1 10 36 https://repository.horizon.ac.id/files/original/906af2c93f92a9023fa76a47065e961a.pdf daa9458859c5e8c039802a9727678668 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 Studi Perbandingan Jaringan Blockchain sebagai Platform Sistem Rating Subject The topic of the resource : blockchain, rating, smart tourism Description An account of the resource In the tourism industry, reputation is important information that influence customer behavior. Some services, such as hotels, take advantage of feedback from customers. This research aims to develop a review system by utilizing blockchain and machine learning for sustainable tourism. As proof of concept, a comparison method is carried out between several existing Blockchain networks. The system prototype then implemented using Hyperledger blockchain network, so that measurement of its performance is possible. The results show the feasibility of the blockchain network to be used for a rating system, although several aspects need to be considered in its implementation Creator An entity primarily responsible for making the resource Lathifah Arief 1 , Tri A. Sundara 2 , Heru Saputra3 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 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/3b35d0ac5796c10a81eb3c22ed244f77.pdf dabf33d02a0a11bad56dd6ca8d5da533 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 Pengenalan Logo Kendaraan Menggunakan Metode Local Binary Pattern dan Random Forest Subject The topic of the resource : recognition system, vehicle logo, local binary pattern, random forest Description An account of the resource The vehicle logo is one of the features that can be used to identify a vehicle. Even so, a lot of Intelligent Transport System which are developed nowadays has yet to use a vehicle logo recognition system as one of its vehicle identification tools. Hence there are still cases of traffic crimes that haven't been able to be examined by the system, such as cases of counterfeiting vehicle license plates. Vehicle logo recognition itself could be done by using various feature extraction and classification methods. This research project uses the Local Binary Pattern feature extraction method which is often used for many kinds of image recognition systems. Then, the classification method used is Random Forest which is known to be effective and accurate for various classification problems. The data used for this study were as many as 2000 vehicle logo images consisting of 5 brand classes, namely Honda, Kia, Mazda, Mitsubishi, and Toyota. The results of the tests carried out obtained the best accuracy value of 88.89% for the front view logo image dataset, 77.03% for the side view logo image dataset, and 83% for the dataset with both types of images. Creator An entity primarily responsible for making the resource Alda Putri Utami1 , Febryanti Sthevanie2 , Kurniawan Nur Ramadhani3 Publisher An entity responsible for making the resource available n Nur Ramadhani3 1,2,3Program Studi S1 Informatika, Fakultas Informatika, Universitas Telkom 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/2ad48d0a606bcf93e33ef0e7bdd703d5.pdf 6a73f9ff202d3dd2b3f9ea7bc42dab81 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 Deteksi Masker Wajah Menggunakan Metode Adjacent Evaluation Local Binary Patterns Subject The topic of the resource classification, COVID-19, face mask Description An account of the resource The COVID-19 pandemic is still ongoing until 2021 and is likely to continue until an uncertain time. This arises because the spread of the SARS-CoV-2 virus also continued to occur in the community. Of the five points in 5M that has been initiated by the government, the focus of this study is the use of face masks. In this study, an image-based automatic mask detection method using a classification approach is proposed. This method can be used in automated systems to increase public discipline in wearing masks to suppress the spread of the SARS-CoV-2 virus. The classes used in the classification are "with mask" and "without mask". The adjacent evaluation local binary patterns (AELBP) method, which is an extension of the local binary patterns (LBP) method, is used to extract the texture features of each image. Tests were carried out on 2,172 facial images of various sizes, facial accessories, and facial expressions. The test results using the AELBP method show that the accuracy and F-measure are 98.39% and 98.08%, respectively. This result is better than other methods which are also evaluated. In addition, testing of the AELBP method execution time shows that this method is feasible to use on real systems. Creator An entity primarily responsible for making the resource Randy Cahya Wihandika Publisher An entity responsible for making the resource available Universitas Brawijaya 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/192c841213ac6a2d328cd831011637e4.pdf 4e3d11e4011207986b9d442c0dd85f47 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 Pengembangan Metode Autentikasi pada Sistem Presensi Berbasis Aplikasi Mobile Subject The topic of the resource attendance system, MAC address, IP address, BSSID, Wi-Fi Description An account of the resource Research has been carried out on a mobile-based presence system authentication method using MAC addresses, BSSID and IP addresses for (Wi-Fi) networks. This study aims to develop an authentication method on the attendance system that meets two authentication requirements, namely the suitability of employee identity and location suitability, so that the attendance process becomes easy, effective, fast, and can reduce fraud. The employee's identity can be obtained from the MAC address of the smartphone that has been previously registered, while the employee's location during the attendance process can be confirmed to be in the company environment by checking the BSSID data and IP of the Wi-Fi network connected to the smartphone. The data is then compared with MAC address data from all Wi-Fi networks installed in the company area. RAD is used as a development model because it is simple and fast. Overall, employee identification and site checking as authentication of the developed system went well. Other than that, every function on the system works well. Furthermore, the results of the user experience evaluation using the UEQ questionnaire received an average score above 0.8 on 6 scales. This shows that the system has attractiveness, perspicuity, efficiency, dependability, stimulation and novelty Creator An entity primarily responsible for making the resource Komang Sri Utami1 , Nyoman Putra Sastra2 , Dewa Made Wiharta3 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 20-08-2021 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/ecaf0e81820716b0e0bdab8bf989992e.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 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 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 In 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 more consistent 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 Sibaroni 2 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 20-08-2021 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/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/e65a4eb687376411d5171bcd259f4d3e.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 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 Penerapan Convolutional Neural Network pada Citra Rontgen Paru-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-19 was officially declared as a pandemic by the WHO on March 11, 2020. For COVID-19, the testing methods commonly used are the Antibody Testing and RT-PCR Testing. 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 performance of CNN in classifying x�ray image of a person’s chest to determine whether the person is suffered from COVID-19 or 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 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 24-08-2021 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/52c80e50b0501edc5a93ba8da0c705f4.pdf 0754d2d29670ae70e7330147d88a41e4 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 Implementation of Verification and Matching E-KTP with Faster R-CNN and ORB Subject The topic of the resource Detection, Matching, Identity Card, EKTP, ORB, Faster R-CNN. Description An account of the resource An EKTP image repository can be a helpful tool to assist human operators in EKTP image pair checking. But, such a repository needs a solid validation that has verification and matching uploaded images. To solve this problem, this paper implementing a detection model using Faster R-CNN and a matching method using ORB (Oriented FAST and Rotated BRIEF) and KNN-BFM (K-Nearest Neighbor Brute Force Matcher). The goal of the implementations is to reach both an 80% mark of accuracy and prove matching using ORB only can be a replaced OCR technique. The implementation accuracy resultsin the detection model reach mAP (Mean Average Precision) of 94%. But, the matching process only achieves an accuracy of 43,46%. The matching process using only image feature matching underperforms the previous OCR technique but improves processing time from 4510ms to 60m). Image matching accuracy has proven to increase by using a high-quality dan high quantity dataset, extracting features on the important area of EKTP card images Creator An entity primarily responsible for making the resource Muhammad Muttabi Hudaya1 , Siti Saadah2 , Hendy Irawan3 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 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