CFP last date
20 June 2024
Call for Paper
July Edition
IJCA solicits high quality original research papers for the upcoming July edition of the journal. The last date of research paper submission is 20 June 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

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
journal cover thumbnail

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 %

References
  1. Andrika P.D., RD Kusumanto, R. K., Dimas, MD., & Anisah, M. 2022. Aplikasi Penilaian Kinerja Pegawai dengan Metode Sosiometri Berbasis Artificial Intelegence. Journal Locus Penelitian Dan Pengabdian, 1(5), 348–360. https://doi.org/10.58344/locus.v1i5.90
  2. BPK, B.P. 2020. Pp_No46 tahun 2011(pasal.1). https://peraturan.bpk.go.id/Home/Details/135059/pp-no-21-tahun-2020
  3. Benhar, H. I.-A. 2020. Benhar, H., Idri, A., & Fernández-A J. L. 2020. Data preprocessing for heart disease classification: A systematic literature review. Computer Methods and Programs in Biomedicine, 195, 105635.
  4. Dahj, J. N. M., & Ogudo, K. A. 2023. Machine Learning-Based Imputation Approach with Dynamic Feature Extraction for Wireless RAN Performance Data Preprocessing. Symmetry, 15(6). https://doi.org/10.3390/sym15061161
  5. Endang ED. S. 2020. IMPLEMENTASI DATA MINING MENGGUNAKAN ALGORITME NAIVE BAYES CLASSIFIER DAN C4.5 UNTUK MEMPREDIKSI KELULUSAN MAHASISWA. TELEMATIKA, 56-67.
  6. Hidayatullah, A. S., Bachtiar, F. A., & Cholissodin, I. 2021 Penerapan Algoritme Nearest Centroid Neighbor Classifier Based on K Local Means Using Harmonic Mean Distance (LMKHNCN) Untuk Klasifikasi Hasil Kinerja Pegawai Negeri Sipil. Jurnal Teknologi Informasi Dan Ilmu Komputer, 8(6), 1287. https://doi.org/10.25126/jtiik.202183443
  7. 1Ilmaniati, A., & Putro, B. E. 2019. Analisis komponen utama faktor-faktor pendahulu (antecendents) berbagi pengetahuan pada usaha mikro, kecil, dan menengah (UMKM) di Indonesia. Jurnal Teknologi, 11(1), 67–78. https://jurnal.umj.ac.id/index.php/jurtek/article/view/2652
  8. Hapsari R.K dan Indriyani. 2022. implementasi algoritma smote sebagai Penyelesaian Imbalance Hight Dimensional Datasets.
  9. Hermawan, A., & Permana W.A. (2022). Implementasi Korelasi untuk Seleksi Fitur pada Klasifikasi Jamur Beracun Menggunakan Jaringan Syaraf Tiruan. 5. https://www.kaggle.com/uciml/mushroom-
  10. Joan D & VIncent .R. 2020. The Design of Rijndael: AES - The Advanced Encryption Standard. Springer Berlin Heidelberg: https://doi.org/10.1007/978-3-662-60769-5_6.
  11. Kripsiandita, Y., Arifianto, D., & A’yun, Q. 2021. Deteksi Gangguan Autis Pada Anak Menggunakan Metode Modified K-Nearst Neighbor. JUSTINDO (Jurnal Sistem Dan Teknologi Informasi Indonesia), 6(1), 31–37. https://doi.org/10.32528/justindo.v6i1.4357
  12. Lestari, P. I., Ratnawati, D. E., & Muflikhah, L. 2019. Implementasi Algoritme K-Means Clustering Dan Naive Bayes Classifier Untuk Klasifikasi Diagnosa Penyakit Pada Kucing. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer (J-PTIIK) Universitas Brawijaya, 3(1), 968– 973
  13. Oktara, P., Yulianti, L., & Fredricka, J. 2021. Analisis Kinerja Pegawai Menggunakan Algoritma K-Means Pada Dinas Pendidikan Dan Kebudayaan Kabupaten Bengkulu Tengah. Jurnal Media Infotama,
  14. Prasetyawan, D., & Gatra, R. 2022. Algoritma K-Nearest Neighbor untuk Memprediksi Prestasi Mahasiswa Berdasarkan Latar Belakang Pendidikan dan Ekonomi JISKA (Jurnal Informatika Sunan Kalijaga), 7(1), 56–67. https://doi.org/10.14421/jiska.2022.7.1.56-67
