Pe Application of the K-Means Algorithm in Grouping Households by Province and Ownership Status of Owned Houses
DOI:
10.33395/sinkron.v5i2.10883Keywords:
Household, Grouping, Community, K-Means Algorithm, OwnershipAbstract
The house is a place or container to protect people from hot or cold air. A household is a group of people who gather in a place and share food, shelter, and others. People who live in a house, some live in a boarding house and some already have their own homeownership status. Research conducted by this author discusses the Application of Household Clustering by Province and Ownership Status of Owned Houses using K-Means Algorithm. The research data is sourced from the National Statistics Agency. The data used in doing this is the data on the percentage of households by province and the status of self-owned house ownership in 1999-2019. The data consists of 34 provinces. The variables of this study are based on the average number of percentages of households by province and ownership status of self-owned houses. The data clustering will be carried out by dividing it into 3 clusters, namely the high level of self-owned house ownership status, the moderate level of self-owned house ownership status, and the low level of self-owned house ownership status. The results of this study are based on the Household index by Province and Ownership Status of Owned Houses, as many as 12 provinces with high self-owned house ownership, namely 86,14, 21 provinces with medium self-owned house ownership, namely 75,11, and 1 province with low self-owned house ownership, namely 51,61. The existence of this research can make people more open-minded about having their own house with ownership status.
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Copyright (c) 2021 Kristin Sianipar, Septri Siahaan, Indra Gunawan
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