Gradient Analysis In Implementation of B-Tree Indexing In Reporting Annual Tax Database

Abstract

A query is a syntax or command used in a database system to access and display data. Queries can be used to make data interact with each other. To display query results in the database, of course, requires execution time which is usually denoted in seconds. Execution time is directly proportional to the amount of data to be displayed and the level of complexity of the database. To speed up query execution time, the term database optimization is known. One of the database optimization methods is to use the b-tree indexing technique the database. This study aims to compare the execution time of databases that have not been indexed or that have been indexed with data objects in the MySQL database MPN-Info application at the Jakarta Palmerah Tax Office (KPP Pratama Jakarta Palmerah). This application was developed to supervise taxpayers which is used in almost all tax service offices throughout Indonesia. The data used is active employee status taxpayer data registered until 2020 with the reporting of Annual Personal Income Tax Returns in 2019 and 2020. The study uses an experimental method by applying a select query, then the execution time is recorded well for unindexed and indexed databases. The gradient method will be used for compare the results of the two. The results obtained are after taking the population of taxpayers registered employees up to year In 2020 at KPP Pratama Jakarta Palmerah, 31,238 data were compared with the annual reporting data, the average execution time before indexing was 255.585 seconds and the average time after indexing was 1.341 seconds and the gradient value before indexing was 0.0211 and after indexing was 0.0001. This proves that the indexing technique has a significant impact in accelerating the query execution process.