1 10 17 https://repository.horizon.ac.id/files/original/2a916f62bd9f489d25d84d27ce448e08.pdf f275897137f11f0136c3b1821195e78e 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 4 (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 Mask Detection Using Convolutional Neural Network Algorithm Subject The topic of the resource Mask detection, Convolutional Neural Network, MobileNetV2, CNN optimizations Description An account of the resource The World Health Organizations and the Ministry of Health of the Republic of Indonesia have required the use of masks to suppress the spread of COVID-19. WHO provides guidance on how to use a good mask to cover the mouth and nose. This study aims to detect the correct use of masks using the Convolutional Neural Network. CNN is a popular Deep Learning algorithm for image data classification problems. The Mask Usage Detector is built with the help of a pre-trained MobileNetV2 model with an architecture that supports media that has minimum computations. This study will also compare the performance of three optimization methods from CNN, namely Adam, SGD, and RMSprop in detecting the use of masks. Performance will be seen from the test results by analyzing the values of accuracy, precision, and recall. The dataset used is in the form of image data of 2,029 images for 2 categories, namely "masked" and "unmasked". A total of 1,623 images were used as training data and 406 images for test data. Based on the testing process, the accuracy of each optimization is 93.84% with Adam optimization, 84.48% with SGD optimization, and 93.10% with RMSprop optimization. With the proposed model, this study obtains the performance results of the three CNN optimizations, and it is concluded that adam's optimization gives better performance results than the other two optimizations. Keywords: Mask detection, Convolutional Neural Net Creator An entity primarily responsible for making the resource Rizky Amalia1 , Febriyanti Panjaitan2 Publisher An entity responsible for making the resource available Universitas Bina Darma Date A point or period of time associated with an event in the lifecycle of the resource 22-04-2022 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/cf1b4ebd530d0e1d4507840e5ce689f3.pdf f31bec1ea1aefc851a9d03c77b0231a3 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 4 (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 Depression Detection on Twitter Social Media Using Decision Tree Subject The topic of the resource depression, tweet, depression anxiety and stress scale 42, classification and regression tree Description An account of the resource Depression is a major mood illness that causes patients to experience significant symptoms that interfere with their daily activities. As technology has developed, people now frequently express themselves through social media, especially Twitter. Twitter is a social media platform that allows users to post tweets and communicate with each other. Therefore, detecting depression based on social media can help in early treatment for sufferers before further treatment. This study created a system to detect if a person is indicating depression or not based on Depression Anxiety and Stress Scale - 42 (DASS-42) and their tweets using the Classification and Regression Tree (CART) method with TF-IDF feature extraction. The results show that the most optimal model achieved an accuracy score of 81.25% and an f1 score of 85.71%, which are higher than baseline results with an accuracy score of 62.50% and an f1 score of 66.66%. In addition, we found that there were significant effects on changing the value of the maximum features in TF-IDF and changing the maximum depth of the tree to the model performance Creator An entity primarily responsible for making the resource Marcello Rasel Hidayatullah1 , Warih Maharani2 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 31-08-2022 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/b85c63f885151575d29ed4623bec4755.pdf 91977a3b8b95c78d8615c480095bd751 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 4 (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 QSAR Study of Larvicidal Phytocompounds as Anti-Aedes Aegypti by using GA-SVM Method Subject The topic of the resource Aedes aegypti, Genetic Algorithm, QSAR, Support Vector Machine https://repository.horizon.ac.id/files/original/630a33ee2aeff14725a2ac8a8d209a6e.pdf e08feb895826719ef8900a164c00002c 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 4 (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 BERT, IndoBERT, and CNN-LSTM in Classifying Public Opinion about COVID-19 Vaccine in Indonesia Subject The topic of the resource Vaccine COVID-19, IndoBERT, IndoBERTweet, CNN-LSTM, Indonesia, Sentiment Description An account of the resource COVID-19 was classified as a pandemic in March 2020, and then in July 2021, this virus had its variance that spreads all over the world including