PENDEKATAN ALGORITHMA SVM UNTUK MENENTUKAN KENAIKAN GAJI - Widi Winjani; Muhamad Fatchan (Program Studi Informatika Universitas Teknologi Sumbawa, 2022)
Employee performance appraisals are sometimes not in accordance with the results of their performance. Employee benefits are often equated, or sometimes benefits are only given to employees without applying a strong calculation. This is due to the employee appraisal monitoring system that is used still in a personal way without using an appraisal system and the data collected is not optimal and inaccurate. Based on these problems, the researchers conducted their research by applying data mining techniques using the Support Vector Machine algorithm to obtain accurate results that can be used as an additional reference in employee performance appraisal decisions to determine whether or not they are eligible for a salary increase. This research also uses the Orange support application for testing the accuracy of the system created. The data used is 396 data taken from permanent employee performance appraisal data for the period January 2020 - December 2020. The data is then analyzed using the Orange support application. The test results produce a Classification Accuracy level that reaches 0.972, Area Under Curve (AUC) 0.944, F1 results 0.972, Recall results 0.972, Precision results 0.973 can be categorized as Excellent Classification because the accuracy is between 0.90 – 1.00.