1 10 25 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 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/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/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/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/d701b68bf26d6e25f7bd035224f489e2.pdf 2b37c3faac139fb9cac5f8e4d3aacb99 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 Optimization Fuzzy Geographically Weighted Clustering with Gravitational Search Algorithm for Factors Analysis Associated with Stunting Subject The topic of the resource fuzzy geographically weighted clustering (FGWC); gravitational search algorithm (GSA); FGWC-GSA; stunting Description An account of the resource Stunting is a significant threat to the quality of human resources in Indonesia because stunting does not only involve physical growth disorders but can also cause children to be vulnerable to disease and experience disorders of brain development and intelligence. Many factors cause stunting, not only malnutrition in pregnant women and toddlers. Grouping can be done to make it easier to see the characteristics of the factors causing stunting in Indonesia. The grouping is done based on the similarity of the characteristics of the factors causing stunting in each province. This study used Fuzzy Geographically Weighted Clustering (FGWC) with Gravitational Search Algorithm (GSA) to group and assess the best cluster using the Partition Coefficient validity index, Classification Entropy, Separation Index, Xie & Beni's Index, and IFV Index. Furthermore, a difference test was conducted to determine the dominant factor causing stunting in the formed cluster. The results showed that the FGWC-GSA gave the best clustering results on the fuzziness value of 2 with the number of clusters 2. Cluster 1 consisted of 16 provinces, and cluster 2 consisted of 18 provinces. Based on the T-test, the variables of infants who received exclusive breastfeeding had significant differences between clusters. Therefore, cluster 2 is a cluster that has dominant problems related to exclusive breastfeeding. Creator An entity primarily responsible for making the resource Isran K. Hasan1 , Nurwan2 , Nur Falaq3 , Muhammad Rezky Friesta Payu4 Publisher An entity responsible for making the resource available Universitas Negeri Gorontalo 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/f526d0ccf320b3ff829795c379f87b03.pdf d6147f0591066bebc8e01d33519a7be5 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 Implementation of Word Recommendation System Using Hybrid Method for Speed Typing Website Subject The topic of the resource recommendation system, collaborative filtering, content based filtering, speed typing, website Description An account of the resource Typing is one of the most frequently done activities in society therefore a medium is necessary to help train typing words that are often mistyped. Methods used in this research are the Content-Based Filtering Algorithm to gather the words that have a similar pattern to the words that are often mistyped based on the user's previous typing records and the Collaborative Filtering Algorithm that uses other users typing pattern to recommend the words. The result of this study shows the Collaborative Filtering Algorithm was able to gather words that are hard to type by the user with an accuracy of 49.2%, dan the Collaborative Filtering able to predict the score on how difficult for the user to type a word with the result of Root Mean Square Error (RMSE) value of 0.82 and with the Root Mean Square Percentage Error (RMSPE) value of 30% from the actual value, and a website which is the combination of the two algorithms with the result of 28% of the total word that is recommended was indeed difficult to type by the user with the typing speed of 103 WPM, and 72.3% for the user that has a typing speed of 39 WPM. Creator An entity primarily responsible for making the resource Melinda Melinda1 , Maulana Imam Muttaqin2 , Yudha Nurdin3 , Al Bahri4 Publisher An entity responsible for making the resource available University of Syiah Kuala 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/ac2bed564b35fe08167d21a2a03670a1.pdf 8a87e7865ad9bf458032b05d2f09c43f 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 Development of Quantum Circuit Architecture on Quantum Perceptron Algorithm for Classification of Marketing Bank Data Subject The topic of the resource classification, quantum computing, quantum perceptron, architecture, quantum circuit Description An account of the resource The creation of quantum circuit architecture based on the quantum perceptron algorithm to classify marketing bank data is proposed in this work. A quantum circuit is a quantum gate made up of two quantum gates. Quantum bits are used in this study's computation. The primary proposed learning method was not ideal, which is the context of this study. The percentage of qubits measurement value is still 90.7 percent. It is essential to raise the value of the qubit rate. Using the IBM Quantum Experience quantum computer, researchers measured, trained, and tested the quantum circuit architecture. Bank marketing data from the UCI Machine Learning Repository was used. A quantum circuit architecture model results from this research the quantum circuit measurement results. Creator An entity primarily responsible for making the resource Mochamad Wahyudi1 , Solikhun2 Publisher An entity responsible for making the resource available Universitas Bina Sarana Informatika 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/4485524154a90c727a646a4328e05eb8.pdf 12f12c5a134ef7440e0c7cebf9180271 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 Herbal Leaves Classification Based on Leaf Image Using CNN Architecture Model VGG16 Subject The topic of the resource classification, herbal leaf, transfer learning, VGG16. Description An account of the resource Herbal leaves are a type that is often used by people in the health sector. The problem faced is the lack of knowledge about the types of herbal leaves and the difficulty of distinguishing the types of herbal leaves for ordinary people who do not understand plants. If any type of plant is used, it will have a negative impact on health. Automatic classification with the help of technology will reduce the risk of misidentification of herbal leaf types. To make identification, a precise and accurate herbal leaf detection process is needed. This research aims to facilitate the classification model of herbal leaf images with a higher accuracy value than previous research. Therefore, the proposed method in this classification process is one of the Transfer Learning methods, namely Convolutional Neural Network (CNN) with a pretrained VGG16 model. This research uses a dataset of herbal leaves with a total of 10 classes: Belimbing Wuluh, Jambu Biji, Jeruk Nipis, Kemangi, Lidah Buaya, Nangka, Pandan, Pepaya, Seledri and Sirih. The performance of the results of the proposed classification method on the test dataset using Classification Report shows an increase in the results of the previous research accuracy value from 82% to 97%. This research also applies Image Data Generator in the augmentation process which aims to improve the image of herbal leaves, reduce overfitting, and improve accuracy. Creator An entity primarily responsible for making the resource Bella Dwi Mardiana1 , Wahyu Budi Utomo2 , Ulfah Nur Oktaviana3 Publisher An entity responsible for making the resource available Agus Eko Minarno5 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/54e038839da12a180509695cfd98148f.pdf 5b7c8b561bead717288f0402beb94dd0 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 Mobile Application Development to Solve Vehicle Routing Problems in Marketing or Tour Trip Planning Subject The topic of the resource mobile application, optimization, software development, vehicle routing problem Description An account of the resource The vehicle routing problem (VRP) is a combinatorial optimization problem faced by transportation services related to pick up or delivery, such as industrial raw materials distribution, tour and travel, or travel routing problems in general. VRP is an NP-hard problem where the higher the dimensions of the problem will have a higher computational complexity. Without realizing it, VRP problem are often encountered every day. Therefore, it will be very useful if VRP solver is implemented in mobile application media. Previously, the Google Map application also provided a feature for determining route problems, but this did not solve the constraint problems that appeared in VRP. Therefore, the development of existing applications needs to be done. So, the aim of this work is developing a mobile application to get the shortest path and minimal cost in VRP problem. It is integrated by both Mapbox API and Google Maps API to get a real distance for modeling problem. The result show that the developed application can run well in all possibility condition. Creator An entity primarily responsible for making the resource Benni Agung Nugroho1 , Abidatul Izzah2* , Kunti Eliyen3 Publisher An entity responsible for making the resource available PSDKU Polinema di Kota Kediri 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