Dwi Amelia, Gusmi Kholijah

Analisis Cluster Pengelompokan Provinsi di Indonesia Berdasarkan Sub Sektor Nilai Tukar Petani

Introduction

Analisis cluster pengelompokan provinsi di indonesia berdasarkan sub sektor nilai tukar petani . Analisis cluster digunakan untuk mengelompokkan provinsi di Indonesia berdasarkan sub sektor Nilai Tukar Petani (NTP). Temukan bagaimana metode average linkage menjadi yang terbaik untuk klasifikasi ini.

Abstract

Semakin tinggi nilai tukar petani, relatif semakin kuat pula tingkat kemampuan atau daya belipetani. Sub Sektor pertanian yang mencakup dalam pengolahan nilai tukar petani meliputi lima sub sektor, antara lain sub sektor padi dan palawija, holtikultura, tanaman perkebunan rakyat, peternakan, dan perikanan. Dalam rangka perkembangan ekonomi diperlukan adanya pengelompokkan indeks nilai tukar petani dari setiap sektor nilai tukar petani sehingga dilakukanlah pengelompokkan nilai tukar petani dengan menggunakan analisis cluster. Analisis cluster merupakan suatu metode dalam analisis peubah ganda yang bertujuan untuk mengelompokkan n satuan pengamatan kedalam k cluster dengan (k < n) berdasarkan p variabel, sehingga unit-unit pengamatan dalam satu kelompok memiliki ciri-ciri yang lebih homogen dibandingkan unit pengamatan dalam kelompok lain. Pengelompokan yang dilakukan pada 3 metode cluster yang digunakan yaitu single linkage, complete linkage dan average linkage diperoleh metode yang paling baik untuk digunakan dalam pengklasifikasian data nilai tukar petani tersebut yaitu metode average linkage karena memiliki nilai korelasi yang tertinggi.

Review

The paper "Analisis Cluster Pengelompokan Provinsi di Indonesia Berdasarkan Sub Sektor Nilai Tukar Petani" tackles a critically important subject in agricultural economics and regional development within Indonesia. By focusing on the Farmer's Terms of Trade (NTP) across five key sub-sectors—padi and palawija, horticulture, smallholder plantations, livestock, and fisheries—the study aims to provide a granular understanding of agricultural performance. The authors correctly emphasize the direct correlation between a higher NTP and stronger farmer purchasing power, positioning this research as a valuable contribution to informing strategies for economic development through targeted provincial groupings. Methodologically, the study employs cluster analysis, a well-established multivariate technique for categorizing observations into distinct, homogeneous groups. The abstract competently explains the core principle of cluster analysis, which is to group 'n' observations into 'k' clusters based on 'p' variables such that within-group similarity is maximized and between-group dissimilarity is evident. A particularly commendable aspect is the comparison of three different linkage methods—single linkage, complete linkage, and average linkage—to determine the most suitable approach for the dataset. This systematic evaluation ensures a more robust and defensible clustering outcome. The key finding regarding methodology, that the average linkage method was identified as the most effective due to its highest correlation value, provides strong justification for the chosen analytical path. While the abstract does not detail the characteristics or implications of the actual clusters formed, the successful application and validation of the clustering method suggest that the study offers a valuable framework for understanding regional disparities. The ability to group provinces based on their NTP sub-sector profiles holds significant potential for policymakers to develop more effective, region-specific agricultural policies and interventions aimed at enhancing farmer welfare and promoting balanced economic growth across Indonesia.

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