1 10 17 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/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/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/10999bcf67f4cc00f433795500c297ab.pdf 2cf47c039e13d399c53959732a4586c6 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 Analysis of Public Sentiment Towards Goverment Efforts to Break the Chain of Covid-19 Transmission in Indonesia Using CNN and Bidirectional LSTM Subject The topic of the resource : COVID-19, Deep Learning, Bidirectional LSTM, CNN Description An account of the resource COVID-19 is a new disease that has a negatively impacts in Indonesia, so the government is taking several measures to suppress the spread of COVID-19, such as new normal, social distancing, health protocols fines, and COVID-19 vaccination. The government's handling efforts have reaped a variety of negative to positive responses from the public on social media, so this study aims to determine the effectiveness of the government's efforts by analyzing public sentiment using the Deep Learning method with 1,875 training datasets consisting of four types government efforts and taken from various media social. The use of Deep Learning begins with testing several Deep Learning architectures to determine the best architecture for predicting data. The architectures tested include CNN and Bi-LSTM, where from these tests, Bi-LSTM outperforms CNN with the best performance achieving the accuracy of 97.34% and 97.33% for precision, recall, and F1-score. The results of public sentiment analysis show that social distancing efforts are considered the most effective by obtaining the most positive sentiments by 33.93%, while the effort to health protocol fines is considered lacking because it obtains the most negative sentiment of 35.64%, so the government must continue to enforce social distancing and optimize other efforts that are still considered ineffective Creator An entity primarily responsible for making the resource Gusti Agung Mayun Kukuh Jaluwana1 , Gusti Made Arya Sasmita2 , I Made Agus Dwi Suarjaya3 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 : 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/065140cc4edd99a1702fc008e41e8ffd.pdf 5732e35fe17bc1f53a769348af44b099 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 Bidirectional Long Short-Term Memory and Word Embedding Feature for Improvement Classification of Cancer Clinical Trial Document Subject The topic of the resource Deep Learning, BiLSTM, Text classification, Word Embedding, Clinical Trials Description An account of the resource In recent years, the application of deep learning methods has become increasingly popular, especially for big data, because big data has a very large data size and needs to be predicted accurately. One of the big data is the document text data of cancer clinical trials. Clinical trials are studies of human participation in helping people's safety and health. The aim of this paper is to classify cancer clinical texts from a public data set. The proposed algorithms are Bidirectional Long Short Term Memory (BiLSTM) and Word Embedding Features (WE). This study has contributed to a new classification model for documenting clinical trials and increasing the classification performance evaluation. In this study, two experiments work are conducted, namely experimental work BiLSTM without WE, and experimental work BiLSTM using WE. The experimental results for BiLSTM without WE were accuracy = 86.2; precision = 85.5; recall = 87.3; and F-1 score = 86.4. meanwhile the experiment results for BiLSTM using WE stated that the evaluation score showed outstanding performance in text classification, especially in clinical trial texts with accuracy = 92,3; precision = 92.2; recall = 92.9; and F-1 score = 92.5 Creator An entity primarily responsible for making the resource Jasmir Jasmir1 , Willy Riyadi2 , Silvia Rianti Agustini3 , Yulia Arvita4 , Despita Meisak5 , Lies Aryani6 Publisher An entity responsible for making the resource available Universitas Dinamika Bangsa Jambi Indonesia 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/3977089e2766f934be2737eb9fde3d9b.pdf 3a18f1efdec291324c11f60d89f21fd1 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 Support Vector Regression Method for Predicting Off-Grid Photovoltaic Output Power in the Short Term Subject The topic of the resource kernel, photovoltaic, prediction, support vector regression Description An account of the resource Photovoltaic (PV) technology is a renewable technology utilizing conversion of solar power or solar radiation into electrical energy. In the manufacture of Solar Power Generation systems, reference is needed regarding the cost of generation and scheduling of maintenance plans. To obtain this reference, it is necessary to predict the photovoltaic power output which is used to determine the power output of PV in the future. In this study, a system that is used to predict short-term power output in PV is designed. This system uses solar irradiation data and 42 days of power output in off-grid PV mini-grid as the dataset. The dataset obtained from the PV output is processed using the Support Vector Regression method with the Kernel Radial Basis Function (RBF) function. Based on the dataset used, this study succeeded in testing the best kernel, namely the RBF kernel. Evaluation of the prediction model obtained a smaller error value than other kernel tests with a Mean Absolute Percentage Error (MAPE) value of 21.082%, Mean Square Error (MSE) value of 0.122, and Mean Absolute Error (MAE) value of 0.262. The prediction model obtained is used to predict the short-term PV power output for the next 3 days. The results of the prediction model have an error value of 5.785 % for MAPE, 0.005 for MAE and 0.069 for MSE. Therefore, the predictive model can be categorized as very good and feasible to predict short-term power output. Creator An entity primarily responsible for making the resource Kharisma Bani Adam1 , Desri Kristina Silalahi2 , Bandiyah Sri Aprillia3 , Husayn Aththar Adhari4 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/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/872c712c0232602c9c15eb45a6bbcdfe.pdf 245a1b967e79df8ad4a4d5d8975d85a3 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 Detecting Diseases on Clove Leaves Using GLCM and Clustering K-Means Subject The topic of the resource K-Means, GLCM, Image Processing, Clove Plants, Diagnosis. Description An account of the resource The detection of disease in clove plant leaves is generally carried out by diagnosing the symptoms that appear on clove plants. This diagnosis is conducted by clove farmers only by relying on their experience or even having to seek information from other clove farmers. This is because the agricultural sector has no disease detection system for clove leaves by utilizing digital image processing technology to detect diseases in clove leaves. In this study, the researchers applied two methods to make it easier for clove farmers to diagnose diseases in their clove plants. Those methods were the imaging system using Gray Level Co�Occurrence Matrix (GLCM) and disease clustering using the K-Means algorithm. The objective of this study was to design and build image pattern recognition by utilizing 4 features of the Gray Level Co-Occurrence Matrix (GLCM): energy, entropy, homogeneity, and contrast. These 4 features were used to obtain the extraction value from an image. The outcomes were then used to cluster the clove plant diseases using the K-Means method. In making the software, the researchers used Javascript, HTML, CSS, PHP, and MySql to create a database. The output in this study was an information system application that provides disease-type clustering using the K-Means algorithm. The results of the Gray Level Co-occurrence Matrix (GLCM) concerning extracting images of clove plant leaves affected by disease indicated that the created system can be used to help clove farmers in diagnosing what diseases are infecting their plants by only uploading photos from affected leaves of the clove plant. Furthermore, the results of the K-Means calculation on the examined data showed several categories of Anthracnose leaf spot diseases. In addition, sample number #40 was included in cluster 2 status, in which the average values for energy, entropy, homogeneity, and contrast were 0.583, 0.175, 0.939, and 0.175, respectively Creator An entity primarily responsible for making the resource Mila Jumarlis1 , Mirfan2 Publisher An entity responsible for making the resource available STAIN Majene 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