Indonesia. The probability of the detrimental of its effect cannot be avoided, because this virus has a huge transmission risk during daily activity. To prevent suffering from COVID-19, people certainly need to be vaccinated. In responding to its vaccine, the citizen of Indonesia become expressive, so they try to express opinions, for example by uploading text on Twitter. Those expressions can be learned using deep learning frameworks which are BERT, CNN-LSTM, and IndoBERTweet to get knowledge about negative speech categories such as anxiety, panic, and emotion, or positive speech such as vaccines whether worked well. By then, these three methods accomplish in carrying out the prediction of sentiments about vaccination using dataset tweets on Twitter from January-2021 to March-2022, for instance using IndoBERT succeeds to classify sentiments as positive sentiment at around 80%, and then IndoBERTweet at 68%, in addition using CNN-LSTM reach 53% with the total of using 2020 dataset from Twitter. According to these results, a lesson learned for continued improvement for Indonesia's Government or authorities can be acquired in ending the COVID-19 pandemic Creator An entity primarily responsible for making the resource Siti Saadah1 , Kaenova Mahendra Auditama2 , Ananda Affan Fattahila3 , Fendi Irfan Amorokhman4 , Annisa Aditsania5 , Aniq Atiqi Rohmawati6 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 30-08-2022 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/645811bcc319c2a51fa5bc77adf77e22.pdf 65b14006ee769a5dd9e42c1956fb5dcf 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 4 (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 Aspect Based Sentiment Analysis with FastText Feature Expansion and Support Vector Machine Method on Twitter Subject The topic of the resource : twitter, aspect-based sentiment analysis, feature expansion, fasttext, svm Description An account of the resource Social media such as Twitter has now become very close to society. Twitter users can express current issues, their opinions, product reviews, and many other things both positive and negative. Twitter is also used by companies to monitor the assessment of their products among the public as insight that will be used to evaluate what aspects of their products need to be further developed. Twitter with its limitation of only allowing users to post a maximum tweet of 280 characters will make a lot of abbreviated and difficult to understand words used, so it will allow vocabulary mismatch problems to occur. Therefore, in this paper, research conducted on aspect-based sentiment analysis of Telkomsel’s products from the aspects of signal and service by applying feature expansion using Fasttext word embedding to overcome vocabulary mismatch problem and classification with the Support Vector Machine (SVM) method. Sampling technique with Synthetic Minority Oversampling Technique (SMOTE) used to overcome data imbalance. The experimental results show that feature expansion can increase the performance of model. The final results obtained F1-Score value of the model for the signal aspect increased by 27.91% with F1-Score 95.93%, and for the service aspect increased by 42.36% with F1-Score 94.53%. Creator An entity primarily responsible for making the resource Muhammad Afif Raihan1 , Erwin Budi Setiawan2 Publisher An entity responsible for making the resource available Telkom University Date A point or period of time associated with an event in the lifecycle of the resource : 22-08-2022 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/2d4f28754aff68eb734ad8a43020d0e3.pdf 11f6570750142c9fb9a653256bb37f59 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 4 (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 Application of Naïve Bayes Algorithm Variations On Indonesian General Analysis Dataset for Sentiment Analysis Subject The topic of the resource sentiment analysis, indonesian dataset, bernoulli nave bayes, gaussian nave bayes, multinomial nave bayes, complement nave bayes Description An account of the resource Indonesian General Analysis Dataset is a dataset sourced from social media twitter by using keywords in the form of conjunctions to get a dataset that does not only focus on a particular topic. The use of Indonesian language datasets with general topics can be used to test the accuracy of the classification model so as to provide additional reference in choosing the right methods and parameters for sentiment analysis. One of the algorithms which in several studies produces the highest level of accuracy is naive Bayes which has several variations. This study aims to obtain the method with the best accuracy from the naive Bayes variation by setting the minimum and maximum document frequency parameters on the Indonesian General Analysis Dataset for sentiment analysis. The naive Bayes classifier variations used include Bernoulli naive Bayes, gaussian naive Bayes, complement