Integrasi Information Management Cycle dan Text Mining untuk Mentransformasikan Umpan Balik Kualitatif menjadi Pengetahuan Organisasi

Abstract

Service oriented organizations in the digital era must convert large volumes of unstructured data into actionable knowledge to improve service quality and decision-making. This study examines how integrating the Information Management (IM) Cycle with Machine Learning (ML), particularly text mining, can transform qualitative feedback into strategic insights. A computational qualitative approach was used, analyzing 881 open-ended survey responses from assessment center participants. Data were processed through IM stages acquisition, organization, analysis, and utilization and analyzed using Latent Dirichlet Allocation (LDA) to identify key themes. The results reveal three dominant themes: Appreciation and Expectations (61.6%), Technical and Application Constraints (23.0%), and Information and Implementation Coordination (15.3%). While overall perceptions are positive, significant challenges remain in system usability, platform reliability, and communication clarity. These findings show that qualitative data contain valuable explicit and hidden insights for service improvement. This study also translates thematic findings into practical managerial tools: Opportunities for Improvement (OFI) and Actions for Improvement (AFI), bridging analysis and implementation. Overall, integrating IM and text mining provides a systematic and replicable framework for converting qualitative feedback into actionable knowledge, supporting data-driven decisions and continuous service innovation.