Open AccessClassification of critical thinking in mathematics using particle swarm optimization based neural network algorithms
Author(s) -
Ade Irma Purnamasari,
Saeful Anwar,
Martanto,
Ahmad Faqih,
Nisa Dienwati Nuris
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1088/1/012039
Subject(s) - artificial neural network , particle swarm optimization , artificial intelligence , critical thinking , algorithm , computer science , machine learning , mathematics , mathematics education
Critical thinking in mathematics can be defined as the processes and abilities used to understand concepts, apply, synthesize and evaluate the information generated. Critical thinking in mathematics is a skill for higher order thinking. It is understood that logical thought plays a part in spiritual growth, social progress, behavioral growth, cognitive development and science progress. This study aims to classify critical thinking skills. The method used to determine the classification of critical thinking skills is to use the Neural Network algorithm. A method which has the potential to classify structured data is the neural network. In this study, a neural network algorithm model was developed. A technology that has the potential to identify structured data is the neural network. In this study, a neural network algorithm model was created. With this neural network model, it can be seen the classification of critical thinking skills. The amount of data used as data in this analysis was 150 in the form of school data and as many as 40 measures were measured in the form of math scores. On the basis of the research results, it was found that the neural network model based on Particle Swarm Optimization achieved an accuracy value of up to 93.33 percent with a 2 percent variance, tested using the k-cross-validation method.