<![CDATA[This study presents a comparative evaluation of Information Retrieval (IR) models on the Indonesian Wikipedia corpus, focusing on sparse, dense, and hybrid retrieval approaches. The evaluated methods include TF-IDF and BM25 as sparse models, SBERT (MiniLM) as a dense retrieval model, and hybrid retrieval implemented through score fusion. The dataset consists of 713,044 Wikipedia articles, with experiments conducted using 1,000 test queries. Performance is measured using Precision@10 (P@10) and Mean Reciprocal Rank (MRR). The results show that BM25 achieves the highest performance, with a P@10 of 0.973 and an MRR of 0.9174, significantly outperforming TF-IDF and SBERT. Hybrid retrieval provides a slight performance improvement, where the BM25 + SBERT combination reaches a P@10 of 0.979 and an MRR of 0.9253 at higher α values. These findings indicate that lexical matching remains dominant in encyclopedic corpora, while semantic representations provide complementary improvements. However, the performance gain of hybrid retrieval is relatively marginal compared to the additional computational cost introduced by dense embedding and score fusion processes, indicating a trade-off between effectiveness and efficiency. These results highlight that, for low-resource languages such as Indonesian, lexical-based retrieval remains highly reliable, while hybrid approaches provide incremental improvements. Therefore, this study provides practical guidelines for developing efficient, scalable, and reliable Information Retrieval systems for Indonesian Wikipedia and other low-resource language corpora.]]>
