SVM Method for Classification of Primary School Teacher Education Journal Articles

Pujianto, Utomo; Zaeni, Ilham Ari Elbaith; Irawan, Ninon Oktaviani

doi:10.1109/iceeie47180.2019.8981455

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…The results obtained in testing the data from the total dataset used are 754 data from 2018, 2019, 2020, and 2021, with detailed data as much as the work program in 2018 there are 262 data, in 2019 there are 189 data, in 2020 there are 174 data. In 2021 there are 161 there, and overall the data for very good results by carrying out tests and a similar level on the results of work program contributions where the existing results for carrying out the results of the classification experiment between SVM and perceptron are very good and more dominant in SVM as at 89, 2% from [30] then there is 80.5% from [31] as well as from the perceptron with a value of 96.2%, with a value of 85.0% from [14].…”

Section: Results and Analysismentioning

confidence: 96%

Comparative Analysis of SVM and Perceptron Algorithms in Classification of Work Programs

Samudra¹,

Rosnelly²,

Situmorang³

2023

matrik

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Government agencies are required to mobilize every aspect of publication which is carried out every year which must be accounted for and also carried out for each device that receives it such as assisted villages by utilizing available apbd funds in maximizing work programs designed so that they can be implemented optimally and effectively. by getting the best from all aspects of the work program implementation, of course there are important points in designing an annual work program without exception. data mining itself can help the department of population, family planning, women's empowerment and child protection in analyzing each work program design from before it is implemented onwards to look at various aspects of past data whose grouping is in the form of classification. The purpose of this study is to build a classification model with the addition of a sigmoid activation function that uses svm and perceptron to obtain a comparison value for the accuracy of the algorithm used to obtain the best working program design. The classification results are used to get the best value for classifying the best P2KBP3A work program dataset where it can be seen that the average accuracy value is 87.5%, the f1 value is 82.2%, the precision value is 80.2%, and the recall value is 87.5% so that the final result of the research results obtained a good accuracy value.

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Section: Results and Analysismentioning

confidence: 96%

Comparative Analysis of SVM and Perceptron Algorithms in Classification of Work Programs

Samudra¹,

Rosnelly²,

Situmorang³

2023

matrik

View full text Add to dashboard Cite

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“…Mite-Baidal et al [7] presented a literature review using sentiment analysis for educational data mining and indicated that SVM and Naive Bayes are the most used techniques. Pujianto et al [8] used SVM for text classification for journal articles about Primary School Teacher Education. Ranjeeth et al [9] used a single SVM and other machine learning models for the prediction of student performance in secondary education.…”

Section: Introductionmentioning

confidence: 99%

Learning Time Acceleration in Support Vector Regression: A Case Study in Educational Data Mining

Pimentel

Ospina

Ara

2021

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The development of a country involves directly investing in the education of its citizens. Learning analytics/educational data mining (LA/EDM) allows access to big observational structured/unstructured data captured from educational settings and relies mostly on machine learning algorithms to extract useful information. Support vector regression (SVR) is a supervised statistical learning approach that allows modelling and predicts the performance tendency of students to direct strategic plans for the development of high-quality education. In Brazil, performance can be evaluated at the national level using the average grades of a student on their National High School Exams (ENEMs) based on their socioeconomic information and school records. In this paper, we focus on increasing the computational efficiency of SVR applied to ENEM for online requisitions. The results are based on an analysis of a massive data set composed of more than five million observations, and they also indicate computational learning time savings of more than 90%, as well as providing a prediction of performance that is compatible with traditional modeling.

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SVM Method for Classification of Primary School Teacher Education Journal Articles

Cited by 2 publications

References 12 publications

Comparative Analysis of SVM and Perceptron Algorithms in Classification of Work Programs

Comparative Analysis of SVM and Perceptron Algorithms in Classification of Work Programs

Learning Time Acceleration in Support Vector Regression: A Case Study in Educational Data Mining

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