3 10 26 https://repository.horizon.ac.id/files/original/886fdbf4943cb698f245beec1a6e4e2a.pdf dc421d163f1a59ba9ed8ac13e1890e84 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 6 (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 Learning Management System Acceptance Analysis Using Hedonic Motivation System Adoption Model Subject The topic of the resource LMS, HMSAM, PLS-SEM, Online learning Description An account of the resource Online learning using LMS (Learning Management System) results in demotivation for Lecturers and Students. This study aims to explore the relationship between the contentment of using LMS with the behavioural intentions and user focus while using the LMS. The present study employed the user's perception of using LMS with HMSAM (Hedonic Motivation System Adoption Model) as the theoretical basis. The quantitative research method employed a questionnaire as a data collection method. The collected data were analysed statistically using the PLS-SEM method with SmartPLS 3.2.9 application. The results of the study showed that of the 10 (ten) hypotheses, 9 (nine) were accepted, and 1 (one) was rejected. In particular, the hypothesis indicating excitement affects behavioural intentions using the LMS shows a t-statistic value of 1.887 (t�statistics < t-value) hence being rejected. This study also provides recommendations for LMS development based on usability, curiosity, excitement, and control factors. Creator An entity primarily responsible for making the resource Victor Alva Andrian Rehy1 , Johan J.C. Tambotoh2 Publisher An entity responsible for making the resource available Universitas Kristen Satya Wacana Date A point or period of time associated with an event in the lifecycle of the resource 27-12-2022 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/c5ba5bcfa50c136b0678a860ba081fb8.pdf 3c0baca25105071f0bbfc35001b9e5ab 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 6 (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 The Role of Genetics in Domestic Research on Forestry Issues: A Text Mining Analysis Subject The topic of the resource data science, forestry issue, genetic diversity Description An account of the resource As a megadiverse country, Indonesia has plentiful genetic resources. The interest of domestic researchers in it and its relation to forestry scope is the focus of this paper. The objective is to determine the genetics aspects represented in forestry scholarly articles. Text mining analysis is carried out for the abstract articles, followed by topic modeling and trend analysis. Python libraries were used to conduct this research. Garuda website was the main source of the data collection. Natural language Toolkits (NLTK) were used to retrieve article information from Garuda. Sci-kit learn (SKLearn) of Latent Dirichlet Allocation module was used for topic modeling analysis, and pyLDAVis was used to represent it. SKLearn was also used for trending analysis. After article text retrieval, three topic clusters were found: forest diversity, products, and land use. The topics were scattered in 1966 abstract articles that were found during data retrieval. Article growth showed the quadratic pattern known after regression analysis. The trend showed the rapid growth of topics and scholars' interest, but the number of articles was low compared to the total articles on the Garuda portal Creator An entity primarily responsible for making the resource Titis Hutama Syah Publisher An entity responsible for making the resource available Sekolah Tinggi Pertanian Kutai Timur Date A point or period of time associated with an event in the lifecycle of the resource : 29-12-2022 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/2fde583128e81a7ff0709bb0c3a42bdc.pdf f0d04fb9a415ac8c64099bceb61a0b34 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 6 (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 Credit Card Fraud with Machine Learning Methods and Resampling Techniques Subject The topic of the resource machine learning, ensemble learning, classification, SMOTE, credit card fraud Description An account of the resource Financial institutions in the form of banks provide facilities in the form of credit cards, but with the development of technology, fraud on credit card transactions is still common, so a system is needed that can detect fraud transactions quickly and accurately. Therefore, this study aims to classify fraudulent transactions. The proposed method is Ensemble Learning which will be tested using the Boosting type with 3 variations, namely XGBoost, Gradient Boosting, and AdaBoost. Then, to maximize the performance of the model, the dataset used is optimized with the Synthetic Minority Oversampling Technique (SMOTE) function from the Imblearn library in the data train to handle imbalanced dataset conditions. The dataset used in this study is entitled "Credit Card Fraud Detection" with a total of 284807 data which is divided into two classes: Not Fraud and Fraud. The proposed model received a recall of 92% with Gradient Boosting, where the results increased by 10.37% compared to the previous study using Random Forest with a recall result of 81.63%. This is because the use of SMOTE in the data train greatly influences the classification of Not fraud and fraud classes. Creator An entity primarily responsible for making the resource Moh. Badris Sholeh Rahmatullah1 , Aulia Ligar Salma Hanani2 , Akmal M. Naim3 , 