1 10 36 https://repository.horizon.ac.id/files/original/371404b59a23d086b4904c3b90c94da4.pdf c1d95ff56c7ebde17e84379a7d8bf522 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 5 NO 5 (2021) 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 Algoritma Multinomial Naïve Bayes Untuk Klasifikasi Sentimen Pemerintah Terhadap Penanganan Covid-19 Menggunakan Data Twitter Subject The topic of the resource : opinion, sentiment, twitter, covid-19, multinomial naïve bayes Description An account of the resource Currently, the spread of information Covid-19 is spreading rapidly. Not only through electronic media, but this information is also disseminated by user posts on social media. Due to the user text posted is varies greatly, it’s needs a special approach to classify these types of posts. This research aims to classify the public sentiment towards the handling of COVID-19. The data from this study were obtained from the social media application i.e., Twitter. This study uses a derivative of the Naïve Bayes algorithm, namely Multinomial Nave Bayes to optimize the classification results. Three class labels are used to classify public sentiment namely positive, negative, and neutral sentiments. The stage starts with text preprocessing; cleaning, case folding, tokenization, filtering and stemming. Then proceed with weighting using the TF-IDF approach. To evaluate the classification results, data is tested using confusion matrix by testing accuracy, precision, and recall. From the test results, it is found that the weighted average for precision, recall and accuracy is 74%. Research shows that the accuracy of the proposed method has fair classification levels. Creator An entity primarily responsible for making the resource Yuyun1 , Nurul Hidayah2 , Supriadi Sahibu3 Publisher An entity responsible for making the resource available STMIK Handayani Makassar Date A point or period of time associated with an event in the lifecycle of the resource 30-08-2021 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/77f639c63983c756b1da9a36caac831d.pdf 63ce31039ccc4a1d17195efeb0686642 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 5 NO 5 (2021) 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 Analisis Akun Twitter Berpengaruh terkait Covid-19 menggunakan Social Network Analysis Subject The topic of the resource Centrality, Covid-19, Follower Rank, Social Network Analysis, Twitter Description An account of the resource Covid-19 is a disease of the virus that is shaking the world and has been designated by WHO as a pandemic. This case of Covid-19 can be a place of dissemination of disinformation that can be utilized by some parties. The dissemination of information in this day and age has turned to the internet, namely social media, Twitter is one of the social media that is often used by Indonesians and the data can be analyzed. This study uses the social network analysis method, conducted to be able to find nodes that affect the ongoing interaction in the interaction network of information dissemination related to Covid-19 in Indonesia and see if the node is directly proportional to the value of its popularity. As well as to know in identifying the source of Covid-19 information, whether dominated by competent Twitter accounts in their fields. The data examined 19,939 nodes and 12,304 edges were taken from data provided by the web academic.droneemprit.id on the project "Analisis Opini Persebaran Virus Corona di Media Sosial", using the period of December 2019 to December 2020 on social media Twitter. The results showed that the @do_ra_dong account is an influential actor with the highest degree centrality of 860 and the @detikcom account is the actor with the highest popularity value of follower rank of 0.994741605. Thus actors who have a high degree of centrality value do not necessarily have a high follower rank value anyway. The study ignores if there are buzzer accounts on Twitter. Creator An entity primarily responsible for making the resource Aprillian Kartino1 , M. Khairul Anam2 , Rahmaddeni3 , Junadhi4 Publisher An entity responsible for making the resource available STMIK Amik Riau Date A point or period of time associated with an event in the lifecycle of the resource 20-08-2021 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/275319c1fed4df04a6a063d74dd58bcf.pdf 3aa4f997e75a380985abd539f1478d2c 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 5 NO 5 (2021) 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 Analisis Dampak Perubahan Artefak Kebutuhan Berdasarkan Kedekatan Semantik Pada Pengembangan XP Subject The topic of the resource XP, traceability link, TF-IDF, semantic proximity, natural language processing Description An account of the resource The Extreme Programming (XP) development method is popular because of the flexibility of the development process, it can accommodate changes quickly. But this method has a weakness in terms of documentation. It is expected that the speed of discovering which parts of the source code need to be changed will be greatly improved by analyzing the impact of changes on the requirements document. In this study, a method of analyzing the impact of changes is