PENGELOMPOKAN PROVINSI-PROVINSI DI INDONESIA MENGGUNAKAN METODE WARD (StudiKasus: Produksi Tanaman Pangan di Indonesia Tahun 2018)

Besya Salsabilla Azani Arif; Agus Rusgiyono; Abdul Hoyyi

doi:10.14710/j.gauss.9.1.112-121

DOI: https://doi.org/10.14710/j.gauss.9.1.112-121

PENGELOMPOKAN PROVINSI-PROVINSI DI INDONESIA MENGGUNAKAN METODE WARD (StudiKasus: Produksi Tanaman Pangan di Indonesia Tahun 2018)

*Besya Salsabilla Azani Arif - Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia

Agus Rusgiyono - Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia

Abdul Hoyyi - Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia

BibTex Citation Data :

@article{J.Gauss27528,
    author = {Besya Arif and Agus Rusgiyono and Abdul Hoyyi},
    title = {PENGELOMPOKAN PROVINSI-PROVINSI DI INDONESIA MENGGUNAKAN METODE WARD (StudiKasus: Produksi Tanaman Pangan di Indonesia Tahun 2018)},
    journal = {Jurnal  Gaussian},
  volume = {9},
    number = {1},
    year = {2020},
    keywords = {Food Crop; Cluster Analysis; Ward Method; Squared Euclid; Elbow Method},
    abstract = { Cluster analysis is a technique for grouping objects or observations into homogeneous groups. Cluster analysis is divided into two methods, namely hierarchy and non-hierarchy. The hierarchy method generally involves a series of n-1 decisions (n is the number of observations) that combine observations into a tree-like structure or dendogram. Hierarchy is divided into two methods, namely agglomerative (concentration) and splitting (distribution). For non-hierarchical methods, the number of clusters can be determined by the researcher. Ward method is a hierarchical cluster analysis method that can maximize homogeneity in the cluster. The   Sum-of-Square  (SSE) formula is used in this method to minimize variations in the clusters that are formed. In this research, squared euclid distance is used to measure the similarity between object pairs. The data used in this study are secondary data on food crop production, namely rice, corn, soybeans, peanuts, green beans, sweet potatoes, and cassava in Indonesia 2018. To determine the cluster, the elbow method is used to form optimal clusters using WSS formula. Based on the analysis results, it was found that the optimal cluster is four clusters. The first cluster consists of 9 Province, the second cluster consists of 20 Province, the third cluster consists of 1 Province, the fourth cluster consists of  2 Province, and the fifth cluster consists of 2 Province.   Keywords:  Food Crop, Cluster Analysis, Ward Method, Squared Euclid, Elbow Method },
   issn = {2339-2541},   pages = {112--121}  doi = {10.14710/j.gauss.9.1.112-121},
    url = {https://ejournal3.undip.ac.id/index.php/gaussian/article/view/27528}
}

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Abstract

Cluster analysis is a technique for grouping objects or observations into homogeneous groups. Cluster analysis is divided into two methods, namely hierarchy and non-hierarchy. The hierarchy method generally involves a series of n-1 decisions (n is the number of observations) that combine observations into a tree-like structure or dendogram. Hierarchy is divided into two methods, namely agglomerative (concentration) and splitting (distribution). For non-hierarchical methods, the number of clusters can be determined by the researcher. Ward method is a hierarchical cluster analysis method that can maximize homogeneity in the cluster. The Sum-of-Square (SSE) formula is used in this method to minimize variations in the clusters that are formed. In this research, squared euclid distance is used to measure the similarity between object pairs. The data used in this study are secondary data on food crop production, namely rice, corn, soybeans, peanuts, green beans, sweet potatoes, and cassava in Indonesia 2018. To determine the cluster, the elbow method is used to form optimal clusters using WSS formula. Based on the analysis results, it was found that the optimal cluster is four clusters. The first cluster consists of 9 Province, the second cluster consists of 20 Province, the third cluster consists of 1 Province, the fourth cluster consists of 2 Province, and the fifth cluster consists of 2 Province.

Keywords: Food Crop, Cluster Analysis, Ward Method, Squared Euclid, Elbow Method

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Keywords: Food Crop; Cluster Analysis; Ward Method; Squared Euclid; Elbow Method

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