Clustering the Length of Roads at North Sumatra BBPJN Using the K-Means Algorithm
DOI:
https://doi.org/10.55537/cosie.v2i1.567Keywords:
clustering, data mining, k-meansAbstract
Abstract
The road is a land transportation infrastructure which is a very vital transportation route. The grouping of road lengths is something that is not yet known in the Road Section Data at North Sumatra BBPJN. Therefore this study will discuss the Long Clustering of Roads in North Sumatra. The method used is K-Means Clustering Data Mining. With the K-Means Clustering method, the data that has been obtained can be grouped into several clusters, where the K-Means Clustering process is implemented using RapidMiner. The data used is data for roads in North Sumatra, the area recorded includes Jl. BTS. PROV. ACEH - SIMPANG PANGKALAN SUSU to Jl. ONAN RUNGGU - TOMOK. Can be divided into 3 Clusters: short (C1), medium (C2) and long (C3). The results obtained are that there are 118 roads with short-level clusters (C1), 37 roads with medium-level clusters (C2), and 21 roads with long-level clusters (C3). This can be input for the North Sumatra BBPJN to find out the boundaries of short, medium and long road sections.
Keywords: Clustering, K-Means, Data Mining, RapidMiner
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