1 10 19 https://repository.horizon.ac.id/files/original/56bd1ad59102347aaee8622da17a4e8e.pdf 85d20a8031104ae7a0487dcd85418b8c 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 3 (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 Factors Affecting the PeduliLindungi User Experience Based on UX Honeycomb Subject The topic of the resource PedulliLindungi, User Experience, UX Honeycomb Description An account of the resource With background of Covid-19 pandemic, Indonesian state trying to make various efforts, so people comply health protocols. One of them is through PeduliLindungi application. PeduliLindungi has 3 main functions, namely tracing, tracking, warning and fencing. However, PeduliLindungi is deemed unable to meet user needs in terms of appearance and experience provided. This study aims to find factors that affect user experience in PeduliLindungi app based on UX Honeycomb. UX Honeycomb is a tool that can explain various aspects of user experience design in 7 indicators and grouped into 3 variables. The 3 variables are Think (useful, valuable, credible), Feel (desirable, credible), and Use (findable, accessible, usable). This study uses primary data by distributing online questionnaires to 404 respondents contains 15 statements that represent all UX Honeycomb variables, with 5 scales of answer choices namely strongly disagree/disagree/neutral/agree/strongly agree. From the calculation results, it is found that all variables and indicators significantly affect user experience with greatest level of influence being on the Think variable at 0,418, the second Use at 0,219, and the last is Feel at 0,151. Further research is expected can measure the level of influence on user experience by making direct comparisons with similar health apps. Creator An entity primarily responsible for making the resource Adhim Jati Kusuma1 , Pantjawati Sudarmaningtyas2 , Antok Supriyanto3 1,2Information System Department, Faculty of Technology and Informatics, Universitas Dinami Publisher An entity responsible for making the resource available Management Department, Faculty of Economic and Business, Universitas Dinamika Date A point or period of time associated with an event in the lifecycle of the resource 15-07-2022 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/641c963aed05e5de8f1c11096e8adda8.pdf c45140a646c8218b7b119de63dc28803 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 3 (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 Performance Analysis of Hybrid Machine Learning Methods on Imbalanced Data (Rainfall Classification) Subject The topic of the resource Rainfall, Machine Learning, Hybrid Methods, Classification, SMOTE Description An account of the resource This study proposes several methods to analyze the performance of the hybrid machine learning method using Voting and Stacking on rainfall classification. The two hybrid methods will combine five classification methods, namely Logistic Regression, Support Vector Machine, Random Forest, Artificial Neural Network, and eXtreme Gradient Boosting. The data used is Bandung City rainfall data for the years 2005 until 2021. The hybrid method is classified as an ensemble, which means combining several individual classification models to improve the performance of the built model. Voting algorithm has weaknesses in imbalanced data, while stacking does not. The results show that by combining five machine learning methods on an imbalanced dataset, the Stacking algorithm obtains an accuracy value of 99.60%. Meanwhile, with the addition of the SMOTE technique, the accuracy increases to 99.71%. This is supported by the performance of the Stacking method which is superior because it takes the best classification value for each individual model and can overcome the imbalance. Model evaluation does not only focus on accuracy, but also precision, recall, and f1-score. The contribution of this research is to provide information about the best Hybrid method between Voting and Stacking in obtaining model performance results on rainfall classification. Creator An entity primarily responsible for making the resource Aditya Gumilar1 , Sri Suryani Prasetiyowati2 , Yuliant Sibaroni3 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 15-07-2022 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/a1b73b25d7f52112164ce17c57a95b33.pdf 726f4e7546437ec8c0ceb3f6408444bc 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 3 (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 Identification of Malaria Parasite Patterns With Gray Level Co-Occurance Matrix Algorithm (GLCM) Subject The topic of the resource : Parasites, Malaria, GCLM, Extraction, Patterns, Identification Description An account of the resource The results of the test using 5 data of malaria parasite test imagery found that image 1 has an average accuracy value of the energy of 0.55627, homogeneity average of 0.8371, PSNR of 6.1336db, and MSE of 0.24358. Image 2 has an average energy