<![CDATA[The placement process for internship participants (KP) at PT XYZ is currently still carried out manually by administrative staff through document file examination and subjective considerations. This approach has a high potential for inaccurate placements and requires a relatively long processing time.To address these operational challenges, this study designed an automatic matching decision support system using the Fuzzy String Matching algorithm through the RapidFuzz library. The system was developed using an iterative Prototype method and a qualitative approach. It processes a historical dataset consisting of 608 participant records through a structured data pipeline. The stages include text preprocessing (case folding to standardize capitalization, removal of unnecessary special characters, synonym normalization based on a standard competency dictionary), and calculation of text similarity scores using the token_sort_ratio or token_set_ratio functions.The assessment of suitability level applies a fixed composite weighting scheme: 70% for technical skills and 30% for participant interests. Functional testing and implementation results showed that 90.6% of all participant data successfully received position recommendations with “Suitable” or “Highly Suitable” status. This high percentage demonstrates that the decision support system—integrated with an HTML/CSS monitoring dashboard interface and Excel (.xlsx) report export feature—can automate the entire placement process more effectively, objectively, efficiently, and data-driven.]]>
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