Digital Archive Classification Performance Analysis Using a Decision Tree Based on TF-IDF Features

The management of digital archives at PT LNS Indonesia faces inefficiencies due to the absence of an automatic classification system, forcing employees to manually inspect file names. This leads to slow document retrieval processes and a high risk of misfiled archives. The aim of this study is to design and implement a digital archive classification system based on the Decision Tree algorithm to automate archive management. The research method follows the Knowledge Discovery in Databases (KDD) framework, starting from the selection of file names from four divisions Human Resources (HR), Finance, Information Technology (IT), and Engineering followed by text preprocessing and feature extraction using TF-IDF with a combination of word n-grams and character n-grams. The Decision Tree model is developed and evaluated using 5- Fold Cross-Validation. Experimental results show that the model achieves 91% accuracy, macro-precision of 0.92, macro-recall of 0.90, and a macro F1-score of 0.91. In conclusion, the implemented system successfully automates the grouping of thousands of files into folders according to division and document type, thereby significantly improving efficiency and accuracy in digital archive management within the company.