Mapping Indonesian Provinces Based on Leading Plantation Commodities with Export Potential Using Multidimensional Scaling Analysis

Indonesia, as an agrarian country, sees its plantation subsector significantly contribute to the national economy. However, the processing of plantation products in Indonesia remains dominated by raw or semi-finished goods, leading to low added value and limited income for both farmers and the nation. This study aims to map Indonesia's provinces based on the production of key plantation commodities with high export potential using the Multidimensional Scaling (MDS) analysis method. This applied research focuses on commodities such as pepper, palm oil, coconut, rubber, coffee, cocoa, clove, and tea. The study seeks to group 34 Indonesian provinces based on similarities in plantation production, providing valuable insights for policymakers to enhance production and export value. The analysis employs inter-provincial similarities to determine distances between objects and evaluates the accuracy of MDS mapping through STRESS and values. The findings reveal that 12 provinces share similarities in cocoa production, while 7 provinces are closely aligned in the production of pepper, rubber, and coffee. Additionally, 5 provinces show similarities in palm oil production, and 9 provinces exhibit commonalities in coconut, clove, and tea production. The analysis achieved a stress value of 0.024 (2.4%) and an value of 0,9994, indicating the MDS mapping is highly reliable. However, the results do not fully align with field data, suggesting the need for orthogonal transformation through Principal Component Analysis (PCA) for improved accuracy.

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Publication Details

Journal
UNP Journal of Statistics and Data Science
Published
2024-11-28
DOI
https://doi.org/10.24036/ujsds/vol2-iss4/327
Primary Topic
Global Trade and Competitiveness
Type
article
Field-Weighted Citation Impact
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article

Mapping Indonesian Provinces Based on Leading Plantation Commodities with Export Potential Using Multidimensional Scaling Analysis

Tessy Octavia Mukhti, Yenni Kurniawati, Dina Fitria, Dicha Putri Yeni
UNP Journal of Statistics and Data Science
Global Trade and Competitiveness
article

Mapping Indonesian Provinces Based on Leading Plantation Commodities with Export Potential Using Multidimensional Scaling Analysis

Tessy Octavia Mukhti, Yenni Kurniawati, Dina Fitria, Dicha Putri Yeni
article en

Abstract

Indonesia, as an agrarian country, sees its plantation subsector significantly contribute to the national economy. However, the processing of plantation products in Indonesia remains dominated by raw or semi-finished goods, leading to low added value and limited income for both farmers and the nation. This study aims to map Indonesia's provinces based on the production of key plantation commodities with high export potential using the Multidimensional Scaling (MDS) analysis method. This applied research focuses on commodities such as pepper, palm oil, coconut, rubber, coffee, cocoa, clove, and tea. The study seeks to group 34 Indonesian provinces based on similarities in plantation production, providing valuable insights for policymakers to enhance production and export value. The analysis employs inter-provincial similarities to determine distances between objects and evaluates the accuracy of MDS mapping through STRESS and values. The findings reveal that 12 provinces share similarities in cocoa production, while 7 provinces are closely aligned in the production of pepper, rubber, and coffee. Additionally, 5 provinces show similarities in palm oil production, and 9 provinces exhibit commonalities in coconut, clove, and tea production. The analysis achieved a stress value of 0.024 (2.4%) and an value of 0,9994, indicating the MDS mapping is highly reliable. However, the results do not fully align with field data, suggesting the need for orthogonal transformation through Principal Component Analysis (PCA) for improved accuracy.

UNP Journal of Statistics and Data ScienceVol. 2(4)
State University of Padang (ID)
Openalex Percentile: Top 62%
Global Trade and Competitiveness
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