1 10 25 https://repository.horizon.ac.id/files/original/bd5ed0a0a4786d51f9c16c697312b6e8.pdf 31b7bcba6896486595efc73da36f2011 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 7 NO 1 (2023) 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 Covid-19 Fake News Detection on Twitter Based on Author Credibility Using Information Gain and KNN Methods Subject The topic of the resource twitter; fake news, COVID-19; credibility; KNN; information gain Description An account of the resource Twitter is one of the social media that is used as a tool to share various kinds of information about various kinds of things that are of concern to social media users. One of the information shared is information about COVID-19, which is known that the COVID-19 pandemic is currently spreading throughout the world at a very alarming rate. COVID-19 is an infectious disease caused by SARS-COV-2. The World Health Organization (WHO) claims that the spread of COVID-19 is supported by the spread of false/fake news. So to find out the truth of the news, a COVID-19 fake news detector is needed so that users don't fall for the hoaxes circulating. This study aims to classify COVID-19 news on Twitter based on author credibility. Credibility in question is a person's perception of the validity of information and is a multidimensional concept that is used as a means of receiving information to assess the source of communication. The method used in this research is Information Gain and KNN. KNN (K-Nearest Neighbor) is a supervised learning algorithm that works by classifying a set of data based on classified training data. Information Gain is used to ranking the most influential attributes, and KNN is used to classify data based on learning data taken from the nearest neighbors. The research consists of 6 main stages, namely data collection (crawling data), data preprocessing, feature extraction, feature selection, data split into training data and testing data, KNN stage, and data evaluation stage. The research carried out succeeded in obtaining an accuracy value of 91%, a correlation value between credibility and hoax of 0.115, and a p-value <0.005. Creator An entity primarily responsible for making the resource Nanda Ihwani Saputri1 , Yuliant Sibaroni2 , Sri Suryani Prasetiyowati3 Publisher An entity responsible for making the resource available Universitas Telkom Date A point or period of time associated with an event in the lifecycle of the resource 06-02-2023 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/a3f8717c9c707b97660342d904895010.pdf 8495a03ed949c92340714280642a06e3 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 7 NO 1 (2023) 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 Comparison of Sentiment Analysis Methods Based on Accuracy Value Case Study: Twitter Mentions of Academic Article Subject The topic of the resource sentiment analysis, decision tree, k-nearest neighbors, naïve bayes, and random forest Description An account of the resource The assessment of academic articles is based on the number of citations, but the number only is not enough. So now there is Altmetric which can measure the impact of academic articles from the number of citations and using social media, usually Twitter. Still, the number of mentions on Twitter is not enough because the expressions of the sentences vary. Mentions must be classified according to neutral, positive, and negative criteria. Sentiment analysis is performed on tweets to measure social media volume and attention related to research findings from academic articles. There are many sentiment analysis methods, so this study aims to compare sentiment analysis methods using Decision Tree, K-NN, Naïve Bayes, and Random Forest to get the most suitable methods. The evaluation method in this study uses the Confusion Matrix by searching for Accuracy, Precision, and Recall values. The results show that the most suitable sentiment analysis method is Naïve Bayes by obtaining the highest classification suitability value of the other methods, which has an actual positive sentiment va Creator An entity primarily responsible for making the resource Muhamad Fahmi Fakhrezi1 , Adian Fatchur Rochim2 , Dinar Mutiara Kusomo Nugraheni3 Publisher An entity responsible for making the resource available Diponegoro University Date A point or period of time associated with an event in the lifecycle of the resource 05-02-2023 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/f6693e1d8be87bb864605eb8d1b83e5b.pdf 3436e69dbfab659e88ffde508f1601f4 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 7 NO 1 (2023) 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 A Comparative Study of CatBoost and Double Random Forest for Multi-class Classification Subject The topic of the resource catboost, double random forest, multi-class classification, lime Description An account of the resource Multi-class classification has its challenge compared to binary classification. The challenges mainly caused by the interactions between explanatory and responses variable are increasingly complex. Ensemble-based methods such as boosting and random forest (RF) have been proven to handle classification problems. We conducted this research to study multi-class classification using