PENERAPAN ALGORITMA K-MEANS PADA CLUSTERING UNTUK PENYEBARAN BROSUR (STUDI KASUS PMB DI STTA YOGYAKARTA)
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Institusion
INSTITUT TEKNOLOGI DIRGANTARA ADISUTJIPTO
Author
AVIFUDIN, MOCH
Subject
Q Science (General)
Datestamp
2024-07-02 04:01:51
Abstract :
Brochures are official company publications in the form of prints, which contain information about a product, service, or program aimed at a target market or a specific target audience. PMB brochures distributed by STTA to various high schools are very numerous and random throughout Indonesia. A method is needed so that the distribution of PMB brochures can be efficient, effective, and on target by using the k-means clustering method. K-means is a repetitive clustering algorithm. The k-means algorithm assigns cluster (K) values randomly, while the value becomes the center of the cluster or commonly called the centroid. Then calculate the distance of each existing data on each centroid using the Euclidian formula to find the closest distance from each data with the centroid. Classify each data based on its proximity to the centroid. If the centroid value does not change, the iteration process is complete. The results of this study are an application that can show the relationship between two variables, namely brochures spread out and also found two types of clusters, namely cluster one with a very good distribution of brochures and cluster two with a distribution of brochures is not good.
Brochures are official company publications in the form of prints, which contain information about a product, service, or program aimed at a target market or a specific target audience. PMB brochures distributed by STTA to various high schools are very numerous and random throughout Indonesia. A method is needed so that the distribution of PMB brochures can be efficient, effective, and on target by using the k-means clustering method. K-means is a repetitive clustering algorithm. The k-means algorithm assigns cluster (K) values randomly, while the value becomes the center of the cluster or commonly called the centroid. Then calculate the distance of each existing data on each centroid using the Euclidian formula to find the closest distance from each data with the centroid. Classify each data based on its proximity to the centroid. If the centroid value does not change, the iteration process is complete. The results of this study are an application that can show the relationship between two variables, namely brochures spread out and also found two types of clusters, namely cluster one with a very good distribution of brochures and cluster two with a distribution of brochures is not good.
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