1 10 6 https://repository.horizon.ac.id/files/original/d0ccb1e5eaf4c7e3503c0c65b8042676.pdf 682c981b74dbc8b8b2e2282165ffb68a 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. 16 NO. 1 (2023) 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 Embedded Deep Learning System for Classification of Car Make and Model Subject The topic of the resource Embedded System Classification, Embedded Deep Learning, Car Classification Description An account of the resource Automatic car make, and model classification is essential to support activities of intelligent traffic systems in urban areas, such as surveillance, traffic information collection, statistics, etc. In order to classify this data, we need an embedded system approach for real-time car recognition. Many approaches could be made, from image processing to machine learning. Recently, the development of the Convolutional Neural Network has spurred various research in the Area. ResNet, Inception, DenseNet, and NasNet are some of the most commonly used Neural Network based method that is used to classify images. In this research, we utilize pre-processing and cropping technique to maximize the quality of dataset. Several deep learning networks are going to be compared in classifying vehicle make and model in the Stanford dataset. The dataset contains 196 different labels. Several evaluation metrics are used to compare the performance of the methods. From the experiment, the InceptionV3 method achieved the best performance of the AUROC ratio for training the dataset under 50 epochs. Other methods that achieve a high AUROC value tends to have a higher computational time. Real-time simulations have shown that the embedded system is capable of classifying a 100 % success rate for six concurrent users. Creator An entity primarily responsible for making the resource Ari Wibisono, Hanif Arief Wisesa, Satria Bagus Wicaksono, Puteri Khatya Fahira Source A related resource from which the described resource is derived http://dx.doi.org/10.21609/jiki.v16i1.1118 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 2023-02-28 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) Car Classification Embedded Deep Learning Embedded System Classification https://repository.horizon.ac.id/files/original/878858adc7a2a94e9567f4dd57b2af01.pdf 31d679e1fb54ae69fb30ebeae6302107 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. 16 NO. 1 (2023) 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 Face Spoofing Detection using Inception-v3 on RGB Modal and Depth Modal Subject The topic of the resource face spoofing, real, spoof, Inception-v3, depth Description An account of the resource Face spoofing can provide inaccurate face verification results in the face recognition system. Deep learning has been widely used to solve face spoofing problems. In face spoofing detection, it is unnecessary to use the entire network layer to represent the difference between real and spoof features. This study detects face spoofing by cutting the Inception-v3 network and utilizing RGB modal, depth, and fusion approaches. The results showed that face spoofing detection has a good performance on the RGB and fusion models. Both models have better performance than the depth model because RGB modal can represent the difference between real and spoof features, and RGB modal dominate the fusion model. The RGB model has accuracy, precision, recall, F1-score, and AUC values obtained respectively 98.78%, 99.22%, 99.31.2%, 99.27%, and 0.9997 while the fusion model is 98.5%, 99.31%, 98.88%. 99.09%, and 0.9995, respectively. Our proposed method with cutting the Inception-v3 network to mixed6 successfully outperforms the previous study with accuracy up to 100% using the MSU MFSD benchmark dataset. Creator An entity primarily responsible for making the resource Yuni Arti, Aniati Murni Arymurth Source A related resource from which the described resource is derived http://dx.doi.org/10.21609/jiki.v16i1.1100 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 2023-02-28 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) depth face spoofing Inception-V3 real spoof https://repository.horizon.ac.id/files/original/a50dc5d988907f3deb8907e6f4096253.pdf 4e3b52b466b1f6b4aea343e67d5032a9 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. 16 NO. 1 (2023) 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 Improved Mask R-CNN And Cosine Similarity Using RGBD Segmentation For Occlusion Handling In Multi Object Tracking Subject The topic of the resource Occlusion, RGBD, Mask R-CNN, Late fusion, Cosine similarity Description An account of the resource In this study, additional depth images were used to enrich the information in each image pixel. Segmentation, by its nature capable to process image up to pixel level. So, it can detect up to the smallest part of the object, even when it’s overlapped with another object. By using segmentation, the main goal is to be able to maintain the tracking process longer when the object starts to be occluded until it is severely occluded right before it is completely disappeared. Object tracking based on object detection was developed by modifying the Mask R-CNN architecture to process RGBD images. The detection results feature extracted using HOG, and each of them got compared to the target objects. The comparison was using cosine similarity calculation, and the maximum value of the detected object would update the target object for the next frame. The evaluation of the model was using mAP calculation. Mask R-CNN RGBD late fusion had a higher value by 5% than Mask R-CNN RGB. It was 68,234% and 63,668%, respectively. Meanwhile, the tracking evaluation uses the traditional method of calculating the id switching during the tracking process. Out of 295 frames, the original Mask R-CNN method had ten switching ID times. On the other hand, the proposed method Mask R-CNN RGBD had much better tracking results with switching ids close to 0. Creator An entity primarily responsible for making the resource Siti Hadiyan Pratiwi, Putri Shaniya, Grafika Jati, Wisnu Jatmiko Source A related resource from which the described resource is derived http://dx.doi.org/10.21609/jiki.v16i1.1073 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 2023-02-28 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) Cosine Similarity Late fusion Mask R-CNN Occlusion RGBD https://repository.horizon.ac.id/files/original/69d996f1bcad4d70db758a2dbd1f47bf.pdf b9668106366d203e7e0a4c8fa44aee02 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. 