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Kohonen Network Modeling for Lemma Recognition in Bangka Dialect of Malay 8

Kohonen Network Modeling for Lemma Recognition in Bangka Dia...

引用

8th International Conference on Cyber and IT Service Management, CITSM 2020

作者： Budi Perkasa, Eza Santoso, Hadi Supardi Alkodri, Ari Amir Technical Information Study Program ISB Atma Luhur Pangkalpinang Indonesia Information System Study Program ISB Atma Luhur Pangkalpinang Indonesia

ISBN: (纸本)9781728177014

There are numbers of lemmas in the Bangka Dialect of Malay (BDoM) Dictionary. However, tracking the lemmas that have been used before is an impractical work because there are thousands of lemmas that must be traced one by one. This research intends to minimize the search burden of many lemma data using the Kohonen network. By using the Kohonen network, it is sufficient to do training on the sample data provided which is far less than the actual data. The result of this training is then used to obtain the number of lemmas that are successfully identified by the Kohonen network. The result can also be used to propose a lemma so that the status of acceptance of the proposed lemma can be determined. The overall result that is variable is obtained because of the optimization used. From the test result, it is also found that there is no linear relationship between the level of learning rate and the number of names that were successfully identified. Further research that can be done are the inclusion of non-BDoM lemma training data, changing Kohonen network parameters, or using other recognition methods. © 2020 IEEE.

关键词： Bangka Dialect of Malay Kohonen network lemma recognition string recognition

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Combining Dynamic K-Means and Binary Search Centroid to Optimize Clustering Results on Home Industry Datasets 9

Combining Dynamic K-Means and Binary Search Centroid to Opti...

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9th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2022

作者： Santoso, Hadi Magdalena, Hilyah Faculty of Information Technology Information System Study Program ISB Atma Luhur Pangkalpinang Indonesia

ISBN: (数字)9786239213565

ISBN: (纸本)9781665470438

This study aims to improve the Dynamic k-means algorithm for determining the optimal number of clusters and iteration. Unfortunately, the dynamic k-means algorithm has limitations in determining the centroid point, whereas the binary search centroid algorithm can determine the cluster's centroid point but has limitations in determining the number of clusters. In the case of home industry datasets, it is proposed to optimize clustering results using a combination of Dynamic k-means algorithm and binary search centroid based on the advantages and disadvantages of each algorithm. 3,466 home industries were used as the sample data. The validity of both algorithms was assessed using the Davies Bouldin Index (DBI) methods. In clustering home industry datasets, the minimum value of DBI Cluster algorithm combination of Dynamic k-means and binary search centroid K-means (DK-means with BSCK-means) is the smallest, which is 0.1845, when compared to only Dynamic K-means algorithm. The final result of home industry clustering using a combination of Dynamic K-means and the Binary Search Centroid algorithm yields an optimal value in the fifth iteration, with the acquisition of the beginner cluster C1 is 3224, the developing cluster C2 is 160, and the advanced cluster C3 is 82. © 2022 Institute of Advanced Engineering and Science (IAES).

关键词： binary search centroid clustering dynamic k-means home industry optimize

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Kohonen Network Modeling for Lemma Recognition in Bangka Dialect of Malay

Kohonen Network Modeling for Lemma Recognition in Bangka Dia...

引用

International Conference on Cyber and IT Service Management (CITSM)

作者： Eza Budi Perkasa Hadi Santoso Supardi Ari Amir Alkodri Technical Information Study Program ISB Atma Luhur Pangkalpinang Indonesia Information System Study Program ISB Atma Luhur Pangkalpinang Indonesia

ISBN: (数字)9781728177014

ISBN: (纸本)9781728177021

关键词： Training data Training information systems Computational modeling Adaptation models Support vector machines Optimization

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YoBagi's User Experience Evaluation using User Experience Questionnaire 7

YoBagi's User Experience Evaluation using User Experience Qu...

