Penerapan Data Mining Dalam Pengelompokan Data Penjualan Paket Internet di Telkomsel Authorized Partner (TAP) Deli Tua Dengan Algoritma K-Means

Siregar, Tesa Aurelia
Mesran, M
Utomo, Dito Putro

Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika)
Vol 8, No 2 (2023): Edisi Agustus

With the increasing sales of internet packages at TAP Deli Tua, it is crucial to be more meticulous in processing the sales data to avoid stock shortages that could result in losses for TAP Deli Tua. Determining the best-selling products among the internet package sales is essential, as incorrect grouping may lead to losses for TAP Deli Tua. This could further decrease the sales level at TAP Deli Tua, causing significant financial losses for the company. Therefore, TAP Deli Tua must be more attentive in data processing to prevent any detrimental outcomes.To achieve accurate decision-making, TAP Deli Tua needs to collect sales data from internet packages for analysis. One of the algorithms used for this purpose is the K-Means algorithm, which falls under Non-Hierarchical Clustering. It partitions the dataset into several clusters, optimizing the grouping criteria. The most commonly used criterion is the one that minimizes the clustering error for each point by calculating its squared distance from the corresponding cluster center. Additionally, the sum of distances for all points in a dataset is computed.Based on research findings, data mining with the implementation of the K-Means algorithm can assist Telkomsel Authorized Partner (TAP) in making more accurate and significant decisions. By applying the K-Means algorithm, the analysis revealed that out of 15 sales data points for internet packages, 8 best-selling products were in Cluster 0, while 7 non-best-selling products were in Cluster 1. With the increasing sales of internet packages at TAP Deli Tua, it is crucial to be more meticulous in processing the sales data to avoid stock shortages that could result in losses for TAP Deli Tua. Determining the best-selling products among the internet package sales is essential, as incorrect grouping may lead to losses for TAP Deli Tua. This could further decrease the sales level at TAP Deli Tua, causing significant financial losses for the company. Therefore, TAP Deli Tua must be more attentive in data processing to prevent any detrimental outcomes.To achieve accurate decision-making, TAP Deli Tua needs to collect sales data from internet packages for analysis. One of the algorithms used for this purpose is the K-Means algorithm, which falls under Non-Hierarchical Clustering. It partitions the dataset into several clusters, optimizing the grouping criteria. The most commonly used criterion is the one that minimizes the clustering error for each point by calculating its squared distance from the corresponding cluster center. Additionally, the sum of distances for all points in a dataset is computed.Based on research findings, data mining with the implementation of the K-Means algorithm can assist Telkomsel Authorized Partner (TAP) in making more accurate and significant decisions. By applying the K-Means algorithm, the analysis revealed that out of 15 sales data points for internet packages, 8 best-selling products were in Cluster 0, while 7 non-best-selling products were in Cluster 1.