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Correlation Analysis on Classification of Government Employee Performance Results with Employment Agreements using Algorithms K-Nearest Neighbour (Case Study: Kebumen Regency Government)

by Siti Rokhanah, Arief Hermawan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 17
Year of Publication: 2024
Authors: Siti Rokhanah, Arief Hermawan
10.5120/ijca2024923560

Siti Rokhanah, Arief Hermawan . Correlation Analysis on Classification of Government Employee Performance Results with Employment Agreements using Algorithms K-Nearest Neighbour (Case Study: Kebumen Regency Government). International Journal of Computer Applications. 186, 17 ( Apr 2024), 32-40. DOI=10.5120/ijca2024923560

@article{ 10.5120/ijca2024923560,
author = { Siti Rokhanah, Arief Hermawan },
title = { Correlation Analysis on Classification of Government Employee Performance Results with Employment Agreements using Algorithms K-Nearest Neighbour (Case Study: Kebumen Regency Government) },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2024 },
volume = { 186 },
number = { 17 },
month = { Apr },
year = { 2024 },
issn = { 0975-8887 },
pages = { 32-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number17/correlation-analysis-on-classification-of-government-employee-performance-results-with-employment-agreements-using-algorithms-k-nearest-neighbour-case-study-kebumen-regency-government/ },
doi = { 10.5120/ijca2024923560 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-04-27T03:06:53.411070+05:30
%A Siti Rokhanah
%A Arief Hermawan
%T Correlation Analysis on Classification of Government Employee Performance Results with Employment Agreements using Algorithms K-Nearest Neighbour (Case Study: Kebumen Regency Government)
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 17
%P 32-40
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This research focuses on the performance appraisal of employees in government agencies and how correlations are in data processing to improve model accuracy. By focusing on data preparation, handling missing data, imbalanced data and feature selection. The purpose of the study is to provide an understanding of the interaction between these methods in the context of performance result analysis. This study includes four experimental scenarios that consider a combination of data preprocessing methods. Each scenario is designed to evaluate the performance of the k-nearest neighbour algorithm on the dataset of performance results of Government Employees with Work Agreements in the Kebumen District Government. The method steps include data preparation, handling missing data and feature selection based on the Correlation Matrix to overcome High Dimensional Data and the K-Nearest Neighbour method to display and produce the final results of processing data. The test results show that using a combination of data pre-processing methods can significantly increase the accuracy of the K-Nearest Neighbor model on the performance results dataset for Government Employees with Performance Agreements. The highest accuracy was obtained in the pre-processing scenario when applying correlation with the Correlation Matrix technique and K-Nearest Neighbor classification by eliminating attributes that had a high correlation value, namely 100 %

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Index Terms

Computer Science
Information Sciences

Keywords

Employee Performance; Classification; K-Nearest Neighbour; Correlation Matrix

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