Clustering of Indonesian Manufacturing Production Dynamics Based on Robust Temporal Features
Keywords:
K-Means, Manufacturing index, Robust scaling, Temporal feature, Unsupervised learning.Abstract
This article develops a robust temporal feature-based clustering framework to group the monthly dynamics of Indonesia's manufacturing production index. The data used is Production Volume: Economic Activity: Manufacturing for Indonesia from the OECD through FRED, in the form of a seasonally adjusted index 2015=100 for the period January 2010-March 2023. The methods developed incorporate winsorization, robust scaling, level feature formation, monthly growth, annual growth, rolling volatility, deviation from local trends, momentum, cluster number validation, and K-Means. The empirical results showed that the three clusters gave the highest Silhouette value of 0.402 and resulted in clear industry interpretations, namely contraction/pressure, normal-stable, and expansion/acceleration. The contraction cluster only accounts for 6.8% of observations but has an average monthly growth of -9.25%, while the normal-stable cluster dominates 74.15% of observations. These findings suggest that clustering can serve as a rapid diagnostic instrument for production cycle monitoring, pressure detection, and industrial operational policy formulation.
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