1 10 9 https://repository.horizon.ac.id/files/original/c8570049aaeb1236a55ddf717e304c7a.pdf 2985f876099152231888f581802be050 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. 14 NO. 1 (2021) Contributor An entity responsible for making contributions to the resource Sri Wahyuni 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 Pleural Effusion Classification Based on Chest X-Ray Images using Convolutional Neural Network Subject The topic of the resource Computer Vision, Image Classification, Convolutional Neural Network, Pleural Effusion Description An account of the resource Pleural effusion is an abnormal lung condition characterized by a buildup of fluid between the two layers of the pleura, which causes specific symptoms such as chest pain and shortness of breath. In Indonesia, pleural effusion cases alone account for 2.7% of other respiratory diseases, with an estimated number of sufferers in general at more than 3000 people per 1 million population annually. Pleural effusion is a severe case and can cause death if not treated immediately. Based on a study, as many as 15% of 104 patients diagnosed with pleural effusion died within 30 days. This paper proposes a model that automatically detects pleural effusion based on chest x-ray images using a Machine Learning algorithm. The machine learning algorithm used is Convolutional Neural Network (CNN), with the dataset used from ChestX-ray14. The number of data used was 2500 in x�ray images, based on two different classes, x-ray with pleural effusion and x-ray with normal condition. The evaluation result shows that the CNN model can classify data with an accuracy of 95% of the test set data; thus, we hope it can be an alternative to assist medical diagnosis in pleural effusion detection. Creator An entity primarily responsible for making the resource Ahmad Rafiansyah Fauzan, Mohammad Iwan Wahyuddin, and Sari Ningsih Source A related resource from which the described resource is derived http://dx.doi.org/10.21609/jiki.v14i1.898 Publisher An entity responsible for making the resource available Faculty of Computer Science Universitas Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 2021-02-28 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource e-ISSN : 2502-9274 printed ISSN : 2088-7051 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 Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information) Computer Vision Convolutional neural network Image classification Pleural Effusion https://repository.horizon.ac.id/files/original/f9c2d1e4f1d8fcddf94764b5cf7f455d.pdf 4a950719e4bb3d7cd5d08af505690c37 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. 14 NO. 1 (2021) Contributor An entity responsible for making contributions to the resource Sri Wahyuni 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 Automatic Image Self-Enhancement for Multi-Scale Spectral Residual on Low-Resolution Video Subject The topic of the resource Exposure Fusion Framework, Human Detection, Multi-Scale Spectral Residual Description An account of the resource Multi-Scale Spectral Residual technique is used to reduce the search area in an image. However, this technique relies on image salience from the capture device. The aim of this study is to obtain a better search area with image enhancement to detect human objects on low resolution video. Enhanced image uses only pixels in each frame of the video using the Exposure Fusion Framework. The dataset is an artificial video obtained from a room with low resolution CCTV. This study compares the detection results before and after applying image enhancement on MSR. We are adopting Linear-SVM based human detection with Histogram of Gradient (HOG) features as a test case. Human detection was evaluated using precision, recall, f-score rate and validated by leave-one-out cross validation. The results show that enhanced images can improve overall performance by 64.46% compared to the original video in human detection on low resolution video, with an increase in recall of 3.21% Creator An entity primarily responsible for making the resource Arwin Halim, Sunaryo Winardi, and Erlina Halim Source A related resource from which the described resource is derived http://dx.doi.org/10.21609/jiki.v14i1.900 Publisher An entity responsible for making the resource available Faculty of Computer Science Universitas Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 2021-02-28 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource e-ISSN : 2502-9274 printed ISSN : 2088-7051 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 Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information) Exposure Fusion Framework Human Detection Multi-Scale Spectral Residual https://repository.horizon.ac.id/files/original/7794bfa27c1015b932432e0ccbb0c645.pdf 9dd0e45859c1afdc1fbb126e26fa6abd 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. 