Halal Supply Chain Risk using Unsupervised Learning Methods for Clustering Leather Industries

Abstract

Cowhide plays a significant role in Indonesia's culinary, leather industries and caters to the preferences of a predominantly Muslim population that strongly emphasizes halal products. Regulatory authorities must comprehensively understand its characteristics to provide halal assurance to the diverse entities within Indonesia's leather industry effectively. This study employs unsupervised learning methods, specifically K-Means and Hierarchical clustering algorithms to analyze a dataset comprising 100 Cowhide The Small and Medium Enterprises (SMEs) Industries located in Garut Regency, West Java Province, Indonesia. This dataset encompasses 62 features that enable the clustering of cowhide industries based on halal risk factors. Experimental results indicate that the optimal number of clusters is m=4. The K-Means algorithm outperforms the Hierarchical clustering algorithm with a higher average silhouette score of 0.59 compared to 0.31 indicating its superior performance. Furthermore, the K-Means algorithm demonstrates exceptional stability in clustering the data, making it a robust choice for this analysis. The clustering outcomes of the Cowhide SMEs Industry provide valuable insights into the industry's characteristics, facilitating the efficient implementation of halal assurance measures. These findings hold substantial implications for the halal certification and assistance processes within the leather industry in Indonesia.