3 10 23 https://repository.horizon.ac.id/files/original/0a1fcaeca01c7cc9b1edac14883fc957.pdf 93e4560f438e34f708ef4993921cd1eb 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 6 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 Feature Expansion Word2Vec for Sentiment Analysis of Public Policy in Twitter Subject The topic of the resource sentiment analysis, feature expansion, word2vec, public policy Description An account of the resource Social media users, especially on Twitter, can freely express opinions or other information in the form of tweets about anything, including responding to a public policy. In a written tweet, there is a limit of 280 characters per tweet and this allows for problems such as vocabulary mismatches. Therefore, in this study, the feature expansion Word2vec method was applied to overcome when the vocabulary mismatches occur. This study implements and compares the Twitter sentiment analysis using the feature expansion Word2vec method and the baseline model. To perform classification on this sentiment data, two different machine learning algorithms including Support Vector Machine (SVM) and Logistic Regression (LR) are used to compare the model. The result is feature expansion Word2Vec with SVM classifier has a slightly better performance which succeeded in increasing the system accuracy up to 0,99% with 78,99% accuracy score, rather than LR classifier which achieved 78,31% accuracy score. Creator An entity primarily responsible for making the resource Alvi Rahmy Royyan1 , Erwin Budi Setiawan2 Publisher An entity responsible for making the resource available Telkom University Date A point or period of time associated with an event in the lifecycle of the resource 27 Februari 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 Indonesia Type The nature or genre of the resource Text https://repository.horizon.ac.id/files/original/e82f9cea772da62729540de359a86623.pdf 33297eb039e34b7a36a3557bf35fb831 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 6 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 Identifikasi Citra Pap Smear RepoMedUNM dengan Menggunakan K-Means Clustering dan GLCM Subject The topic of the resource Pap Smear, ThinPrep, Non-ThinPrep, RepoMedUNM, K-Means, GLCM Description An account of the resource Cervical cancer’s a gynecological malignancy in women that’s very dangerous, even causes death. Prevention through early detection of Pap smear test. It was carried out by pathologists with the help of a microscope still have obstacles in observations. There’re many studies on Pap smear image processing for helping pathologists in cell identification. Availability of Pap smear image dataset is needed in cervical cancer early detection research. The purpose of this study was to segment, feature extraction and classify 180 Pap smear images of RepoMedUNM. The method used to identify Pap smear images begins with preprocessing, namely changing the color in the image to L*a*b color, segmentation using the K-means method, extraction of 6 features, namely metric, eccentricity, contrast, correlation, energy, and homogeneity, and then identified by calculating the closest distance between the training data features and the test data features with the Euclidean distance. The result of identification ThinPrep Pap smear images in 3 classes achieve average accuracy of 93.33%, Non-ThinPrep Pap smear images in 2 classes achieve 90% average accuracy and the average accuracy of the overall in the 4 classes reached 92%. These results indicate that the proposed method can identify Pap smear images well. Creator An entity primarily responsible for making the resource Dwiza Riana1 , Sri Rahayu2 , Sri Hadianti3 , Frieyadie4 , Muhamad Hasan5 , Izni Nur Karimah6 , Rafly Pratama7 Publisher An entity responsible for making the resource available , Universitas Nusa Mandiri 2,3,6,7Informatika, Fakultas Teknologi Informasi, Universitas Nusa Mandiri 4,5Sistem Informasi, Fakultas Teknologi Informasi, Universitas Nusa Mandiri Date A point or period of time associated with an event in the lifecycle of the resource 1 februari 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 Indonesia Type The nature or genre of the resource Text https://repository.horizon.ac.id/files/original/b2c6d7199b9e06f7a8f0ff071dbd28f3.pdf 95182b34dbede0d7eeb43039cb8fd12e 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 6 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 Deteksi Penyakit Covid-19 Pada Citra X-Ray Dengan Pendekatan Convolutional Neural Network (CNN) Subject The topic of the resource COVID-19, convolutional neural network, CNN, Residual Network, ResNet Description An account of the resource The Coronavirus (COVID-19) pandemic has resulted in the worldwide death rate continuing to increase significantly, identification using medical imaging such as X-rays and computed tomography plays an important role in helping medical personnel diagnose positive negative COVID-19 patients, several works have proven the learning approach in-depth using a Convolutional Neural Network (CNN) produces good accuracy for COVID detection based on chest X-Ray images, in this study we propose different transfer learning architectures VGG19, MobileNetV2, InceptionResNetV2 and ResNet (ResNet101V2, ResNet152V2 and ResNet50V2) to analyze their performance, testing conducted in the Google Colab work environment as a platform for creating Python-based applications and all datasets are stored on the Google Drive application, the preprocessing stages are carried out before training and testing, the datasets are grouped into theNormal and COVID folders then combined m become a set of data by dividing them into training sets of 352 images, testing 110 images and validating 88 images, then the detection results are labeled with the number 1 means COVID and the number 0 for NORMAL. Based on the test results, the ResNet50V2 model has a better accuracy rate than other models with an accuracy level of about 0.95 (95%) Precision 0.96, Recall 0.973, F1-Score 0.966, and Support of 74, then InceptionResNetV2, VGG19, and MobileNetV2, so that ResNet50V2-based CNNs can be used as initial identification for the classification of a patientinfected with COVID or NORMAL. Creator An entity primarily responsible for making the resource Mawaddah Harahap*, 2Em Manuel Laia, 3Lilis Suryani Sitanggang, 4Melda Sinaga, 5Daniel Franci Sihombing, 6Amir Mahmud Husein 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 27 februari 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 Indonesia Type The nature or genre of the resource Text