14 NO. 1 (2021) Contributor An entity responsible for making contributions to the resource Sri Wahyuni 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 Bonferroni Mean Based Fuzzy K-Nearest Centroid Neighbor Classifier Subject The topic of the resource K-Nearest Neighbor, Nearest Centroid Neighborhood, Fuzzy K-Nearest Neighbor, local mean vector, Bonferroni mean Description An account of the resource K-nearest neighbor (KNN) is an effective nonparametric classifier that determines the neighbors of a point based only on distance proximity. The classification performance of KNN is disadvantaged by the presence of outliers in small sample size datasets and its performance deteriorates on datasets with class imbalance. We propose a local Bonferroni Mean based Fuzzy K-Nearest Centroid Neighbor (BM-FKNCN) classifier that determines the class label of an unclassified sample dependent on the nearest local mean vector obtained using the Nearest Centroid Neighborhood (NCN) concept to better represent the underlying statistic of the dataset. The proposed classifier is robust towards outliers because the NCN concept also considers spatial distribution and geometrical placement of the neighbors. Also, the proposed classifier can overcome class domination of its neighbors in datasets with class imbalance because it averages all the centroid vectors from each class to adequately interpret the distribution of the classes. The BM-FKNCN classifier is tested on datasets that are taken from the Knowledge Extraction based on Evolutionary Learning (KEEL) repository and benchmarked with classification results from the KNN, Fuzzy-KNN (FKNN), BM-FKNN and FKNCN classifiers The experimental results show that the BM-FKNCN achieves the highest overall average classification accuracy of 89.86% compared to the other four classifiers. Creator An entity primarily responsible for making the resource Arya Widyadhana, Cornelius Bagus Purnama Putra, Rarasmaya Indraswari, Agus Zainal Arifin Source A related resource from which the described resource is derived http://dx.doi.org/10.21609/jiki.v14i1.959 Publisher An entity responsible for making the resource available Faculty of Computer Science Universitas Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 2021-02-28 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource e-ISSN : 2502-9274 printed ISSN : 2088-7051 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 Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information) Bonferroni mean K-Nearest Neighbor local mean vector Nearest Centroid Neighborhood uzzy K-Nearest Neighbor https://repository.horizon.ac.id/files/original/f8371fc777dad3fc898c80b0e92fe04e.pdf 2f8ff489fe2dd9dd6c59ce7d520e53c9 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. 14 NO. 1 (2021) Contributor An entity responsible for making contributions to the resource Sri Wahyuni 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 Sarcasm Detection Engine for Twitter Sentiment Analysis using Textual and Emoji Feature Subject The topic of the resource twitter, sentiment analysis, sarcasm, social media, textual feature, emoji feature Description An account of the resource Twitter is a social media platform used to express sentiments about events, topics, individuals, and groups. Sentiments in Tweets can be classified as positive or negative expressions. However, the sentiment is an expression that is the opposite of what is meant to be, and this is called sarcasm. The existence of sarcasm in a Tweet is chalenging to be detected automatically by a system, even by humans. In this research, we propose a weighting scheme based on the inconsistency between the sentiment of Indonesian tweets and the usage of emoji. The weighting scheme for detecting sarcasm can be used to find out a sentiment about an event, topic, individual, group, or product's review. The proposed method calculates the distance between the textual feature polarity score obtained from the Convolutional Neural Network and the emoji polarity score in a Tweet. This method is used to find the boundary value between Tweets that contain sarcasm or not. The model's experimental results developed obtained an f1-score of 87.5%, precision 90.5%, and recall 84.8% Creator An entity primarily responsible for making the resource Bagus Satria Wiguna, Cinthia Vairra Hudiyanti, Alqis Rausanfita, Agus Zainal Arifin, Rizka W. Sholikah Source A related resource from which the described resource is derived http://dx.doi.org/10.21609/jiki.v14i1.812 Publisher An entity responsible for making the resource available Faculty of Computer Science Universitas Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 2021-02-28 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource e-ISSN : 2502-9274 printed ISSN : 2088-7051 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 Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information) emoji feature sarcasm sentiment analysis Social media textual feature Twitter https://repository.horizon.ac.id/files/original/393376fc9c1e72390ca43edcad6774ad.pdf e6a9e04bb5800b324c9ff5954d196ca1 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. 