The Comparison of Data Mining Algorithms for Classification of Suggestions from Computer-Based Written Exam Participants
| Autor: | Hafiz Muhardi, Enda Esyudha Pratama, Morteza Muthahhari, Fahmi Sajid |
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| Rok vydání: | 2019 |
| Předmět: |
Data processing
Recall Computer science business.industry 0211 other engineering and technologies Decision tree 02 engineering and technology Machine learning computer.software_genre Test (assessment) Support vector machine Kernel (linear algebra) Naive Bayes classifier Statistical classification ComputingMethodologies_PATTERNRECOGNITION 020204 information systems 0202 electrical engineering electronic engineering information engineering Artificial intelligence business computer 021101 geological & geomatics engineering |
| Zdroj: | 2019 International Conference on Data and Software Engineering (ICoDSE). |
| DOI: | 10.1109/icodse48700.2019.9092610 |
| Popis: | The Computer-Based Written Exam is a mandatory requirement for taking the State University Joint Entrance Test. As a form of implementation evaluation, each test participant is required to comment on the questionnaire given. The number of comments collected was 663 comments. Data processing used data mining methods. One form of utilization of data mining is classification. These comments were classified into 10 categories. This study compared the performance of classification algorithms based on parameter values of accuracy, precision, and recall. The classification algorithm compared was Naive Bayes, Support Vector Machine (SVM), and Decision Tree. The SVM algorithm had better performance with an average value of accuracy, precision, and recall of 77.46%, 77.05%, and 74.65%. |
| Databáze: | OpenAIRE |
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