Prediksi Employee Churn Dengan Uplift Modeling Menggunakan Algoritma Logistic Regression

Authors

  • Jovan Kinoto Universitas Prima Indonesia
  • Jansen Liharma Damanik Universitas Prima Indonesia
  • Erwin Tri Saputra Situmorang Universitas Prima Indonesia
  • Josua Siregar Universitas Prima Indonesia
  • Mawaddah Harahap Universitas Prima Indonesia

DOI:

https://doi.org/10.34012/jutikomp.v3i2.1645

Keywords:

Employee Churn, Uplift Modeling, Logistic Regression, Lai’s Generalized Weighted Uplift Method

Abstract

Pada sebuah perusahaan, karyawan merupakan aset yang berharga dan dapat menunjang kesuksesan perusahaan tersebut. Namun, hilangnya tenaga kerja dapat merugikan perusahaan. Kondisi ini disebut dengan Employee Churn. Salah satu solusi untuk mengatasi Employee Churn adalah dengan menerapkan model Uplift Modeling. Dalam penelitian ini, penulis menganalisa penerapan Logistic Regression terhadap Uplift Modeling dalam permasalahan Employee Churn. Data yang diteliti adalah data karyawan dari IBM HR Analytics. Hasil prediksi pada penelitian ini mendapat akurasi sebesar 64,40%, sedangkan hasil preskripsi menghasilkan hasil yang cukup baik apabila menerapkan waktu kerja tambahan pada karyawan. Berdasarkan hasil yang didapat, diketahui bahwa para karyawan justru cenderung bertahan di perusahaan apabila diberikan waktu kerja tambahan.

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Published

2020-10-01

How to Cite

Kinoto, J. ., Damanik, J. L., Situmorang, E. T. S., Siregar, J., & Harahap, M. (2020). Prediksi Employee Churn Dengan Uplift Modeling Menggunakan Algoritma Logistic Regression. JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP), 3(2), 503-508. https://doi.org/10.34012/jutikomp.v3i2.1645