Preferences Analysis of Engineering Students on Choosing Learning Media using Support Vector Machine (SVM) Model

Larasati, Aisyah; Hajji, Apif M.; Handayani, Anik Nur

doi:10.2991/icovet-18.2019.15

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…Instance data classification is a process of predicting classes [3]. Classification is widely used in various fields including education [4] [5], banking [6], health [7] [8], stockbreeding [9], and even game development [10]. Naïve Bayes, K Nearest Neighbor, Decision Tree, Support Vector Machine (SVM), Convolution Neural Network (CNN), and hybrid approaches are some of the most commonly used classification methods.…”

Section: Introductionmentioning

confidence: 99%

Classification of Engineering Journals Quartile using Various Supervised Learning Models

et al. 2023

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In scientific research, journals are among the primary sources of information. There are quartiles or categories of quality in journals which are Q1, Q2, Q3, and Q4. These quartiles represent the assessment of journal. A classification machine learning algorithm is developed as a means in the categorization of journals. The process of classifying data to estimate an item class with an unknown label is called classification. Various classification algorithms, such as K-Nearest Neighbor (KNN), Naïve Bayes, and Support Vector Machine (SVM) are employed in this study, with several situations for exchanging training and testing data. Cross-validation with Confusion Matrix values of accuracy, precision, recall, and error classification is used to analyzed classification performance. The classifier with the finest accuracy rate is KNN with average accuracy of 70%, Naïve Bayes at 60% and SVM at 40%. This research suggests assumption that algorithms used in this article can approach SJR classification system.

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Section: Introductionmentioning

confidence: 99%

Classification of Engineering Journals Quartile using Various Supervised Learning Models

et al. 2023

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“…The rapid development in digital technology has also created new challenges to conventional education systems at various levels of education, from basic education to higher education [2]. The popularity of mobile terminals and the rapid development of the mobile Internet have provided a carrier for the development of mobile learning (m-learning) and have made mobile learning a hot research topic in education informatization [3].…”

Section: Introductionmentioning

confidence: 99%

The EEG-Based Attention Analysis in Multimedia m-Learning

Wang

Liu

2020

Computational and Mathematical Methods in Medicine

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In recent years, research on brain-computer interfaces has been increasing in the field of education, and mobile learning has become a very important way of learning. In this study, EEG experiment of a group of iPad-based mobile learners was conducted through algorithm optimization on the TGAM chip. Under the three learning media (text, text + graphic, and video), the researchers analyzed the difference in learners’ attention. The study found no significant difference in attention in different media, but learners using text media had the highest attention value. Later, the researchers studied the attention of learners with different learning styles and found that active and reflective learners’ attention exhibited significant differences when using video media to learn.

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Development of machine learning implementation in engineering education: A literature review

Sasmita

Mulyanti

2020

IOP Conf. Ser.: Mater. Sci. Eng.

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This study has aims to determine the development of implementing machine learning in several engineering majors. The used method was a literature study, and secondary data was used from reputable international journals and published in 2015 to 2019 from each publisher, which is IEEEXplore, Springer Link, Science Direct, ERIC, and Google Scholar. The author was summarized and analysed articles obtained based on the year of publication and the context of the article. Results show that machine learning has been widely applied in engineering education through fourteen contexts, one of which is Prediction Student Academic Performance, which has continuous development from 2013 to 2019. And the total number of engineering majors that are implementing machine learning was 13 majors. This research was expected to be an illustration, reference, and consideration for technicians in engineering education to give more attention and can be applied in schools, universities, and other engineering institutions in Indonesia country.

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Preferences Analysis of Engineering Students on Choosing Learning Media using Support Vector Machine (SVM) Model

Cited by 3 publications

References 2 publications

Classification of Engineering Journals Quartile using Various Supervised Learning Models

Classification of Engineering Journals Quartile using Various Supervised Learning Models

The EEG-Based Attention Analysis in Multimedia m-Learning

Development of machine learning implementation in engineering education: A literature review

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