CatBoost, a method developed with gradient boosting and double random forest (DRF), RF’s development that is good to be used when the resulting RF model is underfitting. Analysis was carried out using simulation and empirical data. In the simulation study, we generate data based on the distance between classes: high, medium, and low. The empirical data used is the industrial classification code, namely KBLI. CatBoost and DRF can rightly solve the multi-class classification problem at a high distance, measured by a 100% balanced accuracy score. At a medium distance, CatBoost and DRF produce balanced accuracy scores of 99.25% and 97.54%, respectively, whereas 32.37% and 23.97% at the low distance. In empirical studies, CatBoost’s performance outperforms DRF by 4.27%. All the differences are statistically significant based on the t-test result. We also use LIME to explain individual predictions of CatBoost and learn words that contribute the most to an example class’s prediction. Creator An entity primarily responsible for making the resource Annisarahmi Nur Aini Aldania1 , Agus Mohamad Soleh2* , Khairil Anwar Notodiputro3 Publisher An entity responsible for making the resource available IPB University Date A point or period of time associated with an event in the lifecycle of the resource : 03-02-2023 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/af150e5112e99db92b84214e2af94b0d.pdf d3e7d795664ce13ce3cc5616b61ad663 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 7 NO 1 (2023) 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 Software as a Service-based Integrated Interactive Online Course System Subject The topic of the resource cloud computing; learning management systems; live streams; e-learning; configurable system Description An account of the resource Several online learning platforms use recorded videos as a medium for delivering their material. In addition, several applications, such as Zoom, Google Meet, and WhatsApp, can help communicate interactively during this learning process. However, because the application is still separate from the existing online learning platform, users must switch applications and make the necessary data settings. Another obstacle experienced is that not all online learning service providers can have the infrastructure to use online learning systems, especially if the providers are individuals. This research uses a simple sequential method and aims to build an integrated online course system equipped with an interactive learning management system that many online courses, in general, can use. The main features are submissions of new courses by teachers, ordering courses by students, classes can be held using live video streams, the interaction between teachers and students in real-time with live chat, and a learning management system that includes sending and receiving assignments and quizzes. The test results show that as many as 73.2% of respondents gave the highest score for the built system. Creator An entity primarily responsible for making the resource Daniel Soesanto1 , Marcellinus Ferdinand Suciadi2 , Liliana3 Publisher An entity responsible for making the resource available cloud computing; learning management systems; live streams; e-learning; configurable system. Date A point or period of time associated with an event in the lifecycle of the resource 02-02-2023 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/7f1ab9420c0cd36ae98be88f1bffd175.pdf 223209f2e14a5887b9d4a8ef224bcdd7 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 7 NO 1 (2023) 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 Image Classification of Vegetable Quality using Support Vector Machine based on Convolutional Neural Network Subject The topic of the resource vegetable quality; image classification; convolutional neural network; support vector machine; feature extration Description An account of the resource As part of an effort to develop intelligent agriculture, new methods for enhancing the quality of vegetables are being continually developed. In recent years, the Convolutional Neural Network (CNN) has shown to be the most successful and extensively used approach for identifying the quality of pre-trained vegetables. However, this method is time-consuming due to the scarcity of truly large, significant datasets. Using a pre-trained CNN model as a feature extractor is a straightforward method for utilizing CNNs' capabilities without investing time in training. While, Support Vector Machine (SVM excels at processing data with tiny dimensions and significantly larger instances. SVM more accurately classifies the flatten/vector feature supplied by the CNN fully connected layer with small dimensions. In addition, implementing Data Augmentation (DA) and Weighted Class (WC) for data variety and class imbalance reduction can improve CNN-SVM performance. The research results show highest accuracy during training always achieves 100% across all experimental options. With an average accuracy of 69.66% in the testing process and 92.51% in the prediction process for all data, the experimental findings demonstrate that CNN-SVM outperforms CNN in terms of accuracy performance in all possible experiments, with or without WC and or DA approach. Creator