High Validity Index Research Articles

Validity and Reliability Diagnostic Test Computational Thinking Based on Local Wisdom

This study aims to analyze the validity and reliability of the diagnostic test instrument used to measure computational thinking skills based on local wisdom. This method of research is instrument development research. The subjects of this research were students of computer science and information technology undergraduate programs at Bumigora University, totaling 108 students using a random sampling technique. Data collection techniques using tests. The data analysis technique of instrument content validity was determined based on Aiken's V formula, instrument construct validity using EFA, and instrument reliability using Cronbach Alpha. Based on the results of this research, the content validity of the instrument has a high validity index, the construct validity of the instrument items is declared feasible, and the reliability of the instrument has a high-reliability coefficient. So, this instrument is declared valid, reliable, and suitable for use to measure local wisdom-based computational thinking diagnostic tests.

Improved Regularized Multi-class Logistic Regression for Gene Classification with Optimal Kernel PCA and HC Algorithm.

A significant challenge in high-dimensional and big data analysis is related to the classification and prediction of the variables of interest. The massive genetic datasets are complex. Gene expression datasets are enriched with useful genes that are associated with specific diseases such as cancer. In this study, we used two gene expression datasets from the Gene Expression Omnibus and preprocessed them before classification. We used optimal kernel principal component analysis in which the optimal kernel function was chosen for dataset dimensionality reduction and extraction of the most important features. The gene sets with a high validity index were collected using a combined hieratical clustering and optimal kernel principal component analysis (KHC-RLR) algorithm. Logistic regression is one of the most common methods for classification, and it has been shown to be a useful classification approach for gene expression data analysis. In this study, we used multi-class logistic regression to classify the collected gene sets. We found that ordinary logistic regression caused a major overfitting problem; therefore, we used regularized multi-class logistic regression to classify the gene sets. The proposed KHC-RLR algorithm showed a high performance and satisfied accuracy measures.