3 10 25 https://repository.horizon.ac.id/files/original/c1d73829b86b9e482328e29d07f609a3.pdf 699c60425db2b0d34f07ac75f2dfc9bc 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 7 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 Character Recognition of Handwriting of Javanese Character Image using Information Gain Based on the Comparison of Classification Method Subject The topic of the resource character recognition; javanese character; information gain; LBP Description An account of the resource ndonesia is a country rich in a variety of regional cultures. Regional airspace needs to be preserved so as not to become extinct. One of them is the local culture of Central Java Province, namely Javanese Character. In this modern era, globalization is growing in every country. The impact of globalization is increasingly widespread and developing in society. One effect of globalization is local people prefer foreign language skills to learn local languages. This study, appliesthe method of character recognition using a new combination workflow that contains Local Binary Pattern (LBP) and Information Gain. Then compare Support Vector Machine (SVM), k-Nearest Neighbor and Naïve Bayes. The LBP method is used to obtain an image's texture or shape characteristics. Information Gain is used for the feature selection algorithm, whereas SVM, k-Nearest Neighbor and Naïve ayes is used for the classification method. From previous research, the information gain method succeeded in increasing the accuracy by 2%. This research compares the SVM classification with another classification method, and the result shows that our proposed can improve classification performance. The best accuracy result using SVM classification gets 87,86%, at ten folds and cell size 64x64. Creator An entity primarily responsible for making the resource Irham Ferdiansyah Katili1 , Mochamad Arief Soeleman2 , Ricardus Anggi Pramunendar3 Publisher An entity responsible for making the resource available Universitas Dian Nuswantoro Semarang Date A point or period of time associated with an event in the lifecycle of the resource : 06-02-2023 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 Indonesia Type The nature or genre of the resource Text https://repository.horizon.ac.id/files/original/de3f6b9904f5676a4465350646ad3e36.pdf f4419e232c42a7fcc65788c9620c7bd9 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 7 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 Naïve Bayes and TF-IDF for Sentiment Analysis of the Covid-19 Booster Vaccine Subject The topic of the resource naïve bayes, TF-IDF, sentiment analysis, booster vaccine Description An account of the resource The booster vaccine polemic became a trending topic on Twitter and reaped many pros and cons. This booster vaccine began to be distributed on January 12, 2022. This booster vaccine program was implemented free of charge for the people of Indonesia to prevent the new variant of Covid-19, Omicron. The contribution of this study is to analyze the sentiment of booster vaccines to prevent covid-19 using the Naïve Bayes and TF-IDF methods. We conducted sentiment analysis to determine whether the tweet was positive, negative, or neutral. The solution used is the Naïve Bayes method and TF-IDF. The role of TF�IDF is to determine how relevant the data in the document is by utilizing word weighting. The stages of this research using CRISP-DM include Business Understanding, Data Understanding, Data Preparation, Modelling, Evaluation, and Deployment. The net data results show 1,557 data with a positive sentiment of 1,335, a neutral sentiment of 171 data, and a negative sentiment of 51 data. The test results with 60:40 data sharing obtained accuracy, precision, and recall values of 85.26%, 85%, and 100%. The results of this test have increased by 7.26%, 12%, and 20% from other previous studies with the same data distribution. Creator An entity primarily responsible for making the resource Imelda Imelda1 , Arief Ramdhan Kurnianto2 Publisher An entity responsible for making the resource available Universitas Budi Luhur Date A point or period of time associated with an event in the lifecycle of the resource 01-02-2023 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 Indonesia Type The nature or genre of the resource Text https://repository.horizon.ac.id/files/original/577b1e281c7987e5ac289429f7a8b6ea.pdf a13cd36b20e12eabd39304b93cb0ede8 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 7 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 Modification of SqueezeNet for Devices with Limited Computational Resources Subject The topic of the resource deep learning, squeezenet, resnet, imagenet, convolutional layer Description An account of the resource In recent years, the computational approach has shifted from a statistical basis to deep neural network architectures which process the input without explicit knowledge that underlies the model. Many models with high accuracy have been proposed by training the datasets using high performance computing devices. However, only a few studies have examined its use on non�high-performance computers. In fact, most users, who are mostly researchers in certain fields (medical, geography, economics, etc.) sometimes need computers with limited computational resources to process datasets, from notebooks, personal computers, to mobile processor-based devices. This study proposes a basic model with good accuracy and can run lightly on