14 NO. 1 (2021) Contributor An entity responsible for making contributions to the resource Sri Wahyuni 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 COVIWD: COVID-19 Wikidata Dashboard Subject The topic of the resource COVID-19, Wikidata, Dashboard, Data Modeling, Data Analysis, Data Visualization Description An account of the resource COVID-19 (short for coronavirus disease 2019) is an emerging infectious disease that has had a tremendous impact on our daily lives. Globally, there have been over 95 million cases of COVID-19 and 2 million deaths across 191 countries and regions. The rapid spread and severity of COVID-19 call for a monitoring dashboard that can be developed quickly in an adaptable manner. Wikidata is a free, collaborative knowledge graph, collecting structured data about various themes, including that of COVID-19. We present COVIWD, a COVID-19 Wikidata dashboard, which provides a one-stop information/visualization service for topics related to COVID-19, ranging from symptoms and risk factors to comparison of cases and deaths among countries. The dashboard is one of the first that leverages open knowledge graph technologies, namely, RDF (for data modeling) and SPARQL (for querying), to give a live, concise snapshot of the COVID-19 pandemic. The use of both RDF and SPARQL enables rapid and flexible application development. COVIWD is available at http://coviwd.org. Creator An entity primarily responsible for making the resource Fariz Darari Source A related resource from which the described resource is derived http://dx.doi.org/10.21609/jiki.v14i1.941 Publisher An entity responsible for making the resource available Faculty of Computer Science Universitas Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 2021-02-28 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource e-ISSN : 2502-9274 printed ISSN : 2088-7051 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 Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information) COVID-19 Dashboard Data analysis Data Modeling Data Visualization Wikidata https://repository.horizon.ac.id/files/original/b6bc17f1bb68de6d6c6deea80257b9fe.pdf 98ed3f4a64f1923c6f51ed47814830f1 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. 14 NO. 1 (2021) Contributor An entity responsible for making contributions to the resource Sri Wahyuni 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 Reducing Adversarial Vulnerability through Adaptive Training Batch Size Subject The topic of the resource adversarial examples, batch normalization, fixup initialization, batch size variation Description An account of the resource Neural networks possess an ability to generalize well to data distribution, to an extent that they are capable of fitting to a randomly labeled data. But they are also known to be extremely sensitive to adversarial examples. Batch Normalization (BatchNorm), very commonly part of deep learning architecture, has been found to increase adversarial vulnerability. Fixup Initialization (Fixup Init) has been shown as an alternative to BatchNorm, which can considerably strengthen the networks against adversarial examples. This robustness can be improved further by employing smaller batch size in training. The latter, however, comes with a tradeoff in the form of a significant increase of training time (up to ten times longer when reducing batch size from the default 128 to 8 for ResNet-56). In this paper, we propose a workaround to this problem by starting the training with a small batch size and gradually increase it to larger ones during training. We empirically show that our proposal can still improve adversarial robustness (up to 5.73%) of ResNet-56 with Fixup Init and default batch size of 128. At the same time, our proposal keeps the training time considerably shorter (only 4 times longer, instead of 10 times). Creator An entity primarily responsible for making the resource Ken Sasongko, Adila Alfa Krisnadhi, Mohamad Ivan Fanany Source A related resource from which the described resource is derived http://dx.doi.org/10.21609/jiki.v14i1.907 Publisher An entity responsible for making the resource available Faculty of Computer Science Universitas Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 2021-02-28 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource e-ISSN : 2502-9274 printed ISSN : 2088-7051 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 Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information) adversarial examples batch normalization batch size variation fixup initialization https://repository.horizon.ac.id/files/original/f5b5d31b5fbfde5a239a42d5e7ca54c4.pdf d4fba5f5dd1c90542d813efc419221b7 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. 