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 27-12-2022 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/bf02ba004fecaa851cb508d3c306b1f5.pdf cabfe760302c793ee587d7eb8ab591a6 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 6 (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 Ant Colony Optimization Modelling for Task Allocation in Multi-Agent System for Multi-Target Subject The topic of the resource task allocation, multi-agent system, multiple tasks, ACO Description An account of the resource Task allocation in multi-agent system can be defined as a problem of allocating a number of agents to the task. One of the problems in task allocation is to optimize the allocation of heterogeneous agents when there are multiple tasks which require several capabilities. To solve that problem, this research aims to modify the Ant Colony Optimization (ACO) algorithm so that the algorithm can be employed for solving task allocation problems with multiple tasks. In this research, we optimize the performance of the algorithm by minimizing the task completion cost as well as the number of overlapping agents. We also maximize the overall system capabilities in order to increase efficiency. Simulation results show that the modified ACO algorithm has significantly decreased overall task completion cost as well as the overlapping agents factor compared to the benchmark algorithm. Creator An entity primarily responsible for making the resource Iis Rodiah1 , Medria Kusuma Dewi Hardhienata2 , Agus Buono3 , Karlisa Priandana4 Publisher An entity responsible for making the resource available Iis Rodiah1 , Medria Kusuma Dewi Hardhienata2 , Agus Buono3 , Karlisa Priandana4 Date A point or period of time associated with an event in the lifecycle of the resource 27-12-2022 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/1f95223e79ba8bf2c5542d51eefd68e4.pdf 5f25429948be647e7894c78b6bc042ed 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 6 (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-Means Clustering Algorithm Approach in Clustering Data on Cocoa Production Results in the Sumatra Region Subject The topic of the resource K- Means Clustering Algorithm, Gaussian Mixture Model, Data Mapping, Cocoa Farm Description An account of the resource Cocoa agricultural production in Indonesia is currently very low while demand continues to increase every year, so it is very important to build a model that can categorize cocoa farming data. The main objective of this research is to analyze agricultural data using data mining techniques that specifically use the K-Means Clustering algorithm, and Gaussian Mixture Models. In this research, we used quantitative research because it measure number-based data. The results of cocoa production so far still depend on land area, then the number of cocoa trees has a significant effect on the amount of production so it is very important for the government and researchers to develop technologies that can increase cocoa production yields where the demand for cocoa is currently very high in demand worldwide because it can classify the cocoa quality from good quality to poor quality. Based on testing the K-Means Clustering and Gaussian Mixture Model algorithms on data on cocoa production in four provinces, namely North Sumatra, West Sumatra, Lampung and Aceh which were optimized by the Silhouette method, it produced cluster values of 2, 3 and 4. second with a value of 59.8%. Creator An entity primarily responsible for making the resource Mawaddah Harahap1 , Arief Wahyu Dwi Ramadhanu Zamili 2 , Muhammad Arie Arvansyah3 , Erwin Fransiscus Saragih4 , Selwa Rajen5 , Amir Mahmud Husein Publisher An entity responsible for making the resource available University of Prima Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 27-12-2022 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/38f0b07e5336c9d209935bb2df116244.pdf afd5958bdc8a98a21b690e1e1452263e 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 6 (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 Fire Detection on Video Using ViBe Algorithm and LBP-TOP Subject The topic of the resource fire detection, ViBe algorithm, local binary pattern-three orthogonal planes, support vector machine Description An account of the resource In this research, we built a system to detect fire using the ViBe (Visual Background Extractor) algorithm to extract dynamic targets. The ViBe algorithm is better at detecting moving target objects such as flame combustion. In this research we combined the ViBe algorithm with three frame differencing to gain better results on movement object. The HSI color space model was applied after the movement object was obtained. We used Local Binary Pattern-Three Orthogonal Planes to obtain the feature extraction to be classified with Support Vector Machine. Our result has shown that the proposed system were able to detect the fire using the LBP-TOP and ViBe algorithm methods with an average accuracy rate of 88.10%, and the best accuracy was 90.37%. The parameters used to achieve this accuracy in the feature extraction process were T=120, Radius=2, and frame gap=15, then the threshold value parameter for three-frame difference parameter was 25. Creator An entity primarily responsible for making the resource Febryanti Sthevanie1 , Gamma Kosala2 , Kurniawan Nur Ramadhani3 , Ketut Sudyatmika Putra4 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 27-12-2022 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