Rancang Bangun Website Analysthub Berbasis Problem-Based Learning Untuk Meningkatkan Kompetensi Associate Data Analyst - Alfa Qorina; Muhammad Sonhaji Akbar (Jurnal Ilmu Ekonomi, Pendidikan dan Teknik, 2026)
Data Analysis instruction at SMKN 1 Sarirejo remains lecture-based, leaving students passive and their Associate Data Analyst competencies underdeveloped, with an average score of 69.6, below the minimum criterion of 75. This study describes the development of “AnalystHub,” an interactive website based on Problem-Based Learning (PBL); analyzes the improvement of students' Associate Data Analyst competencies; and determines the website's usability using the System Usability Scale (SUS). The study used an R&D method with the ADDIE model, involving 22 tenth-grade TKJ students at SMKN 1 Sarirejo. AnalystHub was built using Laravel 12, Tailwind CSS, MySQL, Pyodide, a Groq API-based AI Assistant, and Chart.js, and was validated by four experts with an average score of 93.0% (Very Valid). The Paired Sample T-Test showed a significant increase in cognitive competency (t = -24.147; p < 0.001) with a mean N-Gain of 0.689 (Medium, approaching High), while psychomotor competency achieved a mean posttest score of 81.30 (Good). SUS measurement yielded a mean score of 81.36 (Grade B, Acceptable), proving PBL integration effectively improves vocational students' data analysis competencies.