dc.contributor.author |
Kurniawan, Rahmad |
|
dc.contributor.author |
Lestari, Fitra |
|
dc.contributor.author |
Mawardi |
|
dc.contributor.author |
Nurainun, Tengku |
|
dc.contributor.author |
Abdul Hamid, Abu Bakar |
|
dc.contributor.author |
Melia , Tisha |
|
dc.date.accessioned |
2024-01-03T22:43:09Z |
|
dc.date.available |
2024-01-03T22:43:09Z |
|
dc.date.issued |
2024-01-02 |
|
dc.identifier.issn |
2210-142X |
|
dc.identifier.uri |
https://journal.uob.edu.bh:443/handle/123456789/5280 |
|
dc.description.abstract |
Cowhide plays a significant role in Indonesia’s culinary and leather industries. It caters to the preferences of a predominantly
Muslim population that emphasizes halal products. Regulatory authorities must understand its characteristics comprehensively to
provide effective halal assurance to the diverse entities within Indonesia’s leather industry. Traditional statistical methods for assessing
halal compliance are inefficient due to the complexity and diversity of the leather industry’s supply chain. This study addresses these
challenges by employing unsupervised learning methods, specifically K-Means and Hierarchical clustering algorithms to analyze a
dataset comprising 100 Cowhide Small and Medium Enterprises (SMEs) located in Garut Regency, West Java Province. This dataset
includes 62 features that facilitate the clustering of these industries based on various halal risk factors. Experimental results indicate
that the optimal number of clusters is four. The K-Means algorithm outperforms the Hierarchical clustering algorithm with a higher
average silhouette score of 0.59 compared to 0.31. Furthermore, the K-Means algorithm demonstrates stability in clustering the data,
making it a robust choice for this analysis. These clustering outcomes offer valuable insights into the SMEs operational characteristics
and halal compliance risks, significantly enhancing the ability of regulatory authorities to implement effective halal assurance measures.
Consequently, this study provides a robust framework for improving halal certification processes and aiding risk management within
Indonesia’s leather industry. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
University of Bahrain |
en_US |
dc.subject |
K-means clustering, Hierarchical clustering, Cowhide, Leather Industries, SMEs, Halal |
en_US |
dc.title |
Halal Supply Chain Risk using Unsupervised Learning Methods for Clustering Leather Industries |
en_US |
dc.identifier.doi |
http://dx.doi.org/10.12785/ijcds/160165 |
|
dc.volume |
16 |
en_US |
dc.issue |
1 |
en_US |
dc.pagestart |
899 |
en_US |
dc.pageend |
910 |
en_US |
dc.contributor.authorcountry |
Indonesia |
en_US |
dc.contributor.authorcountry |
Indonesia |
en_US |
dc.contributor.authorcountry |
Indonesia |
en_US |
dc.contributor.authorcountry |
Indonesia |
en_US |
dc.contributor.authorcountry |
Malaysia |
en_US |
dc.contributor.authorcountry |
Indonesia |
|
dc.contributor.authoraffiliation |
Department of Computer Science, Universitas Riau |
en_US |
dc.contributor.authoraffiliation |
Department of Industrial Engineering, Universitas Islam Negeri Sultan Syarif Kasim Riau |
en_US |
dc.contributor.authoraffiliation |
Faculty of Sharia and Law, Universitas Islam Negeri Sultan Syarif Kasim Riau |
en_US |
dc.contributor.authoraffiliation |
Department of Industrial Engineering, Universitas Islam Negeri Sultan Syarif Kasim Riau |
en_US |
dc.contributor.authoraffiliation |
Putra Business School, Universiti Putra Malaysia |
en_US |
dc.contributor.authoraffiliation |
Department of Computer Science, Universitas Riau |
|
dc.source.title |
International Journal of Computing and Digital Systems |
en_US |
dc.abbreviatedsourcetitle |
IJCDS |
en_US |