Classification of Labor Using Support Vector Machine in North Sumatera
DOI:
https://doi.org/10.17605/jti.v4i2.658Abstract
Labor markets in Indonesia are key challenges and policy issues. Balai Besar Pengembangan Latihan Kerja (BBPLK) Medan is a services unit to develop and implementation of labor to increase skill and knowledge. The classification of labor in North Sumatera is very interesting to evaluate the performance of the labor in North Sumatera. In this case, we compute the labor data to classify and evaluate the model and performance of the dataset. The computation of the dataset using the support vector machine (SVM) as a model in machine learning or probabilistic approach by training and test data. The data was collected from Badan Pusat Statistik (BPS) Sumatera Utara for 2018 samples. Labor force dataset in North Sumatera had been computed and shown the result, indicates the support vector machine classifier is the good algorithm for this classification problem, offering good values in terms of accuracy, for describe the labor force in North Sumatera and can be recommended to BBPLK to add more development and implementation.References
Asian Development Bank, “Analysis of Trends and Challenges in the Indonesian Labor Market,†Asian Dev. Bank Pap. Indones., no. 16, pp. 1–38, 2016.
H. S. Hasibuan, “Indonesia The Development and Labor Situation in Indonesia,†vol. 1, no. 3, pp. 33–40, 2017.
G. Sugiyarto, M. Oey-Gardiner, and N. Triaswati, “Labor markets in Indonesia: Key challenges and policy issues,†Labor Mark. Asia Issues Perspect., no. November 2005, pp. 301–366, 2006.
M. A. Hasoloan, “The Indonesia Labor Market,†pp. 1–13, 1395.
R. Berwick, “An Idiot’s guide to Support vector machines (SVMs) R. Berwick, Village Idiot SVMs: A New Generation of Learning Algorithms,†p. 28, 2015.
D. K. Srivastava and L. Bhambhu, “Data classification using support vector machine,†J. Theor. Appl. Inf. Technol., vol. 12, no. 1, pp. 1–7, 2010.
A. Ira, A. Simbolon, and M. Pujiastuti, “Machine Learning for Handoffs Classification Based on Effective Communication History,†vol. 3, no. 2, pp. 265–267, 2019.
“(Tutorial) Support Vector Machines (SVM) in Scikit-learn (article) - DataCamp.†[Online]. Available: https://www.datacamp.com/community/tutorials/svm-classification-scikit-learn-python. [Accessed: 18-Feb-2020].
“Understanding Support Vector Machines(SVM) algorithm (along with code).†[Online]. Available: https://www.analyticsvidhya.com/blog/2017/09/understaing-support-vector-machine-example-code/. [Accessed: 18-Feb-2020].