proposed by tracing changes in the artifact of the need to find out the source code that occurs. Early language methods and semantic approaches are used. Based on the proximity of the semantics, it will be analyzed to find out the elements in the source code that use the Spearman Correlation Coefficient. The test dataset in this study consisted of the source code in the PHP programming language as well as the functional requirements of the software. Requirements change list is generated by analysis of the latest 2 (two) expert versions of the source code. The changing needs are described in a user story document. Based on the test results in this study, the average precision was 0.1725 and the average recall value was 0.6041. Creator An entity primarily responsible for making the resource Arrijal Nagara Yanottama1 , Siti Rochimah2 Publisher An entity responsible for making the resource available Institut Teknologi Sepuluh Nopember Date A point or period of time associated with an event in the lifecycle of the resource 20-08-2021 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/852d2528d157a97720ae895738e64f85.pdf 410736d436291bd2c9d68021981761a7 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 5 NO 5 (2021) 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 Analisis dan Implementasi Algoritma Asimetris Dual Modulus RSA (DM�RSA) pada Aplikasi Chat Subject The topic of the resource RSA algorithm, Dual Modulus RSA, Kraitchik Factorization Description An account of the resource The RSA algorithm is one of the cryptographic algorithms with an asymmetric model where the algorithm has two keys, namely the public key and the private key. However, as time goes on, these algorithms are increasingly exposed to security holes and make this algorithm vulnerable to being hacked by people who do not have authority. The vulnerability stems from the algorithm's public keys (e and n). The strength of the RSA algorithm is based on the difficulty of factoring two prime numbers that are generated during the key generation process, if these values can be known using certain methods, the public key and private key values will be found. Therefore, there are many studies that improvise the RSA algorithm, one of which is the Dual Modulus RSA (DM-RSA) algorithm. The algorithm uses four prime numbers which produce 2 modulus and 4 keys (2 public keys and 2 private keys). From the results of the Kraitchik factorization test, it was found that the DM-RSA algorithm was proven to be more resistant up to 2 times or even more than the standard RSA algorithm. This is evidenced by the fact that the value of n is 24 bits, the RSA algorithm can last up to 63204 ms (1 minute 22 seconds) while the Dual Modulus RSA algorithm lasts up to 248494123 ms (142 minutes 47 seconds) Creator An entity primarily responsible for making the resource Aminudin1 , Ilyas Nuryasin2 Publisher An entity responsible for making the resource available Universitas Muhammadiyah Malang Date A point or period of time associated with an event in the lifecycle of the resource 25-08-2021 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/b5d617e2789bad948c6d3a2f6d789a75.pdf ba563f86d99cd4f7cd7beff68e020e87 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 5 NO 5 (2021) 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 Analisis Kinerja Skema MPEG Surround Pada Pengkodean Audio 22 Kanal Menggunakan Bitrate 1000 - 2000 Kbps Subject The topic of the resource 22 Channel Audio Coding, Matlab, MPEG Surround, Downmixing, Objective Difference Grade Description An account of the resource MPEG surround is one of the most popular multichannel audio coding standards used today. In producing quality sound, there are several parts that are influential, but those that are directly related to sound reproduction, one of which is the method of downmixing, mixing the right channels will improve the quality of the sound produced. Because this research is closely related to data computing, the Matrix Laboratory (MATLAB) application is used. There are 2 stages carried out in the MATLAB application, namely the encoding and decoding process. In the encoding process in MATLAB, each audio channel is set according to a previously designed scheme. The results of this encoding are then decoded, so that the ODG value of each tested audio channel is obtained. In his presentation, the design and analysis of several domnmixing schemes was carried out. There are 5 schemes tested and to determine the performance of each scheme, these schemes are tested at high bitrates, from the range of 1000 Kbps to 2000 Kbps. Based on the test, it was found that the effect of increasing the bitrate is directly proportional to the resulting audio quality. During testing Scheme-5 has a better level of stability among all downmixing schemes and the average percentage increase in Objective Difference Grade (ODG) for all audio samples is 9.6927 %. Creator An entity primarily responsible for making the resource Vitral JR1 , Ahmad Ridwan2 , Habib Satria3 , Ikhwana Elfitri4 Publisher An entity responsible for making the resource available Universitas Andalas Date A point or period of time associated with an event in the