Implementasi Algoritma K-Nearest Neighbor Pada Database Menggunakan Bahasa SQL - Shafira Margaretta; Issa Arwani; Dian Eka Ratnawati (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2020)
Increasing the amount of data stored in the DBMS, resulting in data mining processes less efficiently implemented through machine learning applications. In general, the data mining process is carried out by implementing a number of work steps starting from data cleaning, data integration, data selection, data transformation and then data mining. In this series of steps, there is a process whereby data stored in a database is taken then exported for analysis. Exporting data from a database allows data to be insecure. Implementation of data mining algorithms in the database, is one solution to overcome these problems. In addition, implementing a data mining process on a DBMS can guarantee data security. The Faculty of Computer Science, Universitas Brawijaya has used the DBMS to store and manage student data. The application of data mining processes to predict the success of studies in the academic data of the Faculty of Computer Science is done by replicating the database. In implementing the K-Nearest Neighbor algorithm in the database, the table design stage is carried out by mapping each algorithm using SQL operations. At the design stage, SQL operations are stored in stored procedures. The results of stored procedure execution were tested by comparing the results of the MySQL DBMS system and the results of the WEKA application, obtaining an accuracy of the classification results of 96,94% using a MySQL DBMS and 96,84% using a WEKA.