Query Suggestion on Drugs e-Dictionary Using the Levenshtein Distance Algorithm

Abstract

Dictionary of medicine in the form of a thick book has many disadvantages, one of which is impractical. This is the reason for Indonesian developers to create drugs e-Dictionary. But the drugs e-Dictionary that has been developed is still in the form of a letter index so that users must search the terms one by one in sequential order. This has become so inefficient and ineffective that it is necessary to add a search function and query suggestion feature to the drug e-dictionary. The purpose of this study is to build a query suggestion facility on drugs e-Dictionary using the Levenshtein Distance algorithm. The stages of this research consist of the Development of web-based drugs e-Dictionary, Implementation of the Levenshtein Distance Algorithm, Query Suggestion Testing, and Usage. Based on the results of the implementation of the Levenshtein Distance algorithm and test results, Drugs e-Dictionary can evaluate words that are not in the database. The query suggestion function works by producing the closest word output contained in the database.