Designing a word recommendation application using the Levenshtein Distance algorithm

Abstract

Good scriptwriting or reporting requires a high level of accuracy. The basic problem is that the level of accuracy of the authors is not the same. The low level of accuracy allows for mistyping of words in a sentence. Typing errors caused the word to become non-standard. Even worse, the word became meaningless. In this case, the recommendation application serves to provide word-writing recommendations in case of a typing error. This application can reduce the error rate of the writer when typing. One method to improve word spelling is Approximate String Matching. This method applies an approach to the string search process. The Levenshtein Distance algorithm is a part of the Approximate String-Matching method. This method, firstly, is necessary to go through the preprocessing stage to correct an incorrectly written word using the Levenshtein Distance algorithm. The application testing phase uses ten texts composed of 100 words, ten texts composed of 100 to 250 words, and ten texts composed of 250 to 500 words. The average accuracy rate of these test results was 95%, 94%, and 90%.