An entity primarily responsible for making the resource Hanny Nurrani1 , Andi Kurniawan Nugroho2* , Sri Heranurweni3 Publisher An entity responsible for making the resource available Universitas Semarang, Semarang, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 05-02-2023 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/b128cb175d8c506b8063289a90b64166.pdf 717d2233a1bf70831944ca757143b8aa 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 7 NO 1 (2023) 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 Classification of Fruits Based on Shape and Color using Combined Nearest Mean Classifiers Subject The topic of the resource fruit classification, nearest mean classifier, color features, shape features Description An account of the resource Fruit classification is an important task in many agriculture industry. The fruit classification system can be used to identify the types and prices of fruit. Manual classification of fruit is not efficient for large amount of fruits. The advancement of information technology has made possible fruit classification be done by a machine. This research aims to propose a fruit classification methodology based on shape and color. To reduce the effect of lighting variability a color normalization is carried out prior to feature extraction. The color features used in this research are mean and standard deviation. The shape features are area, perimeter, and compactness. The classification of an unknown fruit is carried out using the nearest mean classifier. The method developed in this research is tested using 12 classes of fruits where each class is represented by a number of samples. The experimental results show that the method proposed in this research provides an accuracy of 95.83% for two samples per class and 100% for three samples per class. Experiment on small training samples has been conducted to evaluate the performance of the proposed combined nearest mean classifiers and results obtained showed that the technique was able to provide good accuracy Creator An entity primarily responsible for making the resource Abdullah1 , Agus Harjoko2 , Othman Mahmod3 Publisher An entity responsible for making the resource available Universitas Islam Indragiri Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 02-02-2023 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/c57fd5a4898ba33273d707705508c5c1.pdf 19716160668011ce4aaf1c4d8811a963 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 7 NO 1 (2023) 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 Implementing Agile Scrum Methodology in The Development of SICITRA Mobile Application Subject The topic of the resource scrum, agile, mobile application Description An account of the resource Software development management system is important in application development. A proper software development management system will create a team that can adapt to system requirements and changes during application development. Various software development management systems are developed and widely implemented in software development, one of which is Agile Scrum. This study aims to implement well-documented Scrum for end-to-end application development, including the development of servers and mobile applications that we develop. We developed a bus application called SICITRA, with the main feature of being able to help passengers share their travel information with those closest to them. Scrum is used because it has agility which can make application development faster and more organized, and there is a close relationship between everyone involved in the project. The results of this study are that by using well-documented Scrum, we can make it easier to track progress, become a guide during system development, become history and evaluate Scrum implementation during development. Creator An entity primarily responsible for making the resource Oktavia Citra Resmi Rachmawati1 , Deyana Kusuma Wardani2 , Wifda Muna Fatihia3 , Arna Fariza4 , Hestiasari Rante5 Publisher An entity responsible for making the resource available Polytechnic Institute of Surabaya Date A point or period of time associated with an event in the lifecycle of the resource 02-02-2023 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/7d274ba1a4ea7a248fe3703446715a20.pdf f6826dc7860de5c6e4fd348af3031d48 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 7 NO 1 (2023) 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 Sunflower Image Classification Using Multiclass Support Vector Machine Based on Histogram Characteristics Subject The topic of the resource histogram characteristics; first order feature extraction; support vector machine; multiclass SVM Description An account of the resource Sunflower is an important commodity in agriculture, besides being used as an ornamental plant, sunflower is an oil-producing plant and a source of industrial materials. In Indonesia, sunflower productivity is considered less than optimal, because knowledge and information about sunflowers are still lacking. Therefore, information is needed that can be used as an extension of knowledge about sunflowers itself, especially in Indonesia, which is a tropical region which is an area suitable for the growth of sunflowers. Sunflowers can