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.

Implementation of Levenshtein Distance Algorithm for Product Search Query Suggestions on Koro Pedang Edutourism E-Commerce

“…Figure 1 is the design of the search module. [23,24] In Figure 1, the user inputs a product query in the E-commerce search form Koro Pedang Educational Tourism. The query inputted by the user becomes pre-processing data where the number of words is symbolized by j and the initialization value is symbolized by n. Initialize values starting from n=1.…”

“…This algorithm is represented in the form of a matrix to calculate the number of differences in the string, entered by the user with the string contained in the database [18][19][20][21][22]. The final result of the algorithm value is in the lower right corner of the matrix [23][24][25]. The final result of the matrix calculation shows the number of operations that must be executed by the algorithm.…”

“…These types of operations are character addition operations, character substitution operations and character deletion operations. Research on the implementation of the Levenshtein Distance Algorithm has been implemented for query suggestions by Khalidah and Sadiah et al, where the algorithm is implemented in a dictionary search form [23][24][25]. This research purposes to apply the Levenshtein Distance Algorithm for product search query suggestions on Koro Pedang Educational Tourism ecommerce.…”

Word Search on the "SITUK" Application Using the Levenshtein Distance Algorithm

“…It is known that the insertion operation of the characters "i", "m", "p", and "e" in the source string is required, then the value given is by the insertion operation formula in equation ( 2 And so on until the following Levenshtein Distance calculation runs until all the values in the matrix are filled. The Levenshtein Distance is the value in the bottom-right of the matrix, and in the case of the source string "Simulate" and the target string "simple" it is at (8,6). After doing all the matrix calculations, it is known that the result of calculating the distance between the source string "Simulate" and the target string "simple" is 5 (five), as illustrated in the matrix Figure 3 (c).…”