A Machine Learning Approach to Analyze and Reduce Features to a Significant Number for Employee’s Turn Over Prediction Model

Alam, Mirza Mohtashim; Mohiuddin, Karishma; Islam, Md. Kabirul; Hassan, Mehedi; Hoque, Md. Arshad-Ul; Allayear, Shaikh Muhammad

doi:10.1007/978-3-030-01177-2_11

Cited by 7 publications

(4 citation statements)

References 1 publication

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“…The articles (Alam, Mohiuddin, Islam, Hassan, Hoque, & Allayear, 2018) and (Karande, Shyamala, Hu, Tiwari, Mishra, & Trivedi, 2019) are examples where the goal is to understand the main reasons for employee turnover using techniques such as decision trees, logistic regression, and other algorithms. When the focus is more on the reasons and not in the prediction itself, the techniques have a better fit if the importance of the variables shown.…”

Section: Turnovermentioning

confidence: 99%

“…When the focus is more on the reasons and not in the prediction itself, the techniques have a better fit if the importance of the variables shown. One study Alam et al (2018) outlines the data volume problem using a public HR database from Kaggle, a community of databases and data science studies, competitions and hints in algorithms applications (https://www.kaggle.com) with 15,000 registers of employees, of which 3,572 have left a company.…”

Section: Turnovermentioning

confidence: 99%

“…Of the analyzed studies on employee turnover, some use the Kaggle's mentioned database Alam et al (2018), Islam et al (2018) and Sisodia et al (2017), while others also use big databases (Cahyani & Budiharto, 2017) and (Rombaut & Guerry, 2018), one with 50,000 records and another with 13,485. However, other studies (Dsouza, 2019), (Nandialath, David, Das, & Mohan, 2018), (Aswale & Mukul, 2020), (Rombaut & Guerry, 2020) and (Jain, Makkar, Jindal, & Gupta, 2021) use databases with fewer than 2,000 records from internal questionnaires applied in the companies.…”

Section: Turnovermentioning

confidence: 99%

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Analytical Approaches in Human Resources – a Systematic Review

Soares

Alcazar

Ludeña

2022

JISTEM

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Surveys related to analytics in the area of human resources (HR) have increased in the last 10 years. They usually suggest frameworks, tools, and concepts. Although there is much useful information, there is still a lack of materials consolidating real case studies or quantitative experiments with HR data. This systematic review analyzes 42 papers with analytical experiments in terms of three different segments of HR: recruitment, talent management, and turnover. The goal is to offer an updated perspective of what is being applied in HR regarding the problems that can be solved with data analysis, the most used techniques, and what could be explored to promote more scientific research on data-oriented projects in HR. Some of the results include talent management as the segment with the most related papers and the use of companies' internal data as predominant in the studies.

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Section: Turnovermentioning

confidence: 99%

Section: Turnovermentioning

confidence: 99%

Section: Turnovermentioning

confidence: 99%

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Analytical Approaches in Human Resources – a Systematic Review

Soares

Alcazar

Ludeña

2022

JISTEM

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“…Alam et al, telah mengusulkan model berdasarkan pendekatan pembelajaran mesin itu memberikan wawasan tentang perputaran karyawan perusahaan mana pun dengan mencari tahu yang utama faktor-faktor itu[2]. Hasil penelitian sebelumnya yang masih berhubungan adalah penelitian yang dilakukan oleh Ahmed Hosny Ghazi, dkk[3] yang bertujuan untuk membuat model prediksi turnover karyawan dengan menggunakan teknik data mining.…”

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Prediksi Guru Kemungkinan Tetap Bekerja di Sekolah Al Uswah Surabaya Menggunakan Machine Learning

Edris Effendi,

Yuadi,

Puspitasari

2023

jidt

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Penelitian ini bertujuan untuk memprediksi potensi tetap bekerjanya guru di Sekolah Al Uswah Surabaya, sehingga dapat meminimalisir tingkat turnover. Peneliti telah menggunakan algoritma klasifikasi untuk mengetahui algoritma yang paling cocok dalam memprediksi turnover guru dan diproses dengan aplikasi orange data mining. Berdasarkan tabel hasil prediksi maupun confusion matrix, menghasilkan rekomendasi bahwa algoritma yang paling bagus performanya adalah Logistic Regression. Tingkat Presicion untuk perbandingan data training dan testing 80:20 mencapai 80,8%, lebih tinggi dibanding tiga algoritma lainnya yang di bawah 80%. Melalui penelitian ini dapat memperjelas bahwa untuk studi kasus prediksi turnover karyawan, dapat menggunakan parameter Presicion. Melalui hasil penelitian dapat membantu lembaga pendidikan dalam merekrut guru yang memiliki peluang bisa bertahan lebih lama dengan memanfaatkan beberapa atribut standar dari biodatanya.

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