PENERAPAN ALGORITMA K-MEANS UNTUK CLUSTERING DATA PENDUDUK MISKIN PER KABUPATEN/KOTA DI PULAU SUMATERA

ANITA, YULYA and Alfarissi, Alfarissi and Miraswan, Kanda Januar (2024) PENERAPAN ALGORITMA K-MEANS UNTUK CLUSTERING DATA PENDUDUK MISKIN PER KABUPATEN/KOTA DI PULAU SUMATERA. Undergraduate thesis, Sriwijaya University.

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Abstract

Poverty is a problem that exists in every region in Indonesia. There are many factors that cause poverty that influence the level of welfare of the population. Therefore, it is necessary to group data n the poor population of districts/cities to assist in policy decision making. This research uses the K-Means algorithm which is a technique in data mining. Grouping large amounts of data with relatively fast and efficient computing time is the advantage of the K-Means algorithm. This research aims to detemine the results of grouping poverty levels from low to highest levels. The results of the grouping will be evaluated using the Davies-Boulding Index (DBI) value. After testing and evaluating 3 times with 10 groups, it was found that the 10th group was the best result. The comparison of results in 2020 obtained for the district/city category, the lowest was 4 group members and the highest 28 group members. In 2021, the lowest was 4 group members and the highest was 30 group members for the district/city category. In 2022, the lowest was 4 group members for the district/city category and the highest was 29 group members.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Kemiskinan, Clustering, Algoritma K-Means, Davies-Bouldin Index
Subjects:	T Technology > T Technology (General) > T59.7-59.77 Human engineering in industry. Man-machine systems
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Yulya Anita
Date Deposited:	06 Mar 2024 01:59
Last Modified:	06 Mar 2024 01:59
URI:	http://repository.unsri.ac.id/id/eprint/141362

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