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Measuring Resampling Methods on Imbalanced Educational Dataset’s Classification Performance

irfan pratama, Putri Taqwa Prasetyaningrum, Albert Yakobus Chandra, Ozzi Suria — Sun, 25 Feb 2024 00:00:00 +0000

Imbalanced data refers to a condition that there is a different size of samples between one class with another class(es). It made the term “majority” class that represents the class with more instances number on the dataset and “minority” classes that represent the class with fewer instances number on the dataset. Under the target of educational data mining which demands accurate measurement of the student’s performance analysis, data mining requires an appropriate dataset to produce good accuracy. This study aims to measure the resampling method’s performance through the classification process on the student’s performance dataset, which is also a multi-class dataset. Thus, this study also measures how the method performs on a multi-class classification problem. Utilizing four public educational datasets, which consist of the result of an educational process, this study aims to get a better picture of which resampling methods are suitable for that kind of dataset. This research uses more than twenty resampling methods from the SMOTE variants library. as a comparison; this study implements nine classification methods to measure the performance of the resampled data with the non-resampled data. According to the results, SMOTE-ENN is generally the better resampling method since it produces a 0,97 F1 score under the Stacking classification method and the highest among others. However, the resampling method performs relatively low on the dataset with wider label variations. The future work of this study is to dig deeper into why the resampling method cannot handle the enormous class variation since the F1 score on the student dataset is lower than the other dataset.

Exploring the Potentials of Augmented Reality in Medical Education: A Bibliometric Analysis and Scientific Visualization

Tue, 26 Mar 2024 00:00:00 +0000

Alongside the COVID-19 pandemic, digitalization has significantly impacted medical education. The pandemic has necessitated several adaptations, including transitioning from a traditional learning model to a digital-based one. One form of this is augmented reality (AR). The future adoption of AR in medical education is bright and considerable. Therefore, evaluating AR in medical education is essential. One such method is bibliometric analysis. Using comprehensive bibliometric analysis, we aimed to collect data on the tendencies of this topic. The research examined terms, countries/territories, publication numbers, institutions, authors, and published journals. The Scopus database was used to compile the material. VOSviewer analyzed the complete bibliometric information. The analysis was based on data from 379 Scopus papers that met our criteria. The statistics demonstrated that the most significant expansion occurred in 2021, with the USA being the most productive country. The Journal of Studies in Health Technology and Informatics is the leading publication, and the Aristotle University of Thessaloniki has published the most papers. "The effectiveness of virtual and augmented reality in health sciences and medical anatomy" is the most cited paper. Bamidis, P. D., and Moro, C., made the most significant research contributions. In this field, further study is required, particularly in emergency medicine and clinical skills training for medical students. In conclusion, implementing augmented reality in medical education has tremendous potential.

Comparison of Convolutional Neural Network Methods for the Classification of Maize Plant Diseases

Mohamad Ilyas Abas, Syafruddin Syarif, Ingrid Nurtanio, Zulkifli Tahir — Sun, 31 Mar 2024 00:00:00 +0000

The focus of this study is the classification of maize images with common rust, gray leaf spot, blight, and healthy diseases. Various models, including ResNet50, ResNet101, Xception, VGG16, and ENet, were tested for this purpose. The dataset used for corn plant diseases is publicly available, and the data were split into separate sets for training, validation, and testing. After processing the data, the following models were identified: the Xception model epoch with an accuracy of 83.74%, the ResNet model with an accuracy of 97.19% at epoch 8/10, the ResNet101 model with an accuracy of 97.55% at epoch 10/10, and the ENet model with an accuracy of 98.69% at epoch 9/1000. ENet exhibited the highest accuracy among the five models at 98.69%. Additionally, ENet achieved an average accuracy of 95.45%, the highest among all tested models, based on the average accuracy in the confusion matrix. This research indicates that ENet performs best at processing data related to maize plant diseases. Consequently, the analysis of maize plant diseases is expected to evolve as a result of this research. Following the implementation of the system's generated model, this research will continue to explore its impact. The intention is to provide a summary of the comparative classification performance of CNN algorithms.

Development of GWIDO: An Augmented Reality-based Mobile Application for Historical Tourism

Faisal Akbar, Hadiyanto, Catur Edi Widodo — Tue, 26 Mar 2024 00:00:00 +0000

This research aimed to design and reconstruct a business model for an augmented reality (AR) camera mobile application for historical tourism at Keraton Kasepuhan Cirebon. The goal was to utilize AR technology to provide an immersive and informative experience for tourists. The research addressed several main problems, including navigation and historical information through object tracking, by implementing an online application with features such as Indonesian and English language instructions to better serve domestic and foreign tourists. The research also aimed to investigate the benefits of using AR technology for object tracking and navigation and to explore how these aspects could be related to creating a formula that supports each other in addressing the formulated problems. Through the development of the GWIDO application, a positive impact on the development of historical tourist attractions was observed. This can be seen from the usefulness of its features such as AR navigation, which can be used as a virtual guide. The data collected was used to design and reconstruct the business model, which was implemented and tested to collect additional data for analysis. The final results of the research showed that the AR camera mobile application was effective in providing an immersive and informative experience for tourists. The redesigned business model improved the utilization of AR technology in the tourism industry. Based on the test results, the average response time for object distance between 0.1 meters to 0.5 meters was between 1.45 to 2.07 seconds, and the average time for object distance from visitors was between 3.15 to 4.71 seconds with a confidence level of 95%. Meanwhile, testing for navigation features using augmented reality is very dependent on the internet signal used on the user's device. The level of accuracy of objects that have been placed at certain coordinates is determined by how well the internet network performs, allowing objects to appear precisely according to their coordinates.