Application of Visual Data Mining for Visualization of UKBI Achievement Data - Ketri genes Yolanda; Pradita Eko Prasetyo Utomo; Zainil Abidin (Yayasan Cita Cendekiawan Al Khwarizmi, 2025)
The current visualization of Adaptive Indonesian Language Proficiency Test (UKBI Adaptif) results in Jambi Province is suboptimal, often relying on static, basic charts, which hinders transparency and the effective formulation of evidence-based language policies. This research aims to address this critical gap by developing an interactive, data-driven system to analyze the language proficiency profile of UKBI participants in Jambi from 2021 to 2024. The research objective is to accurately map regional competence, identify hidden patterns, and provide actionable intelligence to the Jambi Language Center. The study adopts the Visual Data Mining (VDM) methodology, integrating interactive visualization with the K-Means clustering algorithm. This method allowed for the normalization and grouping of over 10,000 participant data points, with the optimal number of clusters determined by the Silhouette Score. The research results successfully established three distinct proficiency clusters, including a