actually be identified based on recognizable traits. However, the similar shape makes it difficult for some people to distinguish the types of sunflowers. This study aims to classify sunflower images using a first-order feature extraction algorithm using the characteristics of mean, skewness, variance, kurtosis, and entropy which are then used as input to the Multiclass SVM identification algorithm. Data points are mapped to dimensionless space using a Multiclass SVM to produce hyperplane-linear separation between each class. Based on the results of testing the accuracy of the model is able to perform classification with an average accuracy of 79%. These results show that the developed model can classify well Creator An entity primarily responsible for making the resource Rini Nuraini1 , Rachmat Destriana2 , Desi Nurnaningsih3 , Yeni Daniarti4 , Allan Desi Alexander5 Publisher An entity responsible for making the resource available Universitas Nasional Date A point or period of time associated with an event in the lifecycle of the resource 03-02-2023 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/ca9498818a7fab8a4e971f4c773cdd29.pdf 2a7ffae128eab12ee6c4ae9b95c7a1a1 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 7 NO 1 (2023) 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 Capturing Students’ Dynamic Learning Pattern Based on Activity Logs Using Hierarchical Clustering Subject The topic of the resource learning pattern, activity logs, learning management systems, hierarchical clustering. Description An account of the resource Students can have various characteristics and learning patterns. By understanding the characteristics and learning pattern of individual students, teachers can provide individualized learning strategies based on students' needs. Students' learning patterns may experience changes depending on their conditions during the learning process. If the learning pattern analysis is only run once, then the progress and changes in student learning patterns throughout the learning process cannot be recognized. On the other hand, periodical analysis is expected to describe the dynamics of student learning patterns from time to time. This research is intended for capturing students' dynamic learning pattern using Hierarchical Clustering. We clustered the learning patterns based on Learning Management Systems (LMS) activity logs. The activity log data were partitioned into several periodical datasets. The results of the periodic clustering indicated that students’ learning patterns varied from one another and changed from time to time. Most students experienced change in learning patterns throughout the semester. The analysis also indicated that learning pattern also has the potential to be improved and maintained. Creator An entity primarily responsible for making the resource Kusuma Ayu Laksitowening1 , Made Diva Prasetya2 , Dawam Dwi Jatmiko Suwawi3 , Anisa Herdiani4 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 02-02-2023 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/6ed9d58402556fad6e6b3fbc26d07078.pdf a2bc80e64fdf422a778eb74ab8a8713c 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 7 NO 1 (2023) 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 Against Political Figure’s Billboard During Pandemic Using Naïve Bayes Algorithm Subject The topic of the resource covid-19 pandemic; twitter; sentiment analysis; naïve bayes clalssification algorithm Description An account of the resource In the midst of the Covid-19 Pandemic, many Indonesians have reacted negatively to the placement of political individuals' billboards with very huge sizes on the streets. The early political campaign that was run was thought to be contentious. On social media like Twitter, the majority of people freely share their thoughts. The purpose of this study is to investigate how the general public reacted to the placement of billboards advertising political figures during the epidemic and to categorize those responses. It is envisaged that it would also provide advice for connected parties that may be used when making judgments regarding the policy of constructing billboards for political figures during a pandemic based on the results of data analysis. Twitter users tend to be more expressive because of the character limits, which means they have sentimental or emotional values. Using the Nave Bayes Algorithm, it is possible to do sentiment analysis on the sentiment data by categorizing user comments into positive, negative, and neutral attitudes. Regarding the sentiments expressed on billboards showing political leaders during the pandemic, tweets were sorted into three categories: liked, unfavorable, and neutral. The accuracy rate from Naive Bayes categorization of political personalities during the pandemic on social media Twitter was 83.3% with a precision value of 89%, recall 83%, and f-1 score of 82%. Creator An entity primarily responsible for making the resource Ade Bastian1 , Ardi Mardiana2 , Dinda Sri Wulansari3 Publisher An entity responsible for making the resource available Universitas Majalengka Date A point or period of time associated with an event in the lifecycle of the resource 03-02-2023 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