accuracy value of 0.22274, an average Homonegity of 0.98532, a PSNR of 6.1336db, and an MSE of 0.24358. Image 3 has an energy average accuracy value of 0.28735, a Homonegity average accuracy value of 0.9793, a PSNR of 6.133db, and an MSE of 0.24358. Image 4 has an energy average accuracy value of 0.32907 and an average homogeneity accuracy value of 0.97073, PSNR 6.133db, and MSE 0.24358. Image 5 has an average accuracy value of 0.74102, Homonegity average of 0.99844, PSNR of 6.133db, and MSE of 0.4358. Image 6 has an accuracy value of 0.34758 energy, an average accuracy value of homogeneity of 0.99129, a PNSR of 6.133db, and an MSE of 0.24358. Obtained the rule if the average value of energy > = 0.50 then the pattern of malaria parasites is very clear, namely Image 1 and image 5 with a pattern of malaria parasites is very clear Creator An entity primarily responsible for making the resource Annas Prasetio1 , Rika Rosnelly2 , Wanayumini3 Publisher An entity responsible for making the resource available University Of Potensi Utama Medan Date A point or period of time associated with an event in the lifecycle of the resource 30-06-2022 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/24d21d07ae1476306dda106c46ac5e77.pdf b3642e1a2656941bc2aa890a5f33b1a0 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 3 (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 Interdependency and Priority of Critical Infrastructure Information (Case Study: Indonesia Payment System) Subject The topic of the resource Critical infrastructure information, FDANP framework, interdependency, multi-expert perspective, payment system Description An account of the resource The sturdy and reliable payment system is one of the most important systems in the digitalization era, especially in the pandemic COVID-19 period. As part of Critical Infrastructure Information (CII), a strategy to protect the payment system is needed to reduce risks that may arise. But the author's best knowledge, in Indonesia, no reference describes the interdependency of the CII sector that could be used as input on strategy making for reducing the risk on all CII sectors which are influencing the payment system. This research usesthe Fuzzy-based DANP (FDANP) Framework based on a multi-expert perspective on inter�sector influences to identify the interdependency and priority of the CII sector with a case study on the payment system. The contribution of this research is to provide information about the interdependency and priority of the CII sector. The findings of this research show that 5 sectors that have an influence on other sectors with a case study of the payment system and the information and communication technology sector, the energy and mineral resources sector, and the financial sector are the three major sectors that must be paid more attention to because they have an impact on many sectors. Creator An entity primarily responsible for making the resource Arini Muhafidzah1 , Kalamullah Ramli 2 Publisher An entity responsible for making the resource available Universitas Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 30-06-2022 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/df9002a8dad7ace06fd6c2428c794d3b.pdf 8efb33cea125cfc200ae9ca4e5061e45 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 3 (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 Prediction of Retweets Based on User, Content, and Time Features Using EUSBoost Subject The topic of the resource : information diffusion, retweet prediction, EUSBoost Description An account of the resource Twitter is one of the popular microblogs that allow users to write posts. Retweeting is one of the mechanisms for the diffusion of information on Twitter. One way to understand the spread of information is to learn about retweet predictions. This study focuses on predicting retweets using Evolutionary Undersampling Boosting (EUSBoost) based on user, content, and time�based features. We also consider the vector of text as a predictive feature. Models with EUSBoost are able to outperform models using the AdaBoost method. The evaluation results show that the best model can achieve an AUC performance score of 77.21% and a GM score of 77.18%. While the Adaboost-based models achieved AUC scores ranging from 68% to 69% and GM scores ranging from 62% to 63%. In addition, we found that there was no significant difference between using numeric features only and combining numeric and text features Creator An entity primarily responsible for making the resource Ghina Khoerunnisa1 , Jondri2 , Widi Astuti3 Publisher An entity responsible for making the resource available School of Computing, Telkom University Date A point or period of time associated with an event in the lifecycle of the resource 30-06-2022 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/d592f1e191c7929954f22b568337cbe8.pdf f69ba439ff8e403242d64e846bd2b032 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 3 (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 Implementation of CNN-MLP and CNN-LSTM for MitM Attack Detection System Subject The topic of the resource MitM, Kitsune Network Attack Dataset, CNN-MLP, CNN-LSTM Description An account of the resource Man in the Middle (MitM) is one of the attack techniques conducted for eavesdropping on data transitions or conversations between users in some systems secretly. It has a sizeable impact because it could make the attackers will do another attack, such as website or system deface or phishing. Deep Learning could be able to predict various data well. Hence, in this study, we would like to present the approach to detect MitM attacks and process its data, by implementing hybrid deep learning methods. We used 2 (two) combinations of the Deep Learning methods, which are CNN-MLP and CNN-LSTM. We also used various Feature Scaling methods before building the model and will determine the better hybrid deep learning methods for detecting MitM attack, as well as the feature selection methods that could generate the highest accuracy. Kitsune Network Attack Dataset (ARP MitM Ettercap) is the dataset used in this study. The results prove that CNN-MLP has better results than CNN-LSTM on average, which has the accuracy rate respectively at 99.74%, 99.67%, and 99.57%, and using Standard Scaler has the highest accuracy (99.74%) among other scenarios Creator An entity primarily responsible for making the resource Hartina Hiromi Satyanegara1 , Kalamullah Ramli2 Publisher An entity responsible for making the resource available Universitas Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 30-06-2022 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/551b1d079b017134cab5c5a5e9172d08.pdf 33ceefefe36d40a528450cf57b500dba 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 3 (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 Detection of Covid-19 on X-Ray Image of Human Chest Using CNN and Transfer Learning Subject The topic of the resource COVID-19, Image Classification, Convolutional Neural Network, Transfer Learning Description An account of the resource At the end of 2019, a new disease called Coronavirus Disease (COVID-19) originated in Wuhan, China. This disease is caused by respiratory tract infections, ranging from the common cold to serious diseases such as Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). In Indonesia, there are tests to detect COVID-19, such as PCR and Rapid Test. This detector takes a long time and is less accurate in producing a diagnosis. This study aims to classify chest X�ray images using CNN and Transfer Learning methods to diagnose COVID-19. The proposed model has 4 scenarios: CNN Handcraft Model, Transfer Learning (VGG 16, VGG 19, and ResNet 50). This model is accompanied by data augmentation and data balancing techniques using undersampling techniques. The dataset used in this study is the “Covid-19 (COVID-19 and Normal) Radiographic Database” with 13,808 data divided into two classes, namely COVID-19 and Normal. Each model built will produce values for accuracy, precision, recall, and confusion matrix. The results of CNN Scenario 1 accuracy is 95%, in Scenario 2 VGG 16 the accuracy is 93%, Scenario 3 VGG 19 is 90% and Scenario 4 ResNet 50 is 80%. Creator An entity primarily responsible for making the resource Jalu Nusantoro¹, Faldo Fajri Afrinanto², Wana Salma Labibah³, Zamah Sari4 , Yufis Azhar5 Publisher An entity responsible for making the resource available University of Muhammadiyah Malang Date A point or period of time associated with an event in the lifecycle of the resource 30-06-2022 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/59b8e54d26a9517b4a3215b5150021e2.pdf 9de524830eb49708ba385f3390a27e46 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 3 (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 Fuzzy Learning Vector Quantization for Classification of Mixed Meat Image Based on Character of Color and Texture Subject The topic of the resource Pork, Beef, FLVQ, GLCM, HSV, Image Processing Description An account of the resource Beef consumption is quite high and expensive in the world. In Indonesia, beef prices are relatively expensive because the meat supply chain from farmers to the market is quite long. The high demand for beef and the difficulty of obtaining meat are factors in the high price of meat. This makes some meat traders cheat by mixing beef and pork (oplosan). Mixing beef and pork is detrimental to beef consumers, especially those who are Muslim. In this paper, we proposed a new strategy for identifying beef, pig, and mixed meat utilizing Fuzzy learning vector quantization (FLVQ) Based on the color and texture aspects of the meat. The HSV (Hue saturation value) approach is used for color features, whereas the GLCM (Gray level co-occurrence matrix) method is used for texture features. This study makes use of primary data collected from the Pasar Bawah Tourism and Cipuan Market in