lifecycle of the resource 25-10-2021 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/dc25f5edd60ceb092411b09b7d06722f.pdf fc36521626b228ed8dc4cb039d9fb059 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 5 NO 5 (2021) 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 Analisis Sentimen Tweet Vaksin COVID-19 Menggunakan Recurrent Neural Network dan Naïve Bayes Subject The topic of the resource Sentiment Analysis, Vaccine COVID-19, TF-IDF, RNN, Naïve Bayes Description An account of the resource COVID-19 has become a global pandemic including Indonesia, so the government is taking vaccinations as a preventive measure. The public's response to this continues to appear on social media platforms, one of which is Twitter. Tweets about the COVID-19 vaccine have generated various kinds of positive and negative opinions in the community. Therefore, it is very important to detect and filter it to prevent the spread of incorrect information. Sentiment analysis is a method used to determine the content of a dataset in the form of negative, positive or neutral text. The dataset in this study was obtained from 5000 COVID-19 vaccine tweets with the distribution of 3800 positive sentiment tweets, 800 negative sentiment tweets and 400 neutral sentiment tweets. The dataset obtained is then pre-processed data to optimize data processing. There are 4 stages of pre�processing, including remove punctuation, case folding, stemming and tokenizing. This study examines the performance of RNN and Naïve Bayes by adding the TF-IDF (Term Frequency-Inverse Document Frequency) technique which aims to give weight to the word relationship (term) of a document. The test results show that RNN (TF-IDF) has a greater accuracy of 97.77% compared to Naïve Bayes (TF-IDF) of 80%. Creator An entity primarily responsible for making the resource Merinda Lestandy1 , Abdurrahim Abdurrahim2 , Lailis Syafa’ah3 Publisher An entity responsible for making the resource available Universitas Muhammadiyah Malang Date A point or period of time associated with an event in the lifecycle of the resource 26-08-2021 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/b1961cdbee28bbf761bfd24fdc6db0ac.pdf 60967a43b40f9efcedf055256fc2fa8f 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 5 NO 5 (2021) 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 Aspect Level Sentiment Analysis on Zoom Cloud Meetings App Review Using LDA Subject The topic of the resource LDA, SVM, review, aspect Description An account of the resource During the Covid-19 pandemic, almost all community activities are conducted from home. Therefore, video conference technology is needed for people to carry out their normal activities from home. One of the video conference applications is ZOOM Cloud Meetings. Applications certainly have been reviewed given by their users as a reference for new users and companies of the application to know the application’s performance. However, in reviews, some constraints are the number of reviews as well as irregular. Therefore, a solution is needed with sentiment analysis that aims to classify the reviews of the application to be organized by categorizing positive or negative sentiment. In this study, aspect-based sentiment analysis was conducted on ZOOM Cloud Meetings app reviews from Google Play Store. The analysis’s result of the review data obtained three aspects, namely aspects of usability, system, and appearance. The modeling topic used is the Latent Dirichlet Allocation (LDA) method and classification using the Support Vector Machine (SVM). This research resulted in the best performance with the best parameters resulting in the performance accuracy of usability aspect is 88.83%, system aspect with 91.2%, appearance aspect with 94.78%, and performance accuracy of all aspects 91.61%. Creator An entity primarily responsible for making the resource Janu Akrama Wardhana1 , Yuliant Sibaroni2 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 Telkom University 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/d2736b473c013d61f358bc68617021b9.pdf 8574bc2b5c6cc4ee11dc3cb08cd22edf 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 5 NO 5 (2021) 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 Big Cats Classification Based on Body Covering Subject The topic of the resource CLAHE, Segmentation, PHOG, Support Vector Machines Description An account of the resource The reduced habitat owned by an animal has a very bad impact on the survival of the animal, resulting in a continuous decrease in the number of animal populations especially in animals belonging to the big cat family such as tigers, cheetahs, jaguars, and others. To overcome the decline in the animal population, a classification model was built to classify images that focuses on the pattern of body covering possessed by animals. However, in designing an accurate classification model with an optimal level of accuracy, it is necessary to consider many aspects such as the dataset used, the number of parameters, and computation time. In this study, we propose an animal image classification model that focuses on animal body covering by combining the Pyramid Histogram of Oriented Gradient (PHOG) as the feature extraction method and the Support Vector Machine (SVM) as the classifier. Initially, the input image is processed to take the body covering pattern of the animal and converted it into a grayscale image. Then, the image is segmented by employing the median filter and the Otsu method. Therefore, the noise contained in the image can be removed and the image can be segmented. The results of the segmentation image are then extracted by using the PHOG and then proceed with the classification p Creator An entity primarily responsible for making the resource Fernanda Januar Pratama1 , Wikky Fawwaz Al Maki2 , Febryanti Sthevanie3 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 31-10-2021 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/2e04cf2047f61879c57ec72b866f0249.pdf 6ece98eb391bbfd6c50eac600de5e8e9 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 5 NO 5 (2021) 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 Cancer Detection based on Microarray Data Classification Using FLNN and Hybrid Feature Selection Subject The topic of the resource cancer detection, microarray, information gain, genetic algorithm, hybrid Description An account of the resource Cancer is one of the second deadliest diseases in the world after heart disease. Citing from the WHO's report on cancer, in 2018 there were around 18.1 million cases of cancer in the world with a total of 9.6 million deaths. Now that bioinformatics technology is growing and based on WHO’s report on cancer, an early detection is needed where bioinformatics technology can be used to diagnose cancer and to help to reduce the number of deaths from cancer by immediately treating the person. Microarray DNA data as one of the bioinformatics technology is becoming popular for use in the analysis and diagnosis of cancer in the medical world. Microarray DNA data has a very large number of genes, so a dimensional reduction method is needed to reduce the use of features for the classification process by selecting the most influential features. After the most influential features are selected, these features are going to be used for the classification and predict whether a person has cancer or not. In this research, hybridization is carried out by combining Information Gain as a filtering method and Genetic Algorithm as a wrapping method to reduce dimensions, and lastly FLNN as a classification method. The test results get colon cancer data to get the highest accuracy value of 90.26%, breast cancer by 85.63%, lung cancer and ovarian cancer by 100%, and prostate cancer by 94.10%. Creator An entity primarily responsible for making the resource Ghozy Ghulamul Afif1 , Adiwijaya2 , Widi Astuti3 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 26-08-2021 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/a467b80576d7d93b9a5ddb2212b8a008.pdf ce12c93f6a246dfa7e64c3cc54aff637 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 5 NO 5 (2021) 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 Chicken Egg Fertility Identification using FOS and BP-Neural Networks on Image Processing Subject The topic of the resource Backpropagation, Feature Extraction, First Order Statistical, Classification, Image Processing Description An account of the resource This article aims to test FOS (first-order statistical) in extracting features of embryonated eggs. This test uses the initial step of image processing to get the best input image in feature extraction. The image processing method starts from the image acquisition process, then improves with image preprocessing and segmentation. Image acquisition in this study uses the concept of egg candling in a dark place captured with a smartphone camera. The acquisition results are improved by image preprocessing using gray scaling, image enhancement (by Histogram Equalization), and segmentation of chicken egg image. The segmentation results were extracted using FOS with five parameters: mean, entropy, variance, skewness, and kurtosis. Based on the calculation of these parameters, it is graphed and shows the difference in patterns between fertile and infertile eggs. However, some eggs have a similar pattern, thus affecting the identification process. The identification process used neural networks by the backpropagation method for training and testing. The training results provide an accuracy value of 100% of all training data; however, 80% of the new test data obtained test results at testing. This test is carried out with 100 data, 50 each for training and test data. Based on the test results, which significantly affect the level of accuracy is the feature extraction method. FOS pattern in detecting the fertility of chicken eggs by BP Neural Network is still categorized as low, so it is necessary to improve methods to get maximum results Creator An entity primarily responsible for making the resource Shoffan Saifullah1 , Andiko Putro Suryotomo2 Publisher An entity responsible for making the resource available Universitas Pembangunan Nasional Veteran Yogyakarta Date A point or period of time associated with an event in the lifecycle of the resource 25-10-2021 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