Pendugaan Persentase Penduduk Miskin Ekstrem Menggunakan Small Area Estimation dengan Partitioning Around Medoids Clustering - Ramadhan, Yogi; Ubaidillah, Azka (Politeknik Statistika STIS, 2023)
Eradication of extreme poverty is one of the goals to be achieved among the global goals (SDGs). Eradicating extreme poverty is inseparable from the role of governments as policymakers. Policy creation requires high-precision data. Aggregate extreme poverty data are collected based on the National Socio-Economic Survey (Susenas), based on Susenas results in March 2022 East Java is one of the provinces with a high number of people living below the extreme poverty line. Besides that, the high RSE in estimating the percentage of people in extreme poverty in regency/city in East Java province makes the precision low. Low precision results in inaccurate estimated data and should not be used, especially for policy making. One way to improve accuracy is to use Small Area Estimation (SAE). The most commonly used SAE model is EBLUP, and for unsampled area estimation, the estimation can use clusters of information. Problems that arise in forming clusters are outliers in the observed data, which can lead to forming errors within the clusters. A cluster of algorithms that can be used to overcome these problems is Partitioning Around Medoids (PAM).