cancer Detection, Deep Learning, Faster R-CNN Description An account of the resource One of the most common screening tools for breast cancer detection is ultrasound. However, the lack of qualified radiologists causes the diagnosis process to become a challenging task. Deep learning's promising achievement in various computer vision problems inspires us to apply the technology to medical image recognition problems. We propose a detection model based on the Faster R-CNN to detect breast cancer quickly and accurately. We conduct this experiment by collecting breast cancer datasets, conducting pre-processing, training models, and evaluating the model performance. Based on the experiment result, we obtainthat this model can detect breast cancer with bounding boxes. In this model, it is possible to detect the bounding box that is more than what it should be, so we applied NMS to eliminate the prediction of the bounding box that is less precise to increase accuracy Creator An entity primarily responsible for making the resource Anisa Dian Pratiwi1, Irma Permata Sari Source A related resource from which the described resource is derived https://ijicom.respati.ac.id/index.php/ijicom/article/view/49/36 Date A point or period of time associated with an event in the lifecycle of the resource August 2022 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/b832ee58b28832795fa5737443804a5f.pdf 11487fff0653d3555df6e036448bd397 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. 4 NO. 1 (2022) 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 RobustPrediction Model of Covid-19 using Deep Learningalgorithm Subject The topic of the resource Covid-19, Prediction, Deep Learning, CNN Description An account of the resource Predicting the Covid-19 outbreak in a large dataset is a difficult task and complex problem. Many communities have put up different approaches to forecast COVID-19-positivecases. Conventional methods continue to have problems predicting the real trend situations, nevertheless. In this experiment, we use CNN to develop our model by examining attributes from the enormous COVID-19dataset to anticipate long-lasting outbreaks and provide early intervention. Based on the outcomes of the experiment, our model can achieve sufficient accuracy with a negligible loss. In this study, we compute the function that yields RMSE 0.00070 and MAPE 0,02440 for new case prediction and RMSE 0.00468 and MAPE 0.06446 for new death prediction. As a result, our suggested strategy can accurately forecast the trend of positive cases in Indonesia. Creator An entity primarily responsible for making the resource Rike Pradila Source A related resource from which the described resource is derived https://ijicom.respati.ac.id/index.php/ijicom/article/view/45/32 Date A point or period of time associated with an event in the lifecycle of the resource August 2022 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/e0f411ec34fa6e9a293a7f59e48d8e79.pdf e5e4886b7edc1e5bef5a1f403ad97cb2 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. 4 NO. 1 (2022) 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 StockTM: Accurate Stock Price Prediction Model Using LSTM Subject The topic of the resource Prediction, Stocks, Long Short-Term Memory, Deep Learning Description An account of the resource Stock prediction aims to forecast a future stock price trend to assist investors in making strategic investment choices. However, it is hard to predict the price in dynamic conditions, which makesinvestors hard to anticipate equities because of the unstable prices. Thus, in this paper, we present a novel stock price prediction model based on the Long Short-Term Memory (LSTM) algorithm. Several steps are taken in creating a stock prediction model,including collecting datasets, pre-processing, extracting features, training,and validating the model using evaluation metrics techniques. Based on the experimental results, the proposed prediction model can obtain good accuracy with a small error rate in extensive dataset training. Therefore, it can be a promising solution to deal with dynamic prices. Moreover, the proposed model can achieve the results obtained: RMSE EMA10 of 0.00714, RMSE EMA20 of 0.00355, MAPE EMA10 of 0.07705, and MAPE EMA20 0.05273 Creator An entity primarily responsible for making the resource Mohammad Diqi Source A related resource from which the described resource is derived https://ijicom.respati.ac.id/index.php/ijicom/article/view/50/33 Date A point or period of time associated with an event in the lifecycle of the resource August 2022 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