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Rosi Anisya Faujia
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Humanities Computer Science & IT Economics, Econometrics & Finance Social Sciences

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Analisis Klaster K-Means Dan Visualisasi Data Spasial Berdasarkan Karakteristik Persebaran Covid-19 Dan Pelanggaran Protokol Kesehatan Di Jawa Tengah Rosi Anisya Faujia; Muhammad Zidni Subarkah
Seminar Nasional Official Statistics Vol 2022 No 1 (2022): Seminar Nasional Official Statistics 2022
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (466.52 KB) | DOI: 10.34123/semnasoffstat.v2022i1.1222

Abstract

The world is currently faced with the Covid-19 virus pandemic with a significant spike in the spread of cases, especially in Indonesia. The high intensity of the spread of the virus is influenced by the violation of health protocols. In this analysis, the authors took a sample of areas in Central Java using the K-Means Clustering and GeoDa spatial analyze methods with the aim of knowing the characteristics of the spread of Covid-19 in Central Java with indications of health protocol violations. The best number of clusters was obtained, namely 4 clusters with a 74% confidence level. Cluster 1 has the highest confirmed cases of Covid-19. Cluster 2 has the highest health protocol violations. Cluster 3 has the lowest confirmed cases of Covid-19. Cluster 4 had the lowest health protocol violations. The author hopes that this analysis can be a reference for the government to reduce positive number of Covid-19.
Analisis Klaster K-Means Dan Agglomerative Nesting Pada Indikator Stunting Balita Di Indonesia Rosi Anisya Faujia; Eni Sawitri Setianingsih; Hasih Pratiwi
Seminar Nasional Official Statistics Vol 2022 No 1 (2022): Seminar Nasional Official Statistics 2022
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (359.795 KB) | DOI: 10.34123/semnasoffstat.v2022i1.1511

Abstract

The second target of the second goal of the SDGs is to eliminate all forms of malnutrition, one of which is an indicator of the prevalence of stunting in children under five. This study aims to classify and identify the characteristics of provinces in Indonesia based on stunting indicators in children under five. The method used is the k-means algorithm and agglomerative nesting (AGNES) clustering. By comparing the average silhouette value, it can be seen that the hierarchical clustering method of the AGNES algorithm with single linkage has the highest average silhouette value of 0,67 which is a strong cluster. Based on the results of the analysis obtained 2 optimum clusters. The characteristics of cluster 2, namely Papua Province, are indicators of a high incidence of stunting, because in this cluster immunization, access to sanitation, access to health facilities, high school education levels are low and LBW <2.500 grams. Meanwhile, the other 33 provinces are in cluster 1 with indicators of immunization, access to sanitation, access to health facilities, high school education and LBW <2.500 grams so that this cluster is included in the low incidence of stunting indicators.
Co-Authors Eni Sawitri Setianingsih hasih pratiwi Muhammad Zidni Subarkah