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This research aims to apply the K-Means method in classifying egg stock, speeding up stock searches to meet consumer demand, and efficiently analyzing the amount of egg stock for inventory at PT. Kaizen Prima Bersama. The prospect of developing laying hens is expected to be able to meet community demand, especially for use as a business to meet community production and consumption needs. As datasets grow, single CPU based approaches will lose their efficacy. This research aims to develop a parallel universal K-Means algorithm that is capable of handling larger datasets. To overcome this challenge, the K-Means Clustering algorithm is used. The application of the K-Means clustering algorithm in managing egg supplies provides advantages in recording data. This algorithm facilitates categorization of inventory levels based on specific attributes. Utilizing available information, the K-Means clustering algorithm professionally groups egg supply into daily transactions, to meet consumer needs.This research method uses data mining, namely the Knowledge Discovery Database (KDD) to obtain various patterns obtained from data. This method is expected to help company manages egg stocks more effectively. The benefits resulting from this research include the development of an information system that can speed up the data input and output process on egg stock taking, reduce the accumulation of records in paper form, and create an information system that makes work more efficient and easier to understand. With the information system implemented, it is hoped that company can improve operational performance and respond quickly to the dynamics of the egg market.
Antonius et al. (Tue,) studied this question.