Pekanbaru, Riau Province. The data set consists of 600 photos, 200 each of beef, pork, and mixed. Based on the test scenario, the coefficient of fuzzyness and learning rate affect the accuracy of meat image identification. The proposed strategy has succeeded in classifying pork, beef and mixed meat with the best percentage of accuracy results in theclasses of beef and pork, beef and mixed, pork and mixed meat, respectively, at 100%, 97.5%, and 95%. This demonstrates that the proposed strategy has succeeded in classifying the image of pork, beef, and mixed Creator An entity primarily responsible for making the resource Lidya Ningsih1 , Agus Buono2 , Mushthofa3 , Toto Haryanto4 Publisher An entity responsible for making the resource available Institut Pertanian Bogor Date A point or period of time associated with an event in the lifecycle of the resource 30-06-2022 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/befe2782f7db9610886178a9b2a7bea4.pdf 8b733a3600ec08bd4c5d43e4d70a2da1 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 3 (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 k-Nearest Neighbor and Feature Extraction on Detection of Pest and Diseases of Cocoa Subject The topic of the resource Cocoa pods, Detection of pests, and diseases of cocoa pods, HSV, KNN, Confusion Matrix, K-Fold Cross�Validation Description An account of the resource Knowledge and utilization of digital images are growing rapidly not only in the fields of medicine and industry but also in the field of agriculture. This knowledge can apply it to a computer-based program that is used to detect agricultural products more effectively and efficiently. this research aims to build a system to detect the types of pests and diseases of cocoa pods because in general, an inspection of pests and diseases of cocoa pods is still manual based on the visual analysis of the color of the pods visually by the human eye which has limitations, which requires more energy to sort, the level of human consistency. In terms of assessing the symptoms of pests and fruit diseases, it is not guaranteed, because humans can experience fatigue, and humans also assess symptoms of pests and fruit diseases, sometimes it is subjective. This study utilizes digital image processing techniques to extract the color features of digital images of cocoa pods, the method used to extract the color features of Hue, Saturation, Value (HSV), and the classification algorithm used by K-Nearest Neighbor. The data used as many as 150 images divided into 70% training data and 30% testing data. Based on the results of trials using k values of 5,7,11 and 13 in the holdout method, the best accuracy is 84.44% with a value of k = 5. And in the k-5 cross-validation test, the best accuracy is also found at k = 5 with a value accuracy of 99.33% Creator An entity primarily responsible for making the resource Mohammad Yazdi Pusadan1 , Syahrullah2 , Merry3 , Ahmad Imam Abdullah4 Publisher An entity responsible for making the resource available University of Tadulako Date A point or period of time associated with an event in the lifecycle of the resource 30-06-2022 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/890395a667fc86c8491f240f4d25c15e.pdf e53d4aea884418a4f93b140b35bfc509 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 3 (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 Segmentation of Small Objects in Satellite Imagery Using Dense U-Net in Massachusetts Buildings Dataset Subject The topic of the resource class imbalance, dataset, end-to-end, convolution, denseU-net Description An account of the resource Class imbalance is a serious problem that disrupts the process of semantic segmentation of satellite imagery in urban areas in Earth remote sensing. Due to the large objects dominating the segmentation process, small object are consequently limited, so solutions based on optimizing overall accuracy are often unsatisfactory. Due to the class imbalance of semantic segmentation in Earth remote sensing images in urban areas, we developed the concept of Down-Sampling Block (DownBlock) to obtain contextual information and Up-Sampling Block (UpBlock) to restore the original resolution. We proposed an end-to-end deep convolutional neural network (DenseU-Net) architecture for pixel-wise urban remote sensing image segmentation. this method to segmentation the small object in satellite imagery.The accuracy of the small object class in this study was further improved using our proposed method. This study used data from the Massachusetts Buildings dataset using Dense U-Net method and obtained an overall accuracy of 84.34% Creator An entity primarily responsible for making the resource Muhammad Iqbal Izzul Haq1 , Aniati Murni Arymurthy2 , Irham Muhammad Fadhil3 Publisher An entity responsible for making the resource available Universitas Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 30-06-2022 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