naive Bayes and multinomial naive Bayes. The research stage begins with downloading the dataset. Preprocessing becomes the next stage which consists of tokenizing, stemming, converting abbreviations and eliminating conjunctions. In the preprocessed data, feature extraction is carried out by converting the dataset into vectors and applying the TF-IDF method before entering the sentiment analysis classification stage. Tests in this study were carried out by applying the minimum document frequency (min-df) and maximum document frequency (max-df) for each variation of naive Bayes to obtain the appropriate parameters. The test uses k-fold cross validation of the dataset to divide the training data and sentiment analysis test data. The next confusion matrix is made to evaluate the level of accuracy Creator An entity primarily responsible for making the resource Najirah Umar1 , M. Adnan Nur2 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 22-08-2022 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/7e52beaf3872bf52a6d8b950003b8991.pdf e9698cc716b49a7ee1e4d5e00c8a13b8 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 4 (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 Imputation Performance on Missing Value Data Graduate User Satisfaction Subject The topic of the resource Graduate user, Imputation, KNN, Missing Value, Satisfaction Description An account of the resource A missing value is a common problem of most data processing in scientific research, which results in a lack of accuracy of research results. Several methods have been applied as a missing value solution, such as deleting all data that have a missing value, or replacing missing values with statistical estimates using one calculated value such as, mean, median, min, max, and most frequent methods. Maximum likelihood and expectancy maximization, and machine learning methods such as K Nearest Neighbor (KNN). This research uses KNN Imputation to predict the missing value. The data used is data from a questionnaire survey of graduate user satisfaction levels with seven assessment criteria, namely ethics, expertise in the field of science (main competence), foreign language skills, foreign language skills, use of information technology, communication skills, cooperation, and self-development. The results of testing imputation predictions using KNNI on user satisfaction level data for STMIK PPKIA Tarakanita Rahmawati graduates from 2018 to 2021. Where using the five k closest neighbors, namely 1, 5, 10, 15, and 20, the error value of the k nearest neighbors is 5 in RMSE is 0, 316 while the error value using MAPE is 3,33 %, both values are smaller than the value of k other nearest neighbors. K nearest neighbor 5 is the best imputation prediction result, both calculated by RMSE and MAPE, even in MAPE the error value is below 10%, which means it is very good Creator An entity primarily responsible for making the resource Abdul Fadlil1 , Herman2 , Dikky Praseptian M3 Publisher An entity responsible for making the resource available Universitas Ahmad Dahlan Date A point or period of time associated with an event in the lifecycle of the resource 22-08-2022 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/fa92f4dd35d082b1fa27d1628bdd6e13.pdf 7a71c47acc04f0cca9327d499d938c90 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 4 (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 Strategy to Improve Employee Security Awareness at Information Technology Directorate Bank XYZ Subject The topic of the resource information security awareness, information security awareness strategies, Human Aspect of Information Security Questionnaire (HAIS-Q), information security measurement, Knowledge Attitude Behaviour (KAB) Description An account of the resource Bank handles private information like customer financial transactions and personal data. There was a 63% increase in cyberattacks attempted against Bank XYZ in 2021, and 1,323 attempted attacks on corporate email Bank XYZ. Therefore, implementing security awareness training for all employees is crucial for Bank XYZ. The information security awareness program must be assessed to determine the program's efficiency and the level of information security awareness among employees. Therefore, this study assesses the information security awareness at Bank XYZ, especially the Information Technology (IT) Directorate using the Human Aspect of Information Security Questionnaire (HAIS-Q) method. The findings of this study revealed that employees at Bank XYZ in the information security work unit had a "Good" level of awareness. In contrast, the results from other IT work units were “Medium”. Based on the assessment results, Bank XYZ's security awareness strategy recommendation is to align awareness content with information security policies and procedures, use a variety of media awareness, and focus on the "Internet Use" and "Information Handling" awareness areas. As