16 NO. 1 (2023) 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 LexID: The Metadata and Semantic Knowledge Graph Construction of Indonesian Legal Document Subject The topic of the resource Indonesian Legal Ontology, Knowledge Graph Construction Description An account of the resource The Legal Fiction principle stipulates that the government needs to ensure the public availability of all of their legal documents. Unfortunately, the text-based search services they provide cannot return satisfactory answers in retrieval scenarios requiring proper representation of relationships between various legal documents. A key problem here is the lack of explicit representation of such relationships behind the employed retrieval engines. We aim to address this problem by proposing LexID knowledge graph (KG) that provides an explicit knowledge representation for Indonesian legal domain usable for such retrieval purposes. The KG contains both legal metadata information and semantic content of the legal clauses of the legal document’s articles, modeled using formal vocabulary from the LexID ontology also presented in this paper. The KG is constructed from thousands of Indonesian legal documents. Since the procedure of writing a legal document regulated by the government is clear and detailed, we use a rule-based approach to construct our KG. At the end, we describe several use cases of the KG to address different retrieval needs. In Addition, we evaluated the quality of our KG by measuring its ability to answer questions and got that LexID can answer questions with the macro average F1 score is about 0.91. Creator An entity primarily responsible for making the resource Nur Siti Muninggar, Adila Alfa Krisnadhi Source A related resource from which the described resource is derived http://dx.doi.org/10.21609/jiki.v16i1.1096 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 2023-02-28 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) Indonesian Legal Ontology Knowledge Graph Construction https://repository.horizon.ac.id/files/original/ca52c13176b2ff2ac18187b724639acf.pdf b47e2068aa9884c4704685816f3af483 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. 16 NO. 1 (2023) 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 Poetry Generation for Indonesian Pantun: Comparison Between SeqGAN and GPT-2 Subject The topic of the resource poetry generation, pantun, text generation, SeqGAN, GPT-2 Description An account of the resource Pantun is a traditional Malay poem consisting of four lines: two lines of deliverance and two lines of messages. Each ending-line word in pantun forms an ABAB rhyme pattern. In this work, we compare the performance of Sequence Generative Adversarial Nets (SeqGAN) and Generative Pre-trained Transformer 2 (GPT-2) in automatically generating Indonesian pantun. We also created the first publicly available Indonesian pantun dataset that consists of 7.8K pantun. We evaluated how well each model produced pantun by its lexical richness and its formedness. We introduced the evaluation of pantun with two aspects: structure and rhyme. GPT-2 performs better with a margin of 29.40% than SeqGAN in forming the structure, 35.20% better in making rhyming patterns, and 0.04 difference in giving richer vocabulary to its generated pantun. Creator An entity primarily responsible for making the resource Emmanuella Anggi Siallagan, Ika Alfina Source A related resource from which the described resource is derived http://dx.doi.org/10.21609/jiki.v16i1.1113 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 2023-02-28 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) GPT-2 pantun poetry generation SeqGAN text generation https://repository.horizon.ac.id/files/original/209d6ceb7baf768cdc3172b6a26860d0.pdf 748a1b94c62437ef67a7fa9e968dc9d0 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. 16 NO. 1 (2023) 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 Weld Defect Detection and Classification based on Deep Learning Method: A Review Subject The topic of the resource weld defect, radiographic images, deep learning, convolutional neural network Description An account of the resource The inspection of weld defects utilizing nondestructive testing techniques based on radiography is essential for ensuring the operability and safety of weld joints in metals or other materials. During the process of welding, weld defects such as cracks, cavity or porosity, lack of penetration, slag inclusion,and metallic inclusion may occur. Due to the limitations of manual interpretation and evaluation, recent research has focused on the automation of weld defect detection and classification from radiographic images. The application of deep learning algorithms enables automated inspection. The deep learning architectures for building weld defect classification models were discussed. This paper concludes with a discussion of the achievements of automation methods and a presentation of the research recommendations for the future. Creator An entity primarily responsible for making the resource Tito Wahyu Purnomo, Finkan Danitasari, Djati Handoko Source A related resource from which the described resource is derived http://dx.doi.org/10.21609/jiki.v16i1.1147 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 2023-02-28 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) Convolutional neural network deep learning radiographic images weld defect