Comparison of Agglomerative Hierarchical and K-Means in Grouping Provinces Based on Maternal Health Services

Alya Azzahra, Arie Wahyu Wijayanto


During the Covid-19 period, there were barriers to access for pregnant women to health services that could interfere with maternal health. Therefore, it is necessary to know  the achievement of maternal health service coverage in Indonesia during the Covid-19 period in 2020, especially at the provincial level so that it can help the government to determine regional priorities for the fulfillment of more adequate maternal health services. Determination of provincial priorities for the fulfillment of maternal health services can be achieved by grouping the regions according to the characteristics of maternal health services in the local province. Cluster analysis is able to group objects in the form of provinces into one cluster. The clustering methods that will be used are agglomerative hierarchical clustering and k-means clustering. The results of the clustering of the two methods will be compared with internal validation in the form of dunn index, connectivity index, ang silhouette index. The best clustering resuls are obtained by using agglomerative hierarchical clustering alghoritm using the complete linkage similarity function with the resulting five clusters. The results of the identification of cluster characteristics show that cluster 1 with 14 members is categorized as provinces with good coverage of maternal services. Cluster 2 which consists of 15 provinces is categorized as best coverage. Cluster 3 which member are NTT and Maluku is categorized as bad. Cluster 4 which member is East Kalimantan is categorized as sufficient coverage. Meanwhile cluster 5 which member are Papua and West Papua is still on concern because its categorized as worst coverage

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D. Hapsari, P. Sari, and L. Indrawati, “Indeks Kesehatan Maternal Sebagai Indikator Jumlah Kelahiran Hidup ,” Jurnal Ekologi Kesehatan , vol. 14, no. 3, pp. 259–272, Sep. 2015.

M. A. Naga, “Kesehatan Ibu dan Anak,” Jakarta, 2009.

“World Health Statistics 2019 : Monitoring For The SDGs,” Switzerland, 2019. Accessed: Dec. 04, 2021. [Online]. Available:

Kementerian Perencanaan dan Pembangunan Nasional/Bappenas, Terjemahan Tujuan dan Target Global Tujuan Pembangunan Berkelanjutan (TPB). Jakarta: Kementrian Perencanaan dan Pembangunan Nasional/Bappenas, 2017. Accessed: Dec. 04, 2021. [Online]. Available:

S. Sumarmi, “Model Sosio Ekologi Perilaku Kesehatan Dan Pendekatan Continuum Of Care Untuk Menurunkan Angka Kematian Ibu,” The Indonesian Journal of Public Health, vol. 12, no. 1, pp. 129–141, Dec. 2017, doi: 10.20473/IJPH.V12I1.2017.129-141.

UNICEF, “Maternal and newborn health and COVID-19 - UNICEF DATA,” May 2020. (accessed Dec. 04, 2021).

R. Hida Nurrizka et al., “Akses Ibu Hamil terhadap Pelayanan Kesehatan di Masa Pandemi COVID-19,” Jurnal Kebijakan Kesehatan Indonesia : JKKI, vol. 10, no. 2, pp. 94–99, Jun. 2021, doi: 10.22146/JKKI.62752.

Cici Suhaeni, Anang Kurnia, and Ristiyanti, “Perbandingan Hasil Pengelompokan menggunakan Analisis Cluster Berhirarki, K-Means Cluster, dan Cluster Ensemble (Studi Kasus Data Indikator Pelayanan Kesehatan Ibu Hamil),” Jurnal Media Infotama, vol. 14, no. 1, pp. 31–38, Feb. 2018, doi:

K. B. Aditya, D. Puspitaningrum, and Y. Setiawan, “Sistem Informasi Geografis Pemetaan Faktor-faktor Yang Mempengaruhi Angka Kematian Ibu (Aki) Dan Angka Kematian Bayi (Akb) Dengan Metode K-means Clustering (Studi Kasus: Provinsi Bengkulu) - Neliti,” Jurnal Teknik Informatika, pp. 59–65, 2017, Accessed: Dec. 05, 2021. [Online]. Available:

A. Nufus, “Pengelompokkan Kabupaten/Kota Di Jawa Timur Berdasarkan Program Pelayanan Kesehatan Ibu,” Medical Technology and Public Health Journal, vol. 3, no. 1, pp. 9–16, Mar. 2019, doi: 10.33086/MTPHJ.V3I1.934.

N. Thamrin, A. W. Wijayanto, S. Politeknik, and I. Stis, “Comparison of Soft and Hard Clustering: A Case Study on Welfare Level in Cities on Java Island,” Indonesian Journal of Statistics and Its Applications, vol. 5, no. 1, pp. 141–160, Mar. 2021, doi: 10.29244/IJSA.V5I1P141-160.

A. R. Damayanti and A. W. Wijayanto, “Comparison of Hierarchical and Non-Hierarchical Methods in Clustering Cities in Java Island using the Human Development Index Indicators year 2018,” Eigen Mathematics Journal, vol. 4, no. 1, pp. 8–17, Jun. 2021, doi: 10.29303/EMJ.V4I1.89.

A. M. Sikana and A. W. Wijayanto, “Analisis Perbandingan Pengelompokan Indeks Pembangunan Manusia Indonesia Tahun 2019 dengan Metode Partitioning dan Hierarchical Clustering,” Jurnal Ilmu Komputer, vol. 14, no. 2, pp. 66–78, Sep. 2021, doi: 10.24843/JIK.2021.V14.I02.P01.

N. Afira and A. W. Wijayanto, “Analisis Cluster dengan Metode Partitioning dan Hierarki pada Data Informasi Kemiskinan Provinsi di Indonesia Tahun 2019,” Komputika : Jurnal Sistem Komputer, vol. 10, no. 2, pp. 101–109, Sep. 2021, doi: 10.34010/KOMPUTIKA.V10I2.4317.

E. Luthfi and A. W. Wijayanto, “Analisis perbandingan metode hirearchical, k-means, dan k-medoids clustering dalam pengelompokkan indeks pembangunan manusia Indonesia,” Jurnal Inovasi, vol. 17, no. 4, pp. 761–773, Dec. 2021, Accessed: Dec. 26, 2021. [Online]. Available:

Kementerian Kesehatan RI, “Profil Kesehatan Indonesia Tahun 2020,” Jakarta, 2021.

D. N. Gujarati and D. C. Porter, Basic Econometric, 5th ed. Douglas Reiner, 2009.

A. T. Fitriyah, “Penerapan Metode Agglomerative Hierarchical Clustering Untuk Klasifikasi Program Studi Berdasarkan Kualitas Pelayanan Mahasiswa,” Jurnal Ekonomi Syariah, vol. 8, no. 2, pp. 194–202, Dec. 2018.

J. F. Hair Jr, W. C. Black, B. J. Babin, and R. E. Anderson, Multivariate Data Analysis, 7th ed. New Jersey: Pearson Prentice Hall, 2010.

R. A. Johnson and D. W. Wichern, Applied Multivariate Statistical Analysis, 6th ed. 2007.

S. Saraçli, N. Do, ˘ Gan, ˙ Ismet, and D. ˘ Gan, “Comparison of hierarchical cluster analysis methods by cophenetic correlation,” Journal of Inequalities and Applications, p. 203, 2013, Accessed: Dec. 20, 2021. [Online]. Available:

L. Kaufman and P. J. Rousseeuw, Finding Groups in Data : An Introduction to Cluster Analysis. Hoboken, NJ, USA: John Wiley & Sons, Inc., 1990. doi: 10.1002/9780470316801.


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