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7th International Conference on Informatics and Computing, ICIC 2022

作者： Juniawan, Fransiskus Panca Sylfania, Dwi Yuny Putra, Rendy Rian Chrisna Henderi Program Study of Technology Information University of Bangka Belitung Pangkalpinang Indonesia Program Study of Informatics Engineering Institut Sains Dan Bisnis Atma Luhur Pangkalpinang Indonesia University of Raharja Informatics Engineering Faculty of Science and Technology Tangerang Indonesia

ISBN: (纸本)9798350345711

The pandemic has brought many negative impacts on human life. One of the negative impacts is economic downturn of Micro, Small, and Medium Enterprise (MSME) actors in Bangka Belitung Province. To solve this problem, a website-based marketplace platform called Yobagi was created. As we know, a new software product must have a good user experience. By having a good user experience means that the product has met the needs of its users. For this reason, an evaluation of the user experience is necessary. This study aims to measure the user experience of using Yobagi. The evaluation uses the User Experience Questionnaire (UEQ) tool, which consists of six scales: Attractiveness, Efficiency, Perspicuity, Dependability, Stimulation, and Novelty. There were 40 respondents who were taken from the top of MSME actors. As the UX evaluation results, it is known that the final score of the six scales is above 0.8 and at the excellent level. This means that Yobagi users have a very good user experience in using Yobagi. In addition, Yobagi has met the criteria for good software by having an excellent user experience value. © 2022 IEEE.

关键词： User experience

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The Effect of Data Augmentation on the Accuracy of Fish Species Classification Using Deep Learning

The Effect of Data Augmentation on the Accuracy of Fish Spec...

引用

International Conference on Informatics and Computing (ICIC)

作者： Ellya Helmud Catur Edi Widodo Oky Dwi Nurhayati Doctor Information System Postgraduate Diponegoro University Semarang Indonesia Faculty of Information Technology Information Systems Study Program Atma Luhur Institute of Science and Business Pangkalpinang Indonesia Physics Department Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Computer Engineering Faculty of Engineering Diponegoro University Semarang Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Fish image classification presents an intriguing challenge in the field of computer vision. This research aims to develop an accurate classification model to differentiate between four different fish species using a convolutional neural network. The dataset used consists of $\mathbf{3 0 1 0}$ fish images, divided into training, validation, and testing sets. The convolutional neural network model was trained both with and without data augmentation. Evaluation results show that the model trained with data augmentation achieved an accuracy of $95 \%$ with a loss value of 0.0983, slightly better than the model without augmentation which achieved an accuracy of $94.56 \%$ with a loss value of $\mathbf{0. 1 7 9 4}$. This indicates that data augmentation techniques are effective in improving model performance, likely because augmentation helps the model generalize better to variations in fish image data. The results of this research demonstrate the significant potential of convolutional neural network for fish image classification tasks. The developed model can serve as a foundation for the development of computer vision-based applications such as automatic fish species identification in fisheries or educational applications. Further research can be conducted by exploring different convolutional neural network architectures, more advanced data augmentation techniques, and larger datasets to improve model performance.

关键词： Deep learning Training Accuracy Computational modeling Computer architecture Fish Data augmentation Data models Convolutional neural networks Image classification

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YoBagi's User Experience Evaluation using User Experience Questionnaire

YoBagi's User Experience Evaluation using User Experience Qu...

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International Conference on Informatics and Computing (ICIC)

作者： Fransiskus Panca Juniawan Dwi Yuny Sylfania Rendy Rian Chrisna Putra Henderi Program Study of Technology Information University of Bangka Belitung Pangkalpinang Indonesia Program Study of Informatics Engineering Institut Sains dan Bisnis Atma Luhur Pangkalpinang Indonesia Informatics Engineering Faculty of Science and Technology University of Raharja Tangerang Indonesia

ISBN: (纸本)9798350345728

关键词： Economics system testing Pandemics Entrepreneurship User experience Software Informatics

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