the average computer so that it remains lightweight when used as a basis for advanced deep neural networks models, e.g., U-Net, SegNet, PSPNet, DeepLab, etc. Using several well-known basic methods as a baseline (SqueezeNet, ShuffleNet, GoogleNet, MobileNetV2, and ResNet), a model combining SqueezeNet with ResNet, termed Res-SqueezeNet, was formed. Testing results show that the proposed method has accuracy and inference time of 84.59% and 8.46 second, respectively, which has an accuracy of 2% higher than the SqueezeNet (82.53%) and is close to the accuracy of other baseline methods (from 84.93% to 0.88.01%) while still maintaining the inference speed (below nine second). In addition, residual part of the proposed method can be used to avoid vanishing gradient, hence, it can be implemented to solve more advanced problems which need a lot of layers, e.g., semantic segmentation, time-series prediction, etc. Creator An entity primarily responsible for making the resource Rahmadya Trias Handayanto1 , Herlawati2 1Computer Engineering, Faculty of Engineering, Universitas Isl Publisher An entity responsible for making the resource available Universitas Islam 45 Bekasi Date A point or period of time associated with an event in the lifecycle of the resource 03-02-2023 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 Indonesia Type The nature or genre of the resource Text https://repository.horizon.ac.id/files/original/1c6999f6d65b7807419757a29aebaa6c.pdf 09d8212ef538014d0d00c8a14e08808f 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 7 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 Iris Recognition Using Hybrid Self-Organizing Map Classifier and Daugman’s Algorithm Subject The topic of the resource iris recognition, SOM, hybrid SOM, cosine similarity, daugman's algorithm Description An account of the resource One of the neural network algorithms that can be used in iris recognition is self-organizing map (SOM). This algorithm has a weakness in determining the initial weight of the network, which is generally carried out randomly, which can result in a decrease in accuracy when an incorrect determination is made. The solution that is often used is to apply a hybrid process in determining the initial weight of the SOM network. This study takes an approach using the cosine similarity equation to determine the initial weight of the network SOM in order to increase recognition accuracy. In addition, the localization process needs to be carried out to limit the area of the iris image being studied so that it is easy for the recognition process to be carried out. The method proposed in this study for iris recognition, namely hybrid SOM and Daugman’s algorithm, has been tested on several people by capturing the iris of the eye using a digital camera. The captured eyes have been localized first using the Daugman’s algorithm, and then the image features were extracted using the GLCM and LBP methods. In the final stage of the study, an iris recognition comparison test was performed, and the results obtained an accuracy of 85.50% using the proposed method and an accuracy of 73.50% without performing a hybrid process on the SOM network. Creator An entity primarily responsible for making the resource Amir Saleh1 , Yusuf Roni Laia2 , Fransiskus Gowasa3 , Victor Daniel Sihombing4 Publisher An entity responsible for making the resource available Universitas Prima Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 03-02-2023 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 Indonesia Type The nature or genre of the resource Text https://repository.horizon.ac.id/files/original/fc395ea5f574f8a005537ecfc4c3777a.pdf c2307253f7008db2af8a7082a2800258 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 7 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 Face Recognition of Indonesia’s Top Government Officials Using Deep Convolutional Neural Network Subject The topic of the resource deep convolutional neural network; face recognition; facenet; haar cascade classifier Description An account of the resource Facial recognition is a part of Computer Vision that is used to get facial coordinates from an image. Many algorithms have been developed to support Facial Detection such as Cascade Face Detection using Haar-Like features and AdaBoost to classify its Cascade and Convolutional Neural Network (CNN). Face recognition in this study uses the Deep Convolutional Neural Network (DCNN) method, and the output of this method is the measurement value of the face. In the model training process, Triplet Loss from Triplet Network Deep Metric Learning is used to get good face grouping results. The value of this face measurement will then be measured using the Euclidean distance calculation to determine the similarity of the input face from the dataset. This Research is using 6 images of Government officers in Indonesia to determine the accuracy of the model when there is a new picture of these officers inputted into the training machine. The result provides a 94% accuracy level with a variety of face positions and levels of brightness. Creator An entity primarily responsible for making the resource Umar Aditiawarman1 , Dimas Erlangga2 , Teddy Mantoro3 , Lutfil Khakim4 Publisher An entity responsible for making the resource available Nusa Putra University Date A point or period of time associated with an event in the lifecycle of the resource 03-02-2023 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 Indonesia