Analisis Perbandingan Metode Hierarchical dan Non-Hierarchical dalam Pembentukan Cluster Provinsi di Indonesia Berdasarkan Indikator Women Empowerment
Abstract
The focus on improving the quality of women’s live to lessen discrimination and gender inequality is set in the fifth’s goals of SDGs. In Indonesia, the RPJMN 2020-2024 contains measure to improve the contribution of women to equitable development. The Central Bureau of Statistics has developed several indicators related to gender, including Gender Development Index (GDI) an Gender Empowerment Index (GEI), which contain women’s improvement on education and health as well as their participation in economic and political fields. The Ministry of Women’s Empowerment and Child Protection did a quadrant analysis to split Indonesia’s 34 provinces into four categories based solely on GDI and GEI using the national average as a constraint. This study compares the Hierarchical, K-Means, and Fuzzy C-Means method to form number of clusters in Indonesia based on the gender development and empowerment in 2021 in order to complement the quadrant analysis. To choose the number of optimum cluster, Elbow method and Calinski-Harabasz Index were used and the best k value is five. From the validation with Silhoutte Index, K-Means was chosen as the best clustering model.
Keywords: clustering; fuzzy; k-means; hierarchical; women empowerment
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R. Diana, “Analisis Ketimpangan Gender Di Provinsi Sumatera Barat,” J. Kependud. Indones., vol. 13, no. Juni, pp. 55–66, 2018.
D. A. Yuliantono et al., “Algoritma Fuzzy C-Means Dengan Metode Elbow Untuk Mengelompokkan Provinsi Di Indonesia Berdasarkan Indeks Pembangunan Gender,” Universitas Muhammadiyah Jember, 2019. [Online]. Available: http://repository.unmuhjember.ac.id/4708/10/J. ARTIKEL.pdf
K. Nurfadilah, “Analisis Cluster Longitudinal Pada Pembangunan Manusia Di Sulawesi Selatan Berbasis Gender,” J. MSA (Matematika dan Stat. serta Apl., vol. 9, no. 1, 2021, doi: 10.24252/msa.v9i1.21180.
Badan Pusat Statistik, Sensus Ekonomi 2006 Evaluasi Terhadap Kriteria UMK dan UMB Hasil SE2006-SS. Jakarta: Badan Pusat Statistik, 2009.
R. T. Vulandari, S. Siswanti, A. K. Kusumawijaya, and K. Sandradewi, “Classification of Human Development Index Using K-Means,” Indones. J. Appl. Stat., vol. 2, no. 1, pp. 1, 2019, doi: 10.13057/ijas.v2i1.28566.
R. P. Prayogo and J. L. Buliali, “Penentuant Jumlah Cluster Optimum Pada Segmen Rute Penerbangan Menggunakan Data Automatic Dependent Surveillance-Broadcast,” JUTI J. Ilm. Teknol. Inf., vol. 18, no. 1, pp. 48, 2020, doi: 10.12962/j24068535.v18i1.a902.
Y. Liu, Z. Li, H. Xiong, X. Gao, and J. Wu, “Understanding of internal clustering validation measures,” in International Conference on Data Mining, 2010, pp. 911–916. doi: 10.1109/ICDM.2010.35.
A. M. Sikana and A. W. Wijayanto, “Analisis Perbandingan Pengelompokan Indeks Pembangunan Manusia Indonesia Tahun 2019 dengan Metode Partitioning dan Hierarchical Clustering,” J. Ilmu Komput., vol. 14, no. 2, pp. 66, 2021, doi: 10.24843/jik.2021.v14.i02.p01.
A. F. Khairati, A. . Adlina, G. . Hertono, and B. . Handari, “Kajian Indeks Validitas pada Algoritma K-Means Enhanced dan K-Means MMCA,” in PRISMA, Prosiding Seminar Nasional Matematika, 2019, vol. 2, pp. 161–170. [Online]. Available: https://journal.unnes.ac.id/sju/index.php/prisma/article/view/28906
R. Tibshirani, G. Walther, and T. Hastie, “Estimating the Number of Clusters in a Data Set Via the Gap Statistic,” J. R. Stat. Soc. Ser. B Stat. Methodol., vol. 63, no. 2, pp. 411–423, Jul. 2001, doi: 10.1111/1467-9868.00293.
Scikit Learn, “Selecting the number of clusters with silhouette analysis on KMeans clustering,”https://scikitlearn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html, 2017.
P. J. Rousseeuw, “Silhouettes: A graphical aid to the interpretation and validation of cluster analysis,” J. Comput. Appl. Math., vol. 20, no. C, pp. 53–65, 1987, doi: 10.1016/0377-0427(87)90125-7.
L.Fonseca,“Clustering Analysis in R using K-means,” https://towardsdatascience.com/clustering-analysis-in-r-using-k-means-73eca4fb7967, 2019.
University of Cincinnati, “Hierarchical Cluster Analysis,” http://uc-r.github.io/descriptive, 2018.
I. Mokris and L. Skovajsova, “Comparison of Document Clustering Techniques,” Twin Cities, 2008.
Kapilsparshi, “Difference between Hierarchical and Non Hierarchical Clustering,” https://www.geeksforgeeks.org/difference-between-hierarchical-and-non-hierarchical-clustering/, 2022.
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