GARMENT EMPLOYEE PRODUCTIVITY PREDICTION USING RANDOM FOREST

  • Imanuel Balla (1) Sekolah Tinggi Manajemen Informatika dan Komputer Nusa Mandiri
  • Sri Rahayu (2*) Sekolah Tinggi Manajemen Informatika dan Komputer Nusa Mandiri
  • Jajang Jaya Purnama (3) Sekolah Tinggi Manajemen Informatika dan Komputer Nusa Mandiri

  • (*) Corresponding Author
Keywords: Employee Productivity, Garment, Random Forest

Abstract

Clothing also means clothing is needed by humans. Besides the need for clothing in terms of function, clothing sales or business is also very potent. About 75 million people worldwide are directly involved in textiles, clothing, and footwear. In this case, a common problem in this industry is that the actual productivity of apparel employees sometimes fails to reach the productivity targets set by the authorities to meet production targets on time, resulting in huge losses. Experiments were conducted using the random forest model, linear regression, and neural network by looking for the values ​​of the correlation coefficient, MAE, and RMSE.  This aims to predict the productivity of garment employees with data mining techniques that apply machine learning and look for the minimum MAE value. The results of testing the proposed algorithm on the garment worker productivity dataset obtained the smallest MAE, namely the random forest algorithm, namely 0.0787, linear regression 0.1081, and 0.1218 neural networks

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Published
2021-03-15
How to Cite
Balla, I., Rahayu, S., & Purnama, J. (2021). GARMENT EMPLOYEE PRODUCTIVITY PREDICTION USING RANDOM FOREST. Jurnal Techno Nusa Mandiri, 18(1), 49-54. https://doi.org/10.33480/techno.v18i1.2210
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