1 10 15 https://repository.horizon.ac.id/files/original/c881a88ef3f65efb1d848050a9bcb4d4.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 4 (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 NaïveBayesUntuk Klasifikasi Sentimen PemerintahTerhadap PenangananCovid-19 Menggunakan DataTwitter 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’sneeds 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 foundthat 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 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/issue/view/24 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 agustus 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/f32f63b41b45501ed08ff35b5b5c508b.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 4 (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 Sentiment Analysis, Vaccine COVID-19, TF-IDF, RNN, Naïve Bayes Subject The topic 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 obtainedfrom 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%. Description An account of the resource Sentiment Analysis, Vaccine COVID-19, TF-IDF, RNN, Naïve Bayes Creator An entity primarily responsible for making the resource Merinda Lestandy1, Abdurrahim Abdurrahim2, Lailis Syafa’ah3 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/issue/view/24 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 agustus 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/f89ba569e6aa4562537ee69598ff5803.pdf 761a63832259689a4b4d5efd70677cd4 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 4 (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 Penerapan Convolutional Neural Networkpada Citra RontgenParu-Paru untuk Deteksi SARS-CoV-2 Subject The topic of the resource convolutional neural network, covid-19, image, chest, x-ray Description An account of the resource COVID-19was officially declared as a pandemic by the WHOon March 11, 2020. For COVID-19, the testing methods commonly used are the Antibody Testingand RT-PCRTesting. Both methods are considered to be the most effective in determining whether a person has been suffered from COVID-19 or not. However,alternative testing methods need to be tried. One of them is using the Convolutional Neural Network. This study aims to measure the performanceof CNN in classifying x-ray image of a person’s chest to determine whether the person is suffered from COVID-19or not. The CNN model that was built consists of 1 convolutional 2D layer, 2 activation layers, 1 maxpooling layer, 1 dropout layer, 1 flatten layer, and 1 dense layer. Meanwhile, the chest x-ray image dataset used is the COVID-19 Radiography Database. This dataset consists of 3 classes, i.e. COVID-19 class,NORMAL class, and VIRAL_PNEUMONIA. The experiments consisted of 4 scenarios and were carried out using Google Colab. Based on the experiments, the CNN model can achieve an accuracy of 98.69%, a sensitivity of 97.71%, and a specificity of 98.90%. Thus, CNN has a very good performance to classify the disease based on a person’s chest x-ray Creator An entity primarily responsible for making the resource Bambang Pilu Hartato Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/issue/view/24 Publisher An entity responsible for making the resource available Universitas Amikom Purwokerto Date A point or period of time associated with an event in the lifecycle of the resource 24 agustus 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/d03a84dce91822ea1d41d14403dcd188.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 4 (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 PadaPengembangan 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 Rochimah Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/issue/view/24 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 agustus 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/4ce4347066f25725821a247c267e0247.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 4 (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, Twitte 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 followerrank 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, Junadhi Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/issue/view/24 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 agustus 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/9c4ab937b76854bcd6add66922e8fef1.pdf bd263585f7e7b2a138dff1a27fe7c3eb 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 4 (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 Klasifikasi Citra Burung LovebirdMenggunakan Decision Treedengan Empat Jenis Evaluasi Subject The topic of the resource Lovebird, ANN, Decision Tree, Precision, Recall, F-measure Description An account of the resource Lovebird is a pet that many people in Indonesia have known. The diversity of species, coat color, and body shape gives it its charm. As well in this lovebird bird has its uniqueness of various rare colors. However, many ordinary people have difficultydistinguishing the types of lovebirds. This research is needed to improve previous studyperformance inclassifying lovebird images using the Decision Tree J48 algorithm with 4 types of evaluation. In this case,also to reduce the stage of feature extraction to speed up the computational process. Based on availablecomparisons, the results obtained at the same split ratio with a comparison of 60:40 in Decision Tree J48 have the precision of 1,000, recall of 1,000, f-measure of 1,000,and accuracy value of 100%. Then the Artificial Neural Network with a split ratio of 60:40 has a precision of 0.854, recall of 0.843, f-measurement of 0.841,and anaccuracy value of 84.25%. These results prove that by testing the first-level extraction on color features, Decision Tree J48 is superior in classifying images of lovebird species, and Decision Tree J48 canimprove performance and produce the best accuracy Creator An entity primarily responsible for making the resource Aviv Yuniar Rahman Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/issue/view/24 Publisher An entity responsible for making the resource available Universitas Widyagama Malang Date A point or period of time associated with an event in the lifecycle of the resource 20 agustus 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/c0d24bb8a0c944c86f06e4a05d7b22ff.pdf b3acbd21a34e79ed8efca290ba7c7754 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 4 (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 Semantic Approach for Big Five PersonalityPredictiononTwitter Subject The topic of the resource Big Five Personality, BERT, SVM,LIWC Description An account of the resource Personality provides a deepinsight of someone and hasanimportant part in someone’s job performance. Predicting personality throughsocial media has been studied onseveral research.The problem is how to improve the performance of personality prediction system. The purpose of this research is to predict personalityon Twitter usersand increase the performance of thepersonality predictionsystem. An online survey using Big Five