Analysis of Perception and Knowledge on Chemometric Competence among Students and Practitioners Using SEM–PLS - Neni Alyani; Miftahul Madya; Nur Handayani; Aini Ayu; Taufik Hudha; Octaviano Pratama (Institut Pemerintahan Dalam Negeri, 2025)
The advancement of digital technology and data driven analysis in the 21st century has driven a major transformation in science education. The integration of coding, artificial intelligence (AI), and deep learning requires strong computational and analytical thinking skills. One of the emerging disciplines addressing this need is chemometrics, a field that combines mathematics, statistics, and computer science to interpret scientific dataestablishing it as a key competence in modern scientific research and education. This study aims to analyze the relationships among perception (X1), knowledge (X2), and chemometric competence (X3) among students and practitioners, in order to identify the factors contributing to the enhancement of data analysis skills in AI based learning environments. Data were collected through an online survey using google forms with a seven point Likert scale, involving respondents from D4, S1, S2, S3, practitioners. The data were analyzed using structural equation modeling–partial least squares (SEM–PLS). Results indicate that all constructs met the criteria for convergent validity (loading factor > 0.70; AVE > 0.50) and reliability (Cronbach’s alpha, Composite Reliability > 0.70). The AVE values ranged from 0.713 to 0.924, with several indicators in X3 showing VIF > 10, suggesting redundancy. The findings demonstrate that perception and knowledge significantly influence chemometric competence, emphasizing the urgency of integrating chemometrics into science education curricula to strengthen AI based analytical literacy.