Analisis SDM dan Pembelajaran Mesin untuk Prediksi Perputaran Karyawan di Startup: Tinjauan Literatur Sistematis
DOI:
https://doi.org/10.38035/jmpis.v7i2.7877Keywords:
Prediksi Perputaran Karyawan, Pembelajaran Mesin, Analisis SDM, Retensi Startup, Hutan Acak, Model PrediktifAbstract
Perputaran karyawan tetap menjadi tantangan kritis bagi startup, di mana retensi talenta secara langsung memengaruhi kelangsungan dan pertumbuhan organisasi. Tingkat perputaran karyawan yang tinggi menimbulkan biaya finansial yang signifikan, diperkirakan mencapai 50-200% dari gaji tahunan, dan mengganggu kelangsungan organisasi, terutama merugikan dalam lingkungan startup yang terbatas sumber dayanya. Tinjauan literatur sistematis ini mengkaji penerapan analitik SDM dan pendekatan machine learning dalam memprediksi perputaran karyawan, dengan penekanan khusus pada konteks startup. Mengikuti pedoman PRISMA, kami melakukan pencarian komprehensif di enam basis data akademik utama (IEEE Xplore, ACM Digital Library, ScienceDirect, Springer, Emerald Insight, dan arXiv), menganalisis 39 studi yang telah direview oleh rekan sejawat yang diterbitkan antara tahun 2021-2025. Temuan kami menunjukkan bahwa metode ensembel, khususnya algoritma Random Forest dan Gradient Boosting, secara konsisten mencapai akurasi prediksi 88-99% di berbagai konteks organisasi. Fitur prediktif utama meliputi kepuasan kerja (skor penting 0.87), kompensasi relatif terhadap standar pasar (0.79), peluang pengembangan karier (0.74), keseimbangan kerja-kehidupan (0.68), dan untuk startup secara khusus, persepsi keamanan kerja (0.54). Tinjauan ini mensintesis kerangka kerja implementasi, mengidentifikasi praktik terbaik metodologis termasuk strategi implementasi bertahap, dan mengusulkan model adaptif tujuh tahap yang sesuai untuk lingkungan startup dengan sumber daya terbatas. Hasil menunjukkan bahwa model yang dapat diinterpretasikan dikombinasikan dengan rekayasa fitur strategis memungkinkan startup untuk menerapkan intervensi retensi proaktif sambil mempertahankan standar etika dalam pengambilan keputusan algoritmik. Tinjauan ini memberikan panduan praktis bagi praktisi startup dan mengidentifikasi celah penelitian kritis yang memerlukan penyelidikan lebih lanjut di masa depan.
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