a way of determining the achievement of information security Key Performance Indicators (KPI), security awareness measurement must be done regularly, for example, once a year Creator An entity primarily responsible for making the resource 1Halida Ernita, 2Yova Ruldeviyani, 3Desiana Nurul Maftuhah, 4Rahmad Mulyadi Publisher An entity responsible for making the resource available Universitas Indonesia Contributor An entity responsible for making contributions to the resource 22-08-2022 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/206e44f2a10ccf399d65a0e5274f30aa.pdf d65abe3ea3ed0c0b6ff09bd13e51245d 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 4 (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 Time Series Temperature Forecasting by using ConvLSTM Approach, Case Study in Jakarta Subject The topic of the resource Temperature Forecasting, Machine Learning, MLP, LSTM, ConvLSTM Description An account of the resource Climate change has occurred in several countries, especially in tropical countries such as Indonesia. It causes extreme temperature changes in several Indonesian areas, especially Jakarta, one of the world's most populated cities. The population of Jakarta causes the activities carried out by residents to be disturbed by extreme temperature changes. In addition, drastic temperature changes also affect the energy consumption used by residents. Therefore, it is necessary to predict temperature to determine future temperature conditions so that residents can plan their activities. Temperature forecast can be done in several ways, one of which uses a machine learning approach. This research uses a deep learning model called the Convolutional Long Short-Term Memory (ConvLSTM). Moreover, we also compare the model with Multi-Layer Perceptron (MLP), and Long Short-Term Memory (LSTM). We use temperature data taken from the ERA-5 period years 2018 to 2020 located in Kemayoran, Jakarta, Indonesia. This research aims to investigate the accuracy of short-term temperature forecasting by using these three models. The model is built to predict short-term temperatures for 1, 3, and 7 days ahead. The performance of the three methods is measured by calculating the Root Mean Square Error (RMSE), Mean Square Error (MAE), and Coefficient Correlation (CC). The result shows that the LSTM performs better than the other methods to forecast 1, 3, and 7 days, i.e., with the lowest RMSE, MAE, and higher CC. Creator An entity primarily responsible for making the resource Faishal Raihanur Rasyid1 , Didit Adytia2 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 22-08-2022 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/ce447da53f11d30b7aa5548823362669.pdf 0eb4405480226707ad36effbbdd667e7 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 4 (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 Applying Different Resampling Strategies In Random Forest Algorithm To Predict Lumpy Skin Disease Subject The topic of the resource Lumpy Skin Disease, Machine Learning, Oversampling, Random Forest, Random Undersampling Description An account of the resource The spread of Lumpy Skin Disease (LSD) that infects livestock is increasingly widespread in various parts of the world. Early detection of the disease’s spread is necessary so that the economic losses caused by LSD are not higher. The use of machine learning algorithms to predict the presence of a disease has been carried out, including in the field of animal health. The study aims to predict the presence of LSD in an area by utilizing the LSD dataset obtained from Mendeley Data. The number of lumpy infected cases is so low that it creates imbalanced data, posing a challenge in training machine learning models. Handling the unbalanced data is performed by sampling technique using the Random Under-sampling technique and Synthetic Minority Oversampling Technique (SMOTE). The Random Forest classification model was trained on sample data to predict cases of lumpy infection. The Random Forest classifier performs very well on both under-sampling and oversampling data. Measurement of performance metrics shows that SMOTE has a superior score of 1-2% compared to the use of Random Undersampling. Furthermore, Re-call rate, which is the metric we want to maximize in identifying lumpy cases, is superior when using SMOTE and has slightly better precision than Random Undersampling. This research only focuses on how to balance unbalanced data classes so that the optimization of the model has not been implemented, which creates opportunities for further research in the future Creator An entity primarily responsible for making the resource Suparyati1 , Emma Utami2 , Alva Hendi Muhammad Publisher An entity responsible for making the resource available Universitas Amikom Yogyakarta Date A point or period of time associated with an event in the lifecycle of the resource 22-08-2022 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