OPTIMALISASI PREDIKSI KEHILANGAN KARYAWAN MENGGUNAKAN TEKNIK RFE, SMOTE, DAN ADABOOST

Prambudi Setiyadi
Muhamad Nur Prayogi
Achmad Solichin


DOI: https://doi.org/10.29100/jipi.v9i4.5642

Abstract


Kehilangan karyawan menjadi isu vital dalam dinamika organisasi karena dampaknya yang signifikan terhadap produktivitas dan stabilitas tenaga kerja. Penelitian ini menerapkan teknik machine learning untuk mengantisipasi pergantian karyawan dengan menggabungkan seleksi fitur, oversampling, dan algoritma ensemble. Empat pendekatan yang dibandingkan adalah RFE-SMOTE-ADABOOST, RFE-ADABOOST, SMOTE-ADABOOST, dan SMOTE-ADABOOST dengan Hyperparameter. Evaluasi dilakukan menggunakan metrik akurasi, presisi, recall, dan F1-score. Hasil menunjukkan bahwa SMOTE-ADABOOST dengan Hyperparameter mencapai kinerja tertinggi, dengan akurasi 0,907, presisi 0,912, recall 0,898, dan F1-score 0,905. Model ini mengidentifikasi 10 faktor kunci yang mempengaruhi prediksi pergantian karyawan, seperti Education Field, Business Travel, dan Monthly Income. Kesimpulannya, model SMOTE-ADABOOST dengan Hyperparameter terbukti paling efektif dalam memprediksi kehilangan karyawan. Implikasi dari hasil evaluasi ini menunjukkan bahwa organisasi dapat secara proaktif mengidentifikasi dan mengelola faktor-faktor kunci yang mempengaruhi retensi karyawan, sehingga meningkatkan stabilitas tenaga kerja dan produktivitas keseluruhan.

Keywords


Kehilangan karyawan, RFE, SMOTE, Adaboost

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J. Park, Y. Feng, and S. P. Jeong, ‘Developing an advanced prediction model for new employee turnover intention utilizing machine learning techniques’, Sci Rep, vol. 14, no. 1, May 2024, doi: 10.1038/s41598-023-50593-4.

N. Leo, B. Tennisson, K. P. Prasad, E. P. Kumar, K. N. Kumar, and U. G. Scholar, ‘ANALYSIS AND PREDICTION OF EMPLOYEE ATTRITION’, International Journal of Creative Research Thoughts, vol. 11. p. 467, 2023. [Online]. Available: www.ijcrt.org

V. Musanga and C. Chibaya, ‘A Predictive Model to Forecast Employee Churn for HR Analytics’. pp. 17–30, 2023.

M. Pratt, M. Boudhane, and S. Cakula, ‘Employee attrition estimation using random forest algorithm’, Baltic Journal of Modern Computing, vol. 9, no. 1, pp. 49–66, 2021, doi: 10.22364/BJMC.2021.9.1.04.

U. Students, ‘EMPLOYEE ATTRITION PREDICTION USING STACKING AND ITS EVALUATION’, International Research Journal of Engineering and Technology, 2021, [Online]. Available: www.irjet.net

N. Bandyopadhyay and A. Jadhav, ‘Churn Prediction of Employees Using Machine Learning Techniques’, Tehnicki Glasnik, vol. 15, no. 1, pp. 51–59, 2021, doi: 10.31803/tg-20210204181812.

Z. Li and E. Fox, ‘Prediction and optimization of employee turnover intentions in enterprises based on unbalanced data’, PLoS One, vol. 18, no. 8 AUGUST, May 2023, doi: 10.1371/journal.pone.0290086.

M. Lazzari, J. M. Alvarez, and S. Ruggieri, ‘Predicting and explaining employee turnover intention’, Int J Data Sci Anal, vol. 14, no. 3, pp. 279–292, 2022, doi: 10.1007/s41060-022-00329-w.

A. Raza, K. Munir, M. Almutairi, F. Younas, and M. M. S. Fareed, ‘Predicting Employee Attrition Using Machine Learning Approaches’, Applied Sciences (Switzerland), vol. 12, no. 13, May 2022, doi: 10.3390/app12136424.

K. Naz, I. F. Siddiqui, J. Koo, M. A. Khan, and N. M. F. Qureshi, ‘Predictive Modeling of Employee Churn Analysis for IoT-Enabled Software Industry’, Applied Sciences, vol. 12, no. 20, 2022, doi: 10.3390/app122010495.

F. K. Alsheref, I. E. Fattoh, and W. Mead, ‘Automated Prediction of Employee Attrition Using Ensemble Model Based on Machine Learning Algorithms’, Comput Intell Neurosci, vol. 2022, 2022, doi: 10.1155/2022/7728668.

S. F. Sari and K. M. Lhaksmana, ‘Employee Attrition Prediction Using Feature Selection with Information Gain and Random Forest Classification’, Journal of Computer System and Informatics (JoSYC), vol. 3, no. 4, pp. 410–419, May 2022, doi: 10.47065/josyc.v3i4.2099.

M. Chaudhary, L. Gaur, N. Jhanjhi, M. Masud, and S. Aljahdali, ‘Envisaging Employee Churn Using MCDM and Machine Learning’, Intelligent Automation and Soft Computing, vol. Vol.33, p. pp.1009-1024, May 2022, doi: 10.32604/iasc.2022.023417.

F. H. Wardhani and K. M. Lhaksmana, ‘Predicting Employee Attrition Using Logistic Regression With Feature Selection’, Sinkron : jurnal dan penelitian teknik informatika, vol. 7, no. 4, pp. 2214–2222, May 2022, doi: 10.33395/sinkron.v7i4.11783.

P. R. Srivastava and P. Eachempati, ‘Intelligent Employee Retention System for Attrition Rate Analysis and Churn Prediction: An Ensemble Machine Learning and Multi- Criteria Decision-Making Approach’, Journal of Global Information Management, vol. 29, no. 6, May 2021, doi: 10.4018/JGIM.20211101.oa23.

M. Subhashini and R. Gopinath, ‘Employee Attrition Prediction in Industry Using Machine Learning Techniques’, International Journal of Advanced Research in Engineering and Technology, vol. 11, no. 12, pp. 3329–3341, 2020, doi: 10.17605/OSF.IO/9XDWE.

J. Kinoto, J. L. Damanik, E. T. S. Situmorang, J. Siregar, and M. Harahap, ‘Prediksi Employee Churn Dengan Uplift Modeling Menggunakan Algoritma Logistic Regression’, JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP), vol. 3, no. 2, pp. 503–508, May 2020, doi: 10.34012/jutikomp.v3i2.1645.

T. Chengsheng, L. Huacheng, and X. Bing, ‘AdaBoost typical Algorithm and its application research’, MATEC Web of Conferences, vol. 139, p. 222, May 2017, doi: 10.1051/matecconf/201713900222.

A. Priyatno and T. Widiyaningtyas, ‘A SYSTEMATIC LITERATURE REVIEW: RECURSIVE FEATURE ELIMINATION ALGORITHMS’, JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer), vol. 9, no. 2, pp. 196–207, May 2024, doi: 10.33480/jitk.v9i2.5015.

N. V Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, ‘SMOTE: Synthetic Minority Over-sampling Technique’, Journal of Artificial Intelligence Research, vol. 16, pp. 321–357, May 2002, doi: 10.1613/jair.953.