Comparison of Hierarchical Clustering, K-Means, K-Medoids, and Fuzzy C-Means Methods in Grouping Provinces in Indonesia according to the Special Index for Handling Stunting

Perbandingan Metode Hierarchical Clustering, K-Means, K-Medoids, dan Fuzzy C-Means dalam Pengelompokan Provinsi di Indonesia Menurut Indeks Khusus Penanganan Stunting

Authors

  • Ghina Rofifa Suraya Politeknik Statistika STIS, Indonesia
  • Arie Wahyu Wijayanto Politeknik Statistika STIS, Indonesia

DOI:

https://doi.org/10.29244/ijsa.v6i2p180-201

Keywords:

agglomerative hierarchical, fuzzy c-means, k-medoids, k-means, stunting

Abstract

Stunting has been widely known as the highest case of malnutrition suffered by toddlers in the world and has a bad impact on children's future. In 2018, Indonesia was ranked the 31st highest stunting in the world and ranked 4th in Southeast Asia. About 30.8% (roughly 3 out of 10) of children under 5 years suffer from stunting in Indonesia. To support the government policy making in handling stunting, it is undoubtedly necessary to classify the levels of stunting handling in regions in Indonesia. In this work, the hierarchical agglomerative and non-hierarchical clustering is compared and evaluated to perform clustering on stunting data. The agglomerative hierarchical cluster uses Single Linkage, Average Linkage, Complete Linkage, and Ward Method, while the non-hierarchical cluster uses K-Means, K-Medoids (PAM) Clustering, and Fuzzy C-Means. This study uses data from 12 IKPS indicators in 34 provinces in Indonesia in 2018. Based on the results of the evaluation using the Connectivity Coefficient, Dunn Index, Silhouette Coefficient, Davies Bouldin Index, Xie & Beni Index, and Calinski-Harabasz Index, the results show that the Average Linkage is the best cluster method with the optimal number of clusters is four clusters. The first cluster is a cluster with a good level of stunting management which consists of 28 provinces. The second cluster consists of only one province, DI Yogyakarta with a very good level of stunting handling. The third cluster consists of four provinces with poor stunting handling rates. Finally, the last cluster consisting of one province, Papua, has a very poor level of stunting handling.

Downloads

Download data is not yet available.

References

Baarsch, J., & Celebi, M. E. (2012). Investigation of Internal Validity Measures for K-Means Clustering.

Badan Ketahanan Pangan Kementerian Pertanian. (2020). Statistik Ketahanan Pangan 2019. Jakarta: Kementerian Pertanian.

Bappenas. (2017). Rencana Pembangunan Jangka Menengah Nasional (RPJMN) 2015-2019. Jakarta: Bappenas.

Bappenas. (2019). Rancangan Teknokratik : Rencana Pembangunan Jangka Menengah Nasional 2020-2024. Jakarta: Bappenas.

Bappenas. (2020). Pedoman Teknis Penyusunan Rencana Aksi Tujuan Pembangunan Berkelanjutan (TPB)/ Sustainable Development Goals (SDGs). Jakarta: Bappenas.

BPS. (2018). Statistik Kesejahteraan Rakyat. Jakarta: BPS.

BPS. (2019). Statistik Kesejahteraan Rakyat. Jakarta: BPS.

BPS. (2020). Profil Kesehatan Ibu dan Anak . Jakarta: BPS.

Brock, G., Pihur, V., Datta, S., & Datta, S. (2008, March). clValid: An R Package for Cluster Validation. Journal of Statistical Software, 25(4), 1–22.

Chowdhury, T. R., Chakrabarty, S., Rakib, M., Afrin, S., Saltmarsh, S., & Winn, S. (2020, September). Factors associated with stunting and wasting in children under 2 years in Bangladesh. Heliyon, 6(9), e04849.

Davies, D. L., & Bouldin, D. W. (1979). A Cluster Separation Measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 224-227.

Desgraupes, B. (2017). Clustering Indices. 1-34.

Grekousis, G., & Thomas, H. (2012). Comparison of two fuzzy algorithms in geodemographic segmentation analysis: The Fuzzy C-Means and GustafsoneKessel methods. Applied Geography, 125-136.

Han, J., & Kamber, M. (2006). Data Mining: Concepts and Techniques, Second Edition. San Francisco: Elsevier, Inc.

Johnson, R. A., & Wichern, D. W. (2007). Applied Multivariate Statistical Analysis 6th Edition. United States of America: Pearson Education, Inc.

Kaufman, L., & Rousseeuw, P. J. (1990). Finding Groups in Data : An Introduction to Cluster Analysis. United States: John Wiley & Sons.

Kemenkes. (2018). Situasi Balita Pendek (Stunting) di Indonesia. Jakarta: Kementerian Kesehatan.

Kemenkes. (2019). Laporan Nasional Riskesdas 2018. Jakarta: Badan Penelitian dan Pengembangan Kesehatan.

KPPPA dan BPS. (2019). Profil Anak Indonesia . Jakarta: KPPPA.

Luthfi, E., & Wijayanto, A. W. (2021). Analisis perbandingan metode hirearchical, k-means, dan k-medoids clustering dalam pengelompokan indeks pembangunan manusia Indonesia. Inovasi, 761-773.

Oot, L., Sethuraman, K., Ross, J., & Sommerfelt, A. E. (2016, February). The Effect of Chronic Malnutrition (Stunting) on Learning Ability, a Measure of Human Capital: A Model in PROFILES for Country-Level Advocacy. Retrieved 11 26, 2021, from Food and Nutrition Technical Assistance III Project: https://www.fantaproject.org/sites/default/files/resources/PROFILES-brief-stunting-learning-Feb2016.pdf

Pramana, S., Yuniarto, B., Mariyah, S., Santoso, I., & Nooraeni, R. (2018). Data Mining dengan R : Konsep serta Implementasi. Bogor: In Media.

Silva, A. R., & Dias, C. T. (2013). A cophenetic correlation coefficient for Tocher’s method. Pesq. agropec. bras., Brasília, 589-596.

Thamrin, N., & Wijayanto, A. W. (2021). Comparison of Soft and Hard Clustering: A Case Study on Welfare Level in Cities on Java Island. Indonesian Journal of Statistics and Its Applications, 141-160.

TNP2K. (2017). 100 Kabupaten/Kota Prioritas Untuk Intervensi Anak Kerdil (Stunting). Jakarta: Sekretariat Wakil Presiden Republik Indonesia.

UNICEF. (2019). Gizi di Indonesia. Retrieved 11 26, 2021, from Status Anak Dunia 2019: https://www.unicef.org/indonesia/id/status-anak-dunia-2019

WHO. (2014). Global Nutrition Targets 2025 Stunting Policy Brief. Geneva: WHO.

WHO. (2014, December 30). Global nutrition targets 2025: stunting policy brief. Retrieved November 22, 2021, from WHO/NMH/NHD/14.3: https://www.who.int/publications/i/item/WHO-NMH-NHD-14.3

WHO. (2018). Level and Trends in Child Malnutrition. Retrieved 11 12, 2021, from https://www.who.int/nutgrowthdb/2018-jme-brochure.pdf

WHO. (2019). Nutrition Landscape Information System (NLIS) Country Profile Indicators: Interpretation Guide, Second Edition. Geneva: World Health Organization (WHO).

WHO. (2021, June 09). Malnutririon. Retrieved 11 23, 2021, from WHO: https://www.who.int/news-room/fact-sheets/detail/malnutrition

WHO. (2021). Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD) (JME country). Retrieved 11 30, 2021, from The Global Health Observatory: https://www.who.int/data/gho/data/indicators/indicator-details/GHO/gho-jme-country-children-aged-5-years-stunted-(-height-for-age--2-sd)

WHO. (2021). The Global Health Observatory. Retrieved 11 30, 2021, from WHO: https://www.who.int/data/gho/data/countries/country-details/GHO/indonesia?countryProfileId=3584815c-0c4d-4f7b-b7c6-11487adf5df0

Xie, X. L., & Beni, G. (1991). A Validity Measure for Fuzzy Clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(8), 841-847.

Downloads

Published

31-08-2022

How to Cite

Suraya, G. R., & Wijayanto, A. W. (2022). Comparison of Hierarchical Clustering, K-Means, K-Medoids, and Fuzzy C-Means Methods in Grouping Provinces in Indonesia according to the Special Index for Handling Stunting: Perbandingan Metode Hierarchical Clustering, K-Means, K-Medoids, dan Fuzzy C-Means dalam Pengelompokan Provinsi di Indonesia Menurut Indeks Khusus Penanganan Stunting. Indonesian Journal of Statistics and Its Applications, 6(2), 180–201. https://doi.org/10.29244/ijsa.v6i2p180-201

Issue

Section

Articles