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Tifani Amalina
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Analisis K-Means Clustering Pada Pengiriman Produk Bearing Danendra Bima Adhi Pramana; Tifani Amalina; Riza Ibnu Adam
Jurnal Ilmiah Wahana Pendidikan Vol 8 No 15 (2022): Jurnal Ilmiah Wahana Pendidikan
Publisher : Peneliti.net

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (189.459 KB) | DOI: 10.5281/zenodo.7048988

Abstract

Delivery of finished goods is carried out based on the incoming PO from the customer and received by the Marketing Department. The Marketing Department will make a DO based on the PO that has been received and then the DO will be forwarded to the Shipping Section to make a Packing List. The packing list that has been made will be validated by the section head and the items contained in the packing list will be prepared by the Prepare Subsection. The validated packing list will be printed and forwarded to the Delivery Sub-section for delivery with the goods that are ready to be shipped. Shipping lines are made by the shipping coordinator based on the thoughts of the shipping coordinator, this causes the distance between one driver and another driver to be uneven. To overcome this problem, we need a method of dividing the task of delivering finished goods to customers. The method used is the K-Means Clustering Method. The K-Means Clustering method is a clustering method that partitions data into clusters so that data that have similarities are in the same cluster. In this study, the K-Means Clustering method was proven to be able to minimize the difference in distance between the driver and one PIC with the other driver and PIC. The difference in distance between the manual method and the K-Means Clustering method is 87,203 km.
Metode K-Means Clustering Dalam Pengelompokan Penjualan Produk Frozen Food Tifani Amalina; Danendra Bima Adhi Pramana; Betha Nurina Sari
Jurnal Ilmiah Wahana Pendidikan Vol 8 No 15 (2022): Jurnal Ilmiah Wahana Pendidikan
Publisher : Peneliti.net

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (179.465 KB) | DOI: 10.5281/zenodo.7052276

Abstract

The creation of many businesses in the field of online-based sales or known as e-commerce is proof that internet technology is currently developing so rapidly