In 2018, Indonesian government set 100 districts as the priority to reduce stunting. In this study, we hypothesize that the 100 determined districts should not be treated on an equal policy, due to some underlying factors that might affect the stunting in those districts. Thus, it is necessary to identify and analyze the grouping of 100 priority districts for stunting interventions in 2018 based on the National Team's indicators for the Acceleration of Poverty Reduction to see the severity of stunting. It is hoped that this clustering could be a reference for the government in determining priority regency groups to reduce stunting rates. Data on 100 districts, represented by eight numerical measurements and six categorical measurements were analyzed using Partitioning Around Medoids (PAM) method. Data similarity was measured using Gower distance, which can handle the clustering of mixed data types. We identified five priority district groups which provide meaningful insights. One of the groups has the worst stunting severity condition among other groups, for each indicator; implying the high priority to follow-up by the government. The majority of districts in Papua and East Nusa Tenggara Provinces are districts with poor stunting severity. We also found that poverty, proportion of the population with defecation in latrines, access to clean water and proper sanitation, number of integrated healthcare centers (posyandu) in a village, and number of doctors in each district are important factors that explain the stunting severity.
|Journal||Journal of Physics: Conference Series|
|Publication status||Published - 7 Jan 2021|
|Event||10th International Conference and Workshop on High Dimensional Data Analysis, ICW-HDDA 2020 - Sanur-Bali, Indonesia|
Duration: 12 Oct 2020 → 15 Oct 2020