14 NO. 1 (2021) Contributor An entity responsible for making contributions to the resource Sri Wahyuni 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 3D Information from Scattering Media Images Subject The topic of the resource scattering media, 3D depth, transmission, statistical prior, airlight Description An account of the resource Haze, fog, and bad weather conditions occur often in daily life. In these scattering media environments, micro-particles interfere with light propagation and image formation. Images captured in these conditions will suffer from low contrast and loss of intensity, hindering many computer vision methods. Thus, many approaches attempt to estimate the corresponding clear scene before processing the image further. However, the image formation model in scattering media shows that the 3D distance information is encoded implicitly in image intensities. In this paper, we provide a systematic review on methods to estimate relative depth and explicit depth directly from scattering media images. We use a dataset consisting of synthesized hazy images with known ground truths to establish accuracy, as well as real hazy images for a general visual analysis. For the accuracy evaluation, we demonstrate transmission estimation using statistical priors obtaining an average SSIM of 0.411 and MAE of 1.004; and depth map estimation using deep networks with an average SSIM of 0.305 and MAE of 0.860. Furthermore, for additional visual analysis, we also present the distance estimation for real hazy and underwater images of which we have no ground truth Creator An entity primarily responsible for making the resource Laksmita Rahadianti Source A related resource from which the described resource is derived http://dx.doi.org/10.21609/jiki.v14i1.963 Publisher An entity responsible for making the resource available Faculty of Computer Science Universitas Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 2021-02-28 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource e-ISSN : 2502-9274 printed ISSN : 2088-7051 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 Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information) 3D depth airlight scattering media statistical prior transmission https://repository.horizon.ac.id/files/original/c7394aca113c733a5a8a3ab2f02eb3bd.pdf a79cb474b741557a2e5737fca0b7192a 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. 14 NO. 1 (2021) Contributor An entity responsible for making contributions to the resource Sri Wahyuni 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 Comparison of FairMOT-VGG16 and MCMOT Implementation for Multi-Object Tracking and Gender Detection on Mall CCTV Subject The topic of the resource CCTV, Detection, Gender, Tracking Description An account of the resource The crowd detection system on CCTV has proven to be useful for retail owners in mall areas where data can be used as a guide by owners to find out the number of visitors who enter at a certain time. However, such information was still insufficient. The necessity for richer data has led to the development of specific person detection which involves gender. Gender detection can provide specific details about men and women visiting a particular location. However, gender detection alone does not provide an identity label for every detection that occurs, so it needs to be combined with a multi-person tracking system. This study compares two methods, method that are tracks person using one-class FairMOT and classify detected person based on gender and method that track multi-class object where the class is male and female at the same time simultaneously called MCMOT which is extended version of FairMOT. The first method produces MOTA, MOTP, IDS, and FPS of 78.56, 79.57, 19, and 24.4, while the second method makes 69.84, 81.94, 147, and 30.5. Besides, evaluation of gender was also carried out where the first method resulted in a gender accuracy of 65% while the second method was 62.35% Creator An entity primarily responsible for making the resource Pray Somaldo, Dina Chahyati Source A related resource from which the described resource is derived http://dx.doi.org/10.21609/jiki.v14i1.958 Publisher An entity responsible for making the resource available Faculty of Computer Science Universitas Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 2021-02-28 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource e-ISSN : 2502-9274 printed ISSN : 2088-7051 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 Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information) Cctv Detection Gender Tracking https://repository.horizon.ac.id/files/original/a63a225940a7c9ca35c6cb70be117ba3.jpg d99de79cfa93ef976091691e104c9072 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. 14 NO. 1 (2021) Contributor An entity responsible for making contributions to the resource Sri Wahyuni 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 Cover Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)