Hybrid KMeans–HDBSCAN clustering of Construction Cost Index dynamics for adaptive infrastructure planning in Indonesia

Abstract

Previous studies on regional construction cost estimation in Indonesia have predominantly relied on uniform national indices or conventional econometric models, which often fail to capture spatial heterogeneity and evolving temporal trends. This study introduces a data-driven hybrid clustering framework that integrates KMeans and HDBSCAN to classify Indonesian districts and cities using Construction Cost Index (CCI) dynamics from 2014 to 2024, with economic plausibility assessed using Gross Regional Domestic Product (GRDP). Empirically, KMeans yields three interpretable macro-clusters, but produces a highly imbalanced partition in which the dominant cluster contains 474 districts (over 90% of observations), indicating limited sensitivity to localized irregularities. In contrast, HDBSCAN identifies 35 micro-clusters and flags 103 districts as noise, explicitly isolating anomalous trajectories that centroid-based clustering may absorb. Cluster validity is supported by statistically significant between-cluster differences in CCI (one-way ANOVA on 2020 CCI: KMeans F = 1696.49, p = 2.35 × 10⁻²²⁶; HDBSCAN F = 370.91, p = 9.22 × 10⁻²⁶⁷, excluding noise). The temporal profiles reveal heterogeneous cost regimes, including persistently high-cost zones, transitional regions exhibiting structural correction, and stable low-cost areas. By exposing these hidden disparities and providing an anomaly-sensitive zoning structure, the proposed framework supports differentiated budgeting, targeted subsidies, and adaptive infrastructure planning for geographically diverse settings such as Indonesia.