Autocorrect on Drugs e-Dictionary Search Module Using Levenshtein Distance Algorithm

The Dictionary of Medicine in the form of a physical book has many drawbacks, one of them is its thickness makes it impractical to be carried. This becomes a motivation to develop drug dictionary applications in the form of a Drugs e-Dictionary. One of the developed Drugs e-Dictionary uses A-Z index-based approach to discover any drug terms. This approach is less effective and less efficient timewise. Therefore, it is necessary to add a search function that has an autocorrect feature to aid the user. The purpose of this study is to build a search module that has an autocorrect feature on Drugs e-Dictionary using the Levenshtein Distance algorithm. The methodology or the stages of this research divided into the construction of a search module on Drugs e-Dictionary, implementation of the Levenshtein Distance algorithm, and autocorrect validation test. The results of the algorithm implementation show that the search module with the autocorrect feature can detect typing errors in the inputted terms by producing the closest drug term output in the database, then automatically provide suggestions for improvement and display the results of the improved drug terms to the user. it reaches 90% accuracy of inputted query, with 90% precision and 90% recall.