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ANALISIS SEGMENTASI SPBU DI KOTA PALEMBANG BERDASARKAN POLA TRANSAKSI PADA APLIKASI MYPERTAMINA MENGGUNAKAN ALGORITMA K-MEANS CLUSTERING

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K-Means Clustering is a method for grouping data into a number of clusters based on similarity of characteristics. This study aims to group 36 gas stations in Palembang City based on gas station transaction patterns in 2023. This study used four variables, namely sales volume, stock quantity, transaction quantity, and revenue. Data analysis started with understanding the data and ended by evaluating the results. The data is normalized using the Min-Max Normalization method to equalize the scale between variables. The K-Means Clustering algorithm is applied using Euclidean distance to group gas stations based on the similarity of their transaction patterns. The optimal number of clusters using the elbow and DBI methods was obtained as K=6 and K=7, respectively. The difference in the number of clusters in the DBI method results was 7 clusters, where cluster 5 in the elbow method was divided into clusters 5 and 7 in the DBI method. The value of variable � �1 was high in the elbow method results, while it was very high in the DBI results. The grouping of the other 5 clusters from the results of the two methods has the same gas station value characteristics. Cluster 1, consisting of 12 gas stations, has high value characteristics except for a moderate volume value. Cluster 2, consisting of 4 gas stations, has high value characteristics except for a low volume. Cluster 3 consists of 8 gas stations with very high values but moderate Volume. Cluster 4 consists of 6 gas stations with low values but moderate Income. Cluster 6 consists of 1 gas station with very high values but low Volume. Keywords: Segmentation, K-Means Clustering, Gas Station, Elbow method, DBI

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Series Title

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Call Number

T1843302025

Publisher

Indralaya : Prodi Ilmu Matematika, Fakultas Matematika Dan Ilmu Pengetahuan Alam Universitas Sriwijaya., 2025

Collation

xvi, 73 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

519.707

Content Type

Text

Media Type

unmediated

Carrier Type

other (computer)

Edition

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Subject(s)

Prodi Ilmu Matematika
Matematika Pemograman

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SEW

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