MULTIVARIATE ANALYSIS OF COMMODITY AVAILABILITY OF STAPLE FOODS USING COMPLETE LINKAGE HIERARCHICAL CLUSTERING METHOD

  • Arjon Sitio (1) STMIK Pelita Nusantara
  • Anita Sindar Sinaga (2*) STMIK Pelita Nusantara
  • Akhyar Haikal (3) STMIK Pelita Nusantara
  • Sumitra Dewi (4) STMIK Pelita Nusantara

  • (*) Corresponding Author
Keywords: : Multivariate Analysis, Complete Linkage, Hierarchical Clustering, Food Commodities

Abstract

The government directly supervises 11 basic food commodities. The system of interplay between the price of goods and the availability of staple food directly has an impact on the high price of food at certain times. It is necessary to classify the food that is most needed by the community on big holidays in Indonesia so that it can be a reference for the government in preparing market needs in the coming year. In this study, the grouping of staple food availability was based on hierarchical cluster analysis with complete linkage method. The availability of food commodities in the discussion of this research is sourced from production materials and daily prices for meat, eggs, cooking oil and rice commodities. Cluster interpretation results in cluster 1 indicating Fulfilled Availability of 88-89%, Cluster 2 showing Sufficient Commodity Availability of 90-93% and Cluster 3 showing Availability of Rare Commodities of 87%. The three clusters formed are depicted in the form of a dendogram as a visualization of the relationship between food availability groupings.

Downloads

Download data is not yet available.

References

A. S. Sinaga and A. S. Sitio, “Big Data Analysis of Covid-19 Spread Based on Distribution Map and Protocol Regulations with Business Intelligence,” vol. 15, no. 1, pp. 106–114, 2022.

T. Märzinger, J. Kotík, and C. Pfeifer, “Application of hierarchical agglomerative clustering (Hac) for systemic classification of pop-up housing (puh) environments,” Appl. Sci., vol. 11, no. 23, 2021, DOI: 10.3390/app112311122.

M. Roux, “A Comparative Study of Divisive and Agglomerative Hierarchical Clustering Algorithms,” J. Classif., vol. 35, no. 2, pp. 345–366, 2018, DOI: 10.1007/s00357-018-9259-9.

S. Patel, S. Sihmar, and A. Jatain, “A study of hierarchical clustering algorithms,” 2015 Int. Conf. Comput. Sustain. Glob. Dev. INDIACom 2015, vol. 3, no. 10, pp. 537–541, 2015.

M. N. Aziz and T. Ahmad, “Cluster analysis-based approach features selection on machine learning for detecting intrusion,” Int. J. Intell. Eng. Syst., vol. 12, no. 4, pp. 233–243, 2019, doi: 10.22266/ijies2019.0831.22.

H. Nguyen, X. N. Bui, Q. H. Tran, and N. L. Mai, “Corrigendum to ‘A new soft computing model for estimating and controlling blast-produced ground vibration based on hierarchical K-means clustering and cubist algorithms’ [Appl. Soft Comput. 77 (2019) 376–386] (Applied Soft Computing Journal (2019) 77 (376,” Appl. Soft Comput., vol. 100, p. 107123, 2021, doi: 10.1016/j.asoc.2021.107123.

A. Achmad and R. Fernandes, “Comparison of Cluster and Linkage Validity Indices in Integrated Cluster Analysis with Structural Equation Modeling War-PLS Approach no. February, 2021.

S. Aggarwal, P. Phoghat, and S. Maitrey, “Hierarchical Clustering- An Efficient Technique of Data mining for Handling Voluminous Data,” Int. J. Comput. Appl., vol. 129, no. 13, pp. 31–36, 2015, doi: 10.5120/ijca2015907081.

M. Nedyalkova and V. Simeonov, “Multivariate chemometrics as a strategy to predict the allergenic nature of food proteins,” Symmetry (Basel)., vol. 12, no. 10, pp. 1–19, 2020, doi: 10.3390/sym12101616.

C. Etumnu and A. W. Gray, “A Clustering Approach to Understanding Farmers’ Success Strategies,” J. Agric. Appl. Econ., vol. 52, no. 3, pp. 335–351, 2020, doi: 10.1017/aae.2020.4.

P. Yildirim and D. Birant, “K-Linkage: A new agglomerative approach for hierarchical clustering,” Adv. Electr. Comput. Eng., vol. 17, no. 4, pp. 77–88, 2017, doi: 10.4316/AECE.2017.04010.

Vijaya, S. Aayushi, and R. Bateja, “A Review on Hierarchical Clustering Algorithms,” Journal of Engineering and Applied Sciences, vol. 12, no. 24. pp. 7501–7507, 2017.

E. A. Leal Piedrahita, “Hierarchical Clustering for Anomalous Traffic Conditions Detection in Power Substations,” Cienc. e Ing. Neogranadina, vol. 30, no. 1, pp. 75–88, 2019, doi: 10.18359/rcin.4236.

N. Apfel and X. Liang, “Agglomerative Hierarchical Clustering for Selecting Valid Instrumental Variables,” 2021, [Online]. Available: http://arxiv.org/abs/2101.05774.

G. Brandi and T. Di Matteo, “Higher-Order Hierarchical Spectral Clustering for Multidimensional Data,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 12746 LNCS, pp. 387–400, 2021, doi: 10.1007/978-3-030-77977-1_31.

A. M. Jarman, “Hierarchical Cluster Analysis : Comparison of Single linkage, Complete linkage, Average linkage and Centroid Linkage Method,” no. 2, 2020, doi: 10.13140/RG.2.2.11388.90240.

Published
2022-02-18
How to Cite
[1]
A. Sitio, A. Sinaga, A. Haikal, and S. Dewi, “MULTIVARIATE ANALYSIS OF COMMODITY AVAILABILITY OF STAPLE FOODS USING COMPLETE LINKAGE HIERARCHICAL CLUSTERING METHOD”, jitk, vol. 7, no. 2, pp. 61-66, Feb. 2022.
Article Metrics

Abstract viewed = 54 times
PDF downloaded = 47 times