Implementation of Semantic Similarity for Book Search in Digital Library - Daffa, M. Royhan; Yaqin, M. Ainul (Faculty of Engineering, Universitas Brawijaya, 2025)
This study discusses improving book search relevance in digital libraries by implementing semantic similarity. This study aims to improve search relevance and contextual understanding by integrating the TF-IDF method. This process includes text preprocessing, TF-IDF weighting, and semantic similarity calculation using the Wu-Palmer method with the aid of WordNet. Testing was carried out with 25 different queries on 1000 digital books in five scenarios. The test results show: (1) The 3-term TF-IDF scenario produces the highest average similarity, ranging from 71.83% (1-word query) to 40.08% (5-word query or more), with a standard deviation increasing from 15.82% to 28.76%; (2) The 5-term TF-IDF scenario shows an average similarity from 61.36% to 35.69% and a standard deviation from 19.91% to 28.64%; (3) The 10-term TF-IDF scenario provides an average similarity of 53.6% to 31.77%, with a relatively stable standard deviation in the range of 27-28%; (4) The scenario without TF-IDF has the lowest average similarity, 42.14% to 25.6%, with a standard deviation decreasing from 30.31% to 27.13%; (5) The non-semantic scenario yields the highest average similarity of 80% (1-word query) and decreases to 38.25% for long queries, with no standard deviation due to the absence of score variation. This study proves that the combination of semantic similarity with the top 3 TF-IDF terms is an optimal approach in increasing the relevance of search results compared to other scenarios, especially for short to medium queries. These results contribute to developing a more effective and contextual digital library search system.