Perbandingan Kinerja MetodeTF-IDF dan BM25 pada Pencarian Artikel Berita Bahasa Indonesia

Abstract

The growth of online media has increased the number of Indonesian-language news articles and created a need for effective and efficient retrieval systems. This study compares Term Frequency–Inverse Document Frequency (TF-IDF) and Best Matching 25 (BM25) using the Indonesia News Dataset (2025). Of 80,472 articles, 80,463 documents were retained after removing nine articles with empty content. The evaluation used 10 multi-category queries. Ground truth was constructed by pooling the top 20 results from each method, producing 304 query–document pairs manually assessed by one evaluator using relevance labels from 0 to 2. Retrieval effectiveness was measured using Precision@k, Recall@k, Mean Average Precision (MAP), Mean Reciprocal Rank (MRR), and normalized Discounted Cumulative Gain (nDCG). Retrieval time was measured over 30 repetitions after three warm-up runs. At Top-10, BM25 achieved a Precision@10 of 0.79, Recall@10 of 0.351630, MAP@10 of 0.728635, and nDCG@10 of 0.630976, which were higher than the corresponding TF-IDF scores. TF-IDF obtained a slightly higher MRR of 0.90 than BM25 at 0.85. In the evaluated implementation, BM25 required 0.010191 seconds on average, whereas TF-IDF required 0.066014 seconds. Descriptively, BM25 performed better on most Top-10 metrics and retrieval time, but the results depended on query characteristics and experimental configuration.