Employee Attrition Prediction Using Feature Selection with Information Gain and Random Forest Classification
- Sindi Fatika Sari Telkom University, Bandung, Indonesia
-
Kemas Muslim Lhaksmana
*
Telkom University, Bandung,
Indonesia
(*) Corresponding Author
Abstract
Employee attrition is the loss of employees in a company caused by several factors, namely employees resigning, retiring, or other factors. Employee attrition of employees can have a negative impact on a company if it is not handled properly, including decreased productivity. The company also requires more time and effort to recruit and train new employees to fill vacant positions. This attrition prediction aims to help the human resources (HR) department in the company to find out what factors influence the occurrence of employee attrition. This research implements Random Forest while comparing Information Gain, Select K Best, and Recursive Feature Elimination feature selection methods to find which feature selection produces the best performance. The implementation of the aforementioned methods outperforms previous research in terms of accuracy, precision, recall, and f1 scores. In preparing this research, the first author collects data sets, makes programs, and compiles journals. The second author assists the first author in programming and preparing the journal. From the results of the tests that have been carried out, Information Gain produces the highest accuracy value of 89.2%, while Select K Best produces an accuracy value of 87.8% and Recursive Feature Elimination produces an accuracy value of 88.8%.
Downloads
References
H. Zhang, L. Xu, X. Cheng, K. Chao dan X. Zhao, “Analysis and Prediction of Employee Turnover Characteristics based on Machine Learning,” 2018 18th International Symposium on Communications and Information Technologies (ISCIT), pp. 371-376, 2018.
M. T. Bodie, M. A. Cherry, M. L. Mccormick dan J. Tang, “The Law and Policy of People Analytics,” 2016.
C. Stephanie dan R. Sarno, “ Classification Talent of Employee Using C4.5,,” 2019 International Conference on Information and Communications Technology (ICOIACT), pp. 388-393, 2019.
F. Fallucchi, M. Coladangelo, R. Giuliano dan E. W. De Luca, “Predicting employee attrition using machine learning techniques,” Predicting employee attrition using machine learning techniques, vol. 9, no. 4, pp. 1-17, 2020.
P. “IBM HR Analytics Employee Attrition & Performance,” Kaggle, 2017. [Online]. Available: https://www.kaggle.com/datasets/pavansubhasht/ibm-hr-analytics-attrition-dataset. [Diakses 2022].
J. L. Speiser, M. E. Miller, J. Tooze dan E. Ip, “A comparison of random forest variable selection methods for classification prediction modeling,” vol. 134, pp. 93-101, 2019.
A. Qutub, A. Al-Mehmadi, M. Al-Hssan dan R. Aljohani, “Prediction of Employee Attrition Using Machine Learning and Ensemble Methods,” International Journal of Machine Learning and Computing, vol. 11, pp. 110-114, 03 2021.
D. S. Sisodia, S. Vishwakarma dan A. Pujahari, “Evaluation of Machine Learning Models for Employee Churn Prediction,” Proceedings of the International Conference on Inventive Computing and Informatics (ICICI 2017), pp. 1016-1020, 2017.
A. Primajaya dan B. N. Sari, “Random Forest Algorithm for Prediction of Precipitation,” pp. 27-31, 2018.
A. Qutub, A. Al-Mehmadi, M. Al-Hssan, R. Aljohani dan H. S. Alghamdi, “Prediction of Employee Attrition Using Machine Learning and Ensemble Methods,” International Journal of Machine Learning and Computing, vol. 11, no. 2, pp. 110-114, 2021.
M. Muntasir Nishat, F. Faisal, I. Jahan Ratul, A. Al-Monsur, A. M. Abdullah, S. M. Nasrullah, M. T. Reza dan M. R. H. Khan, “A Comprehensive Investigation of the Performances of Different Machine Learning Classifiers with SMOTE-ENN Oversampling Technique and Hyperparameter Optimization for Imbalanced Heart Failure Dataset,” Scientific Programming, vol. 2022, 2022.
A. E. Irsad, “SELEKSI FITUR INFORMATION GAIN UNTUK KLASIFIKASI INFORMASI TEMPAT TINGGAL DI KOTA MALANG BERDASARKAN TWEET MENGGUNAKAN METODE NAIVE BAYES DAN PEMBOBOTAN TF-IDF-CF,” 2019.
S. H. A. Aini, Y. A. Sari dan A. Arwan, “Seleksi Fitur Information Gain untuk Klasifikasi Penyakit Jantung Menggunakan Kombinasi Metode K-Nearest Neighbor dan Naïve Bayes,” pp. 2546-2554, 2018.
T. Desyani, A. Saifudin dan Y. Yulianti, “Feature Selection Based on Naive Bayes for Caesarean Section Prediction,” IOP Conference Series: Materials Science and Engineering, 2020.
M. S. Wibawa, K. Dwi dan P. Novianti, “Reduksi Fitur Untuk Optimalisasi Klasifikasi Tumor,” Konferensi Nasional Sistem & Informatika 2017, pp. 73-78, 2017.
R. D. L. P, C. Fatichah dan D. Purwitasari, “Deteksi Gempa Berdasarkan Data Twitter Menggunakan Decision Tree, Random Forest, dan SVM,” Jurnal Teknik ITS, vol. 6, pp. 153-158, 2018.
D. G. I. Desantha dan K. M. Lhaksmana, “Aplikasi Sistem Seleksi Pelamar Kerja dengan menggunakan Metode,” eProceedings of Engineering, vol. 7, pp. 9731-9738, 2020.
R. M. Rifqi P, “KLASIFIKASI KEPRIBADIAN PADA SELEKSI PELAMAR KERJA DI PT. TELKOM INDONESIA MENGGUNAKAN PENDEKATAN SUPERVISED MACHINE LEARNING,” 2020.
N. Hadianto, H. B. Novitasari dan A. Rahmawati, “KLASIFIKASI PEMINJAMAN NASABAH BANK MENGGUNAKAN METODE NEURAL NETWORK,” Jurnal Pilar Nusa Mandiri, vol. 15, no. 2, pp. 163-70, 2019.
A. A. Rahayuningsih dan R. Maulana, “Analisis Perbandingan Algoritma Klasifikasi Data Mining,” Analisis Perbandingan Algoritma Klasifikasi Data Mining, vol. VI, no. 1, 2018.
T. K. Amory, A. dan W. Astuti, Jurnal Tugas Akhir Fakultas Informatika, 2021.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Employee Attrition Prediction Using Feature Selection with Information Gain and Random Forest Classification
Pages: 410-419
Copyright (c) 2022 Sindi Fatika Sari, Kemas Muslim Lhaksmana
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
