Menggunakan Stepwise Linear Regression Untuk Menentukan Faktor Yang Mempengaruhi Produktivitas Tenaga Kerja

Aswar Hanif

Abstract


Abstrak
Semakin lama masa kerja, semakin banyak pengalaman yang dimiliki seseorang atas pekerjaannya. Seorang yang memiliki tingkat kehadiran yang tinggi, dianggap sebagai pekerja yang baik. Kedua faktor ini membentuk asumsi bahwa masa kerja dan tingkat kehadiran, secara positif atau negatif, mempengaruhi produktivitas pekerja. Dikarenakan besarnya pengaruh produktivitas pekerja terhadap kesehatan sebuah perusahaan, kegiatan menganalisis produktivitas tenaga kerja perusahaan, seharusnya tidak didasarkan pada asumsi-asumsi, meskipun asumsi tersebut bisa diterima. Menggunakan Regresi Linier Berganda, sebuah model persamaan dihasilkan dari data-data mengenai tenaga kerja. Tapi, karena nilai Koefisien Determinasi yang dihasilkan kurang memuaskan, dilakukan analisis ulang terhadap data. Kali ini menggunakan Regresi Linier Stepwise. Analisis kedua ini dapat menghasilkan nilai Koefisien Determinasi yang lebih tinggi dari nilai sebelumnya, meskipun harus diterima bahwa nilai yang baru ini masih terlalu rendah. Meskipun begitu, beberapa fakta mengenai sistem kerja perusahaan dan latar belakang tenaga kerjanya, dapat dijadikan penjelasan mengenai hasil analisis yang telah dilakukan.

Kata kunci: produktivitas, regresi linier stepwise

Abstract
The longer the employment length, the more experience a person has on his or her job. A person who has a high attendance at work, is considered a good worker. These two factors form the assumption that both employment length and work attendance, influence laborer productivity, either positively or negatively. As Labor productivity holds a lot of weight in relation to a company’s health, conducting an analysis of a company's productivity, must not be based on assumptions, even though it’s an acceptable one. Using multiple linear regression, a model was generated from data about the workforce. But, because the Coefficient of Determination value was less than satisfactory, an analysis was performed again on the data, this time using Stepwise Regression. The second analysis managed to produce a higher Coefficient of Determination value than the previous one, but it must accepted that the value remains too low. Though a few facts about the company’s work system and labours history could provide some explanation on this result.

Keyword: productivity, stepwise linear regression

Keywords


productivity, stepwise linear regression

References


Afifi, A., & Clark, V. A. (1999). Computer-Aided Multivariate Analysis, Fourth Edition. New York: CRC Press.

Draper, N. R., & Smith, H. (1998). Applied Regression Analysis, Third Edition. Canada: John Wiley & Sons, Inc.

Field, A. (2013). Discovering Statistics Using IBM Spss Statistics. London: SAGE Publications Ltd.

Hocking, R. R. (2005). Methods and Applications of Linear Models: Regression and the Analysis of Variance. New Jersey: John Wiley & Sons.

Nurd, D. (2014, June 05). Uji Asumsi Klasik Regresi Linier. Retrieved from Statsdata: http://www.statsdata.my.id/2014/06/uji-asumsi-klasik-regresi-linier.html

Santoso, S. (2010). Statistik Parametrik: Konsep dan Aplikasi dengan SPSS. Jakarta: Elex Media Komputindo.

School of Geography, University of Leeds. (n.d.). Stepwise linear regression. Retrieved from School Of Geography: http://www.geog.leeds.ac.uk/courses/other/statistics/spss/stepwise/

Statistics Solutions. (n.d.). Autocorrelation. Retrieved from Statistics Solutions: http://www.statisticssolutions.com/autocorrelation/

Sumarsono, S. (2003). Ekonomi Manajemen Sumberdaya Manusia Dan Ketenagakerjaan. Yogyakarta: Graha Ilmu.

Taylor, J. J. (2013, April 22). Confusing Stats Terms Explained: Heteroscedasticity . Retrieved from Stats Make Me Cry: http://www.statsmakemecry.com/smmctheblog/confusing-stats-terms-explained-heteroscedasticity-heteroske.html

Watson, P. (2017, 02 07). What is the difference between hierarchical and stepwise regressions? Retrieved from MRC Cognition and Brain Sciences Unit: http://imaging.mrc-cbu.cam.ac.uk/statswiki/FAQ/hier

Weisberg, S. (2005). Applied Linear Regression. New Jersey: John Wiley & Sons, Inc.




DOI: https://doi.org/10.31294/ji.v5i1.2701

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Index by:

 
  
Published by Department of Research and Public Service (LPPM) Universitas Bina Sarana Informatika with supported Relawan Jurnal Indonesia

Jl. Kramat Raya No.98, Kwitang, Kec. Senen, Kota Jakarta Pusat, DKI Jakarta 10450
Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License