Inventory (BFI) questionnaire has been distributed and gathered 295Twitteruserswith 511,617tweets data. In this research, we experimenton twodifferent methods using SupportVector Machine (SVM),and the combination of SVM and BERTas the semantic approach.This research also implementsLinguistic Inquiry Word Count (LIWC) asthe linguistic feature for personality prediction system.Theresults showed thatcombination of these two methods achieve 79.35% accuracyscore and with the implementation of LIWC can improvethe accuracy score up to 80.07%.Overall, these results showed thatthecombination of SVM and BERTas the semantic approach with the implementation of LIWCis recommended to gain a better performance for the personality prediction system Creator An entity primarily responsible for making the resource GhinaDwi Salsabila1, Erwin Budi Setiawan Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/issue/view/24 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 20 agustus 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/c31d9bf3769fefa314b9a8496df7f44f.pdf 81cb6a387516d441235685f4d3725899 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 4 (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 Sentiment Analysis of Public Opinion Related to Rapid Test Using LDA Method Subject The topic of the resource rapid test, Twitter, LDA, sentiment, corona Description An account of the resource n 2020 the world will be shocked by an outbreak of a disease that has developed tremendously. This disease is the Coronavirus. The Indonesian government, in overcoming conducted a Rapid early detection test in the spread of the Coronavirus. The steps of the Indonesian government have received rejection in several areas because people consume hoax news on social media. Indonesians widely use Twitter in conversations about the Coronavirus. Previous research was carried out using large-scale data, which affected the performance of the topic extraction method. The classification used resulted in poor accuracy using LDA to find the probability of topics in existing documents. LDA excels in large-scale data processing and is moreconsistent in generating the topic proportion value and word probability. Aspect-based sentiment analysis on public opinion regarding the rapid test on Twitter using LDA can determine aspects and public opinion on the rapid test. The test results of this study obtained 7000 tweets, four aspects of the results of topic using LDA, and getting the best accuracy using the RBF kernel by 95%. The sentiment of the Indonesian people towards the Rapid test is positive, with 4,305 sentiments Creator An entity primarily responsible for making the resource Viny Gilang Ramadhan1, Yuliant Sibaroni2 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/issue/view/24 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 20 agustus 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/ea049502b4d25f900d492c2022764054.pdf b38f5ebbe85d277b55ad14c59ce14b1b 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 4 (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 mplementasi Sistem Otentikasi Dokumen Berbasis Quick Response(QR) Codedan Digital Signature Subject The topic of the resource Digital Signature, Quick Response(QR) code, Secure Hash Algorithm-256 (SHA-256), Rivest-Shamir-Adleman (RSA), Error correction. Description An account of the resource The authenticityand integrityof documents areessentialindata exchange and communication. Digital documents must be verifiable for their authenticity and integrityby all parties that use the documents. Generally, digital documents can be authenticated by using digital signatures. This study aims to implement adocument authentication system based on Quick Response(QR) code and digital signature. As the case study, thedocument authentication system is implementedto generate digital signatures forstudent’scertificate documents. Furthermore, the system can also verifythe authenticityof the certificate documents. Creatinga digital signature requires ahash function algorithm forgeneratingthe message digestof the document. In addition, an algorithm togeneratethe public key and the private key used in the encryption/decryption of the message digest is also needed. Thehashfunction utilizedin this study is the Secure Hash Algorithm-256 (SHA-256), while the algorithm used for encryption/decryption is theRivest-Shamir-Adleman (RSA)algorithm. The system is evaluated by verifying 30 student certificate documents, of which 15of them were certificates with QR code signature generated by the system and the other 15were certificates with QR code signature generated usinga random QR code generator. The system’stesting results demonstrate thatthe systemcan ensurethe authenticityand integrityof the signed certificate documents toprevent document falsification. All documents that contain random QR codes were correctly identified asfalse documents Creator An entity primarily responsible for making the resource Antika Lorien1, Theophilus Wellem2 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/issue/view/24 Publisher An entity responsible for making the resource available Universitas Kristen Satya Wacana Date A point or period of time associated with an event in the lifecycle of the resource 20 agustus 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/9470e5aaebb459aa02362832fc18f76a.pdf 4c02834b36a90d3b2aad1e1a833de1d7 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 4 (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 lasifikasi Citra Daging Menggunakan DeepLearningdengan Optimisasi Hard Voting Subject The topic of the resource Deep Leaning, Ensemble Learning, Image Recognition, Transfer Learning Description An account of the resource Meat is a staple food for some Indonesian people, apart from the taste, meat also contains vitamins and minerals that are good for the human body, however, not all meat can be consumed by the Indonesian people. the texture and color of beef, pork and mutton have similarities and tend tobe similar, therefore a system is needed to recognize the three types of meat. In this study, the authors use various types of Deep Learning architecture such as Resnet-50, VGG-16, VGG-19 and Densenet-121 with Hard Voting to improve the performance of Deep Learning in recognizing the three types of meat. The results show that Resnet-50 with Hard Voting can outperform Deep Learning Resnet-50, VGG-16, VGG-19 and Densenet-121-with f1 score 98.88%, precision 98.89% and recall 98.88%. in image classification of pork, beef and mutton Creator An entity primarily responsible for making the resource Made Bramasta Vikana Putra1, I Putu Agung Bayupati2, Dewa Made Sri Arsa Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/issue/view/24 Publisher An entity responsible for making the resource available Universitas Udayana Date A point or period of time associated with an event in the lifecycle of the resource 20 agustus 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