IMPLEMENTASI ALGORITMA K-MEANS UNTUK PEMETAAN PENDUDUK DI WILAYAH BANDA ACEH BERBASIS WEB
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Institusion
Institut Teknologi Perusahaan Listrik Negara
Author
Hubbirrafi, M.Raji
Aziza, Rosida Nur
Asri, Yessy
Aziza, Rosida Nur
Asri, Yessy
Subject
Teknik Informatika
Datestamp
2022-09-26 07:41:42
Abstract :
The uneven population density causes infrastructure development to only be concentrated in certain densely populated areas. This causes the results of development not to be enjoyed equally. This application was made to make it easier for people to find information about the population density in Banda Aceh, so that it is easier for people to analyze to build an infrastructure. This study uses the K-means method, K-Means is one of the data mining algorithms that can be used to determine data grouping. The way K-Means works will cluster areas that have high, medium, low population density. The results of this study using the K-Means method found that there were 26 villages with high density areas, while 33 villages and 31 villages low. From the cluster results obtained, the test uses the Davies Bouldin Index to find accuracy. And get optimal results of 0.59162.
The uneven population density causes infrastructure development to only be concentrated in certain densely populated areas. This causes the results of development not to be enjoyed equally. This application was made to make it easier for people to find information about the population density in Banda Aceh, so that it is easier for people to analyze to build an infrastructure. This study uses the K-means method, K-Means is one of the data mining algorithms that can be used to determine data grouping. The way K-Means works will cluster areas that have high, medium, low population density. The results of this study using the K-Means method found that there were 26 villages with high density areas, while 33 villages and 31 villages low. From the cluster results obtained, the test uses the Davies Bouldin Index to find accuracy. And get optimal results of 0.59162.
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