  15. Oktafiani, R., Hermawan, A., & Avianto, D. 2023. Pengaruh Komposisi Split data Terhadap Performa Klasifikasi Penyakit Kanker Payudara Menggunakan Algoritma Machine Learning. Jurnal Sains Dan Informatika, 19–28. https://doi.org/10.34128/jsi.v9i1.622
  16. Joshi, A. P., & Patel, B. V.2021. Data Preprocessing: The Techniques for Preparing Clean and Quality Data for Data Analytics Process. Oriental Journal of Computer Science and Technology, 13(0203), 78–81. https://doi.org/10.13005/ojcst13.0203.03
  17. Khakim, E. N. R., Hermawan, A., & Avianto, D. 2023. IMPLEMENTASI CORRELATION MATRIX PADA KLASIFIKASI DATASET WINE. JIKO (Jurnal Informatika Dan Komputer), 7(1), 158. https://doi.org/10.26798/jiko.v7i1.771
  18. Khan, S. I., & Hoque, A. S. M. L.2020. SICE: an improved missing data imputation technique. Journal of Big Data, 7(1). https://doi.org/10.1186/s40537-020-00313-w
  19. Muneer, A., Mohd Taib, S., Mohamed Fati, S., O. Balogun, A., & Abdul Aziz, I.2022 A Hybrid Deep Learning-Based Unsupervised Anomaly Detection in High Dimensional Data. Computers, Materials & Continua, 70(3), 5363–5381. https://doi.org/10.32604/cmc.2022.021113
  20. Nugroho, B., & Denih, A. 2020. Perbandingan kinerja metode pra-pemrosesan dalam pengklasifikasian otomatis dokumen paten. Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika, 17(2), 381-387.
  21. Nazabal, A., Williams, C. K. I., Colavizza, G., Smith, C. R., & Williams, A. 2020. Data Engineering for Data Analytics: A Classification of the Issues, and Case Studies. http://arxiv.org/abs/2004.12929
  22. Oğuz, M. B. 2020 Speech emotion recognition: Emotional models, databases, features, preprocessing methods, supporting modalities, and classifiers. https://doi.org/10.1016/j.specom.2019.12.001.
  23. Rahmawan, H. 2020. Penentuan Rekomendasi Pelatihan Pengembangan Diri Bagi Pegawai Negeri Sipil Menggunakan Algoritma C4.5 Dengan Principal Component Analysis Dan Diskritisasi. Jurnal Tekno Kompak, 14(1), 5. https://doi.org/10.33365/jtk.v14i1.531
  24. R Kurniawan, P Pizaini, F Insani. 2021. Penerapan Algoritma K-Means Clustering dan Correlation Matrix untuk Menganalisis Risiko Penyebaran Demam Berdarah di Kota Pekanbaru. ejurnal.unmerpas.ac.id, 1-6.
  25. Seto A,A., Dewi S,K., Agustianto, K., Wiryawan, G., & Jember, P. N. (n.d.). PENGARUH PREDIKSI MISSING VALUE PADA KLASIFIKASI DECISION TREE C4.5. https://doi.org/10.25126/jtiik.202294778
  26. Setianto, Y. A., Kusrini, K., & Henderi, H. 2019 Penerapan Algoritma K- Nearest Neighbour Dalam Menentukan Pembinaan Koperasi Kabupaten Kotawaringin Timur. Creative Information Technology Journal, 5(3), 232. https://doi.org/10.24076/citec.2018v5i3.179
  27. Shen Y, Elke H & Qiong B & Tom B & Geert. 2020. Towards better road safety management: Lessons learned from inter-national benchmarking. Accident Analysis & Prevention
  28. TuhinaBanerjee, P. R. 2021. Various dimension reduction techniques forhigh dimensional data analysis: areview. Artificial Intelligence Review.
  29. Vincenzo Moscato Giancarlo Sperlí Antonio Picariello. 2021 A benchmark of machine learning approaches for credit score prediction. Expert Systems with Applications.
  30. Wahyudi, M. 2022. Diagnosa Gejala Kecanduan Game Online Dengan Metode K-Nearest Neighbor. Seminar Nasional Informatika (Senatika), 6(3), 106– 117.17(2), 341139.
  31. Wets, Y. S. 2020. Towards better road safety management: Lessons learned from inter-national benchmarking. Accident Analysis & Prevention.
Index Terms

Computer Science
Information Sciences

Keywords

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

Powered by PhDFocusTM