| Abstrak/Abstract |
This research develops a new approach to
query expansion by integrating Association Rules (AR)
and Ontology. In the proposed approach, there are
several steps to expand the query, namely (1) the
document retrieval step; (2) the step of query expansion
using AR; (3) the step of query expansion using Ontology.
In the initial step, the system retrieved the top documents
via the user's initial query. Next is the initial processing
step (stopword removal, POS Tagging, TF-IDF). Then do
a Frequent Itemset (FI) search from the list of terms
generated from the previous step using FP-Growth. The
association rules search by using the results of FI. The
output from the AR step expanded using Ontology. The
results of the expansion with Ontology use as new queries.
The dataset used is a collection of learning documents.
Ten queries used for the testing, the test results are
measured by three measuring devices, namely recall,
precision, and f-measure. Based on testing and analysis
results, integrating AR and Ontology can increase the
relevance of documents with the value of recall, precision,
and f-measure by 87.28, 79.07, and 82.85. |
