*Besya Salsabilla Azani Arif  -  Departemen Statistika, FSM, Universitas Diponegoro, Indonesia
Agus Rusgiyono  -  Departemen Statistika, FSM, Universitas Diponegoro, Indonesia
Abdul Hoyyi  -  Departemen Statistika, FSM, Universitas Diponegoro, Indonesia
Received: 15 May 2020; Published: 15 May 2020.
Open Access Copyright 2020 Jurnal Gaussian
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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

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

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  1. Ghozali, I. 2011. Aplikasi Analisis Multivariate Dengan Program IBM SPSS 19. Semarang: Badan Penerbitan Universitas Diponegoro
  2. Gudono. 2014. Analisis Data Multivariat. Yogyakarta: BPFE.
  3. Gujarati, D. N. 1978. Basic Econometrics. New Jersey: The McGraw-Hill Companies
  4. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. 2010. Multivariate Data Analysis. New Jersey: Pearson Education, Inc.
  5. Johnson, R. A., & Wichern, D. W. 2003. Applied Multivariate Statistical Analysis. New Jersey: Pearson Education, Inc.
  6. [Kemenpan RI]. KementerianPertanianRepublik Indonesia.2019.
  7. Kodinariya, T. M., & Makwana, P. R. 2013. Review on determining number of Cluster in K-Means Clustering. International Journal of Advance Research in Computer Science and Management Studies
  8. Kumari, V. V., Raju, B. R., & Naik, A. 2012. Hybrid Clustering Algorithm based on Mahalanobis Distance and MST. International Journal of Applied Information Systems (IJAIS).
  9. Malik, A., & Tuckfield, B. 2019. Applied Unsupervised learning With R. In Uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA.
  10. Noorjenah, Subagya, E. H., Iswadi, Amalia, R. R., Siagian, S. H., Poerwaningsih, R., Anggraeny, R. 2014. Produksi Tanaman Pangan 2014. Jakarta: Badan Pusat Statistik
  11. Rencher, A. C. 2004. Methods of Multivariate Analysis. Canada: John Wiley & Sons
  12. Supranto, J. 2018. Analisis multivariat : Arti dan Interpretasi. Jakarta: PT Rineka Cipta.
  13. Widarjono, A. 2010. Analisis Statistika Multivariat Terapan. Yogyakarta: UPP STIM YKP