Hazem Salim, Abdullah and Salim Abdullah, Saleh and Mario, Maroun (2025) Exploring Machine Learning Techniques for Questionnaire Analysis. International Journal of Informatics and Data Science Research, 2 (7). pp. 33-45. ISSN 2997-3961

Text 33-45 Exploring Machine Learning Techniques.pdf Download (1MB)

Abstract

The application of machine learning in questionnaire design and evaluation is examined in this work, with a special emphasis on data quality, specifically Cronbach's alpha. It evaluates the effect of a method for creating various Cronbach's alpha random data (0.8–0.94) on machine learning algorithms. The method minimizes human labor in questionnaire design by generating data with estimated internal consistency through the use of machine learning and natural language processing. To assess how Cronbach's alpha affects the accuracy of machine learning models, the data is analyzed. When dealing with low Cronbach's problems, the ML models RF, NB, LR, SVM, and KNN, respectively, have acceptable and exceptional accuracy (58% - 100%). However, when dealing with high Cronbach's problems, their accuracy drops to 26% - 79%.

Item Type:	Article
Subjects:	L Education > L Education (General)
Divisions:	Postgraduate > Master's of Islamic Education
Depositing User:	Journal Editor
Date Deposited:	30 Jul 2025 09:06
Last Modified:	30 Jul 2025 09:06
URI:	http://eprints.umsida.ac.id/id/eprint/16303

Actions (login required)

View Item