Application of Agglomerative Hierarchical Clustering Method for Grouping Non-Cash Food Assistance Recipients in Ngambon Bojonegoro

 Abstract views: 93

Authors

  • Alif Yuanita Kartini Universitas Nahdlatul Ulama Sunan Giri
  • Abdul Manaf Jamiluddin Universitas Nahdlatul Ulama Sunan Giri

DOI:

https://doi.org/10.36456/jstat.vol16.no1.a6122

Keywords:

Agglomerative hierarchical clustering, Non-cash food assistance, Grouping

Abstract

One of the sub-districts in Bojonegoro that received non-cash food assistance was Ngambon sub-district. The non-cash food assistance provided in Ngambon sub-district has not been on target. This is because underprivileged people do not get assistance, while people who can afford it actually get non-cash food assistance. So, research is needed with the aim that non-cash food assistance provided by the government can be distributed according to procedures. The method used in this study is agglomerative hieralchical clustering to group recipients of non-cash food assistance from the people of Ngambon Bojonegoro. The variables used were 12 indicators of non-cash food assistance set by the Bojonegoro district Social Office. The data used were 131 recipients of non-cash food assistance spread across five villages in Ngambion sub-district. Grouping results with the single linkage method are less relevant. Meanwhile, with the average linkage and complate linkage methods, five clusters were obtained, and with ward linkage, three clusters were obtained. Based on the elbow rule, it was found that ward linkage is the best grouping method, with cluster 1 totaling 57 people, cluster 2 totaling 53 people and cluster 3 totaling 21 people.

Downloads

Download data is not yet available.

References

H. Hardianto, “Determinasi Pemberdayaan Masyarakat Dan Pemberantasan Kemiskinan Desa: Analisis Dana Desa Dan Alokasi Dana Desa (Literature Review Manajemen Keuangan),” J. Manaj. Pendidik. DAN ILMU Sos., vol. 3, no. 1, pp. 266–275, 2022.

N. R. R. Dani, “Implementasi Program Bantuan Sosial Tunai (BST) Pada Masa Pandemi Covid-19 di Kecamatan Kedungadem Kabupaten Bojonegoro,” Publika, pp. 1187–1200, 2022.

D. M. Hasimi, “Analisis Program Bantuan Pangan Non Tunai (BPNT) guna meningkatkan kesejahteraan masyarakat dalam perspektif ekonomi Islam,” REVENUE J. Manaj. Bisnis Islam, vol. 1, no. 01, pp. 61–72, 2020.

N. A. Putri and H. Purnaweni, “Implementasi Program Keluarga Harapan (Pkh) Dalam Upaya Penanggulangan Kemiskinan Di Kecamatan Bojonegoro,” J. Public Policy Manag. Rev., vol. 10, no. 3, pp. 510–522, 2021.

S. Rahayu and A. Y. Kartini, “Algoritma K-Means Dan K-Medoids Untuk Pengelompokan Kecamatan Penerima Bantuan Sosial Di Kabupaten Bojonegoro,” MEDIA BINA Ilm., vol. 16, no. 5, pp. 6815–6822, 2021.

J. Parhusip, “Penerapan Metode Analytical Hierarchy Process (AHP) Pada Desain Sistem Pendukung Keputusan Pemilihan Calon Penerima Bantuan Pangan Non Tunai (BPNT) Di Kota Palangka Raya,” J. Teknol. Inf. J. Keilmuan dan Apl. Bid. Tek. Inform., vol. 13, no. 2, pp. 18–29, 2019.

C. A. Sugianto and F. R. Maulana, “Algoritma Naï ve Bayes Untuk Klasifikasi Penerima Bantuan Pangan Non Tunai (Studi Kasus Kelurahan Utama),” Techno. Com, vol. 18, no. 4, pp. 321–331, 2019.

R. A. Saputra, S. Wasiyanti, and D. Pribadi, “Information Gain Pada Algoritma C4. 5 Untuk Klasifikasi Penerimaan Bantuan Pangan Non Tunai (BPNT),” Indones. J. Bus. Intell., vol. 4, no. 1, pp. 25–30, 2021.

P. R. Garikapati, K. Balamurugan, T. P. Latchoumi, and G. Shankar, “A quantitative study of small dataset machining by agglomerative hierarchical cluster and K-medoid,” in Emergent Converging Technologies and Biomedical Systems: Select Proceedings of ETBS 2021, Springer, 2022, pp. 717–727.

A. Naeem, M. Rehman, M. Anjum, and M. Asif, “Development of an efficient hierarchical clustering analysis using an agglomerative clustering algorithm,” Curr. Sci., vol. 117, no. 6, pp. 1045–1053, 2019.

N. Randriamihamison, N. Vialaneix, and P. Neuvial, “Applicability and interpretability of Ward’s hierarchical agglomerative clustering with or without contiguity constraints,” J. Classif., vol. 38, no. 2, pp. 363–389, 2021.

Y. Rong, “Staged text clustering algorithm based on K-means and hierarchical agglomeration clustering,” in 2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), 2020, pp. 124–127.

S. Wu, J. Lin, Z. Zhang, and Y. Yang, “Hesitant fuzzy linguistic agglomerative hierarchical clustering algorithm and its application in judicial practice,” Mathematics, vol. 9, no. 4, pp. 1–16, 2021, doi: 10.3390/math9040370.

C. Chatfield, Introduction to multivariate analysis. Routledge, 2018.

Downloads

Published

07/31/2023

How to Cite

Kartini, A. Y., & Jamiluddin, A. M. . . (2023). Application of Agglomerative Hierarchical Clustering Method for Grouping Non-Cash Food Assistance Recipients in Ngambon Bojonegoro. J Statistika: Jurnal Ilmiah Teori Dan Aplikasi Statistika, 16(1), 342–353. https://doi.org/10.36456/jstat.vol16.no1.a6122