PEMETAAN ZONA RESIKO PENULARAN COVID-19 DI SULAWESI SELATAN MENGGUNAKAN PLOT DENDROGRAM HIERARCHICAL CLUSTERING - Muhammad Ilham Mubarok; Aris Rusyiana (Seminar Nasional Official Statistics, 2020)
Recently, South Sulawesi Province is one of the five provinces with a high positive rate of Covid-19 in Indonesia. Also, this province shows the positive rate and death rate due to Covid-19 increases daily. Observing this situation, we conduct a study to cluster the risk of handling Covid-19 in terms of pandemic statistics (total cumulative cases, patients recovered, patients died, and patients treated) in entirelt districts and municipality of South Sulawesi Provinces. We also examine the hierarchical clustering of info-demics with the economic, health, social and demographic characteristics of districts/cities nexus. This study uses a quantitative research design using official statistical data from BPS and related governments, to cluster 19 districts/cities of the Covid-19 pandemic areas into 4 areas, as well as to analyze a dendrogram hierarchical clustering within the regional scope of South Sulawesi Province. We categorize districts/municipality of South Sulawesi Provinces into 4 (four) clusters, namely 1st cluster (low), 2nd cluster (moderate), 3rd cluster (alert), 4th cluster (dangerous). We find that Makassar City relates to cluster 4 with the following characteristics: highly positive cases of Covid-19 patients and densedly populated areas. On the other hand, Makassar, a capital city of South Sulawesi Province, shows resilience from the point of view of the supply factor for health facilities support, whether related to hospital health care facilities, primary health facilities (puskesmas), or community-based health facilities (posyandu), as well as largest health personnel. Meanwhile, Gowa and Bone are included in the 3rd cluster as two closest districts to the epicenter of Covid-19 outbreakes in Makassar, which have the second highest number of positive case characteristics for Covid-19 patients from districts/cities in the 1st zone , and are supported by population density figures that are more tenuous than the 4th zone, and supported by health facilities and health workers, the second largest compared to districts/cities in the 1st zone. Despites our immature findings, this study could give important suggestion that Government of South Sulawesi Provinces and the entirely districts/cities may take into account and look closer for the readiness policy to combat the Covid-19. This study suggests that South Sulawesi province’s decision makers should improve the quality of coordination for the Covid-19 emergency response, for examples in limiting citizens’ mobility and extending Large-Scale Social Restrictions (PSBB). For upcoming studies, researchers could go futher in calculating a composite resilient index (optimistic point of view) or a composite index of vulnerability (pessimistic point of view) to the Covid-pandemic. 19, which will be useful for regional, national, and even international.