GARMENT EMPLOYEE PRODUCTIVITY PREDICTION USING 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
References
Afani, Utari Nur. dan Solovida, G. T. (2019). LINGKUNGAN ( Studi Kasus UMKM Rumah Pemotongan Hewan di Semarang ). Jurnal Sustainable Competitive Advantage, 9(51), 51–59.
Asohi, Y., & Andri, A. (2020). Impelementasi Algoritma Regresi Linier Berganda Untuk Prediksi Penjualan. Jurnal Nasional Ilmu Komputer, 1(3), 149–158. https://doi.org/10.47747/jurnalnik.v1i3.161
Balla, I., Rahayu, S., & Purnama, J. J. (2021). Prediksi Produktivitas Karyawan Garmen Menggunakan Random Forest. Jurnal TECHNO Nusa Mandiri, 1, 1–6.
Chaerani, N. (2018). Peran International Labour Organization Terhadap Peningkatan Lingkungan Kerja Di Sektor Industri Garmen Di Bangladesh. Universitas Hasanuddin, 151(2), 10–17.
Doni Efriza, I. I. (2018). Produktivitas kerja karyawan perbankan di kota medan. Jurnal BIS-A, 05(02), 49–53. http://ejurnal.plm.ac.id/index.php/BIS-A/article/view/164/145
Gunawan, A., Wahdan, M., & van den Herik, H. J. (2010). Increasing the managerial capabilities in Indonesian garment manufacturing. International Journal of Economic Policy in Emerging Economies, 3(4), 346–367. https://doi.org/10.1504/IJEPEE.2010.037582
Hamja, A., Maalouf, M., & Hasle, P. (2019). The effect of lean on occupational health and safety and productivity in the garment industry–a literature review. Production and Manufacturing Research, 7(1), 316–334. https://doi.org/10.1080/21693277.2019.1620652
Imran, A. Al. (2020). Productivity Prediction of Garment Employees Data Set. UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/Productivity+Prediction+of+Garment+Employees
Imran, A. Al, Amin, M. N., Islam Rifat, M. R., & Mehreen, S. (2019). Deep neural network approach for predicting the productivity of garment employees. 2019 6th International Conference on Control, Decision and Information Technologies, CoDIT 2019, 1402–1407. https://doi.org/10.1109/CoDIT.2019.8820486
Li, J., Tian, Y., Zhu, Y., Zhou, T., Li, J., Ding, K., & Li, J. (2020). A multicenter random forest model for effective prognosis prediction in collaborative clinical research network. Artificial Intelligence in Medicine, 103(September 2019), 101814. https://doi.org/10.1016/j.artmed.2020.101814
M Saiful Islam, Rakib, M. A., & Adnan, A. (2019). Ready-Made Garments Sector of Bangladesh: Its Growth, Contribution, and Challenges. Economics World, 7(1). https://doi.org/10.17265/2328-7144/2019.01.004
Mubarok, N. (2017). Strategi Pemasaran Islami Dalam Meningkatkan Penjualan Pada Butik Calista. I-Economics, 3(1), 73–92.
Sri, D., & Margareta, C. (2020). Pengaruh Pelatihan Kewirausahaan , Kemampuan Memanfaatkan Teknologi Dan Pendidikan Terhadap Produktifitas Wanita. Economic and Education Journal, 42, 142–158.
United, F. (2021). Global fashion industry statistics - International apparel. Https://Fashionunited.Com/. https://fashionunited.com/global-fashion-industry-statistics/
Winanda, L. (2010). Estimasi Produktivitas Pekerja Konstruksi Dengan Probabilistic Neural Network. Spectra, 8(15), 40–50.
Zhou, G., Moayedi, H., Bahiraei, M., & Lyu, Z. (2020). Employing artificial bee colony and particle swarm techniques for optimizing a neural network in prediction of heating and cooling loads of residential buildings. Journal of Cleaner Production, 254, 120082. https://doi.org/10.1016/j.jclepro.2020.120082
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