PENGGEROMBOLAN SUBSEKTOR INDUSTRI BERDASARKAN PERKEMBANGAN INDEKS PRODUKSI MENGGUNAKAN PREDICTION-BASED CLUSTERING
DOI:
https://doi.org/10.29244/ijsa.v4i3.585Keywords:
clustering, clustering, industry, industry, prediction-based, prediction-based, TSclust, TSclustAbstract
Statistics Indonesia (BPS) noted that there has been a decrease in the contribution of the industrial sector to the national GDP even though it had provided a significant multiplier effect on national economic growth. Therefore, it is necessary to cluster the industrial subsector based on its growth patterns so that the optimization of development results can be achieved. Prediction-based clustering is part of time series clustering (TSclust) which aims to form clusters based on prediction characteristics so that it can be used to choose a cluster that will become a mainstay industry in the future. This study focused on applying prediction-based clustering in the large and medium industrial sub-sector for a prediction period of 1 month, 1 quarter, and 1 semester. The data used in this study was the production index data from January 2010 to December 2018. The results showed that the best cluster for 1 month consisted of 5 groups, for 1 quarter consisted of 4 groups and for 1 semester consisted of 2 groups. Thus, it was concluded that the food industry; leather industry, leather goods, and footwear; and the pharmaceutical industry, chemical drug products, and traditional medicine could be chosen to be the mainstay industry in the future.
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References
Alonso, A. M., Berrendero, J. R., Hernández, A., & Justel, A. (2006). Time series clustering based on forecast densities. Computational Statistics & Data Analysis, 51(2): 762–776.
Hardle, W. (1994). Applied Nonparametric Regression. New York (US): Cambridge University Press.
Liao, T. W. (2005). Clustering of time series data—a survey. Pattern Recognition, 38(11): 1857–1874.
Mattjik, A. A., & Sumertajaya, I. M. (2011). Sidik Peubah Ganda Dengan Menggunakan SAS. Bogor (ID): IPB Press.
Montero, P., & Vilar, J. A. (2014). TSclust: An R Package for Time Series Clustering. Journal of Statistical Software, 62(1): 1–43.
Sheather, S. J., & Jones, M. C. (1991). A Reliable Data-Based Bandwidth Selection Method for Kernel Density Estimation. Journal of the Royal Statistical Society, 53(3): 683–690.
Sukarsa, I. K. G., & Srinadi, I. G. A. M. (2012). ESTIMATOR KERNEL DALAM MODEL REGRESI NONPARAMETRIK. Jurnal Matematika, 2(1): 19–30.
Vilar, J. A., Alonso, A. M., & Vilar, J. M. (2010). Non-linear time series clustering based on non-parametric forecast densities. Computational Statistics & Data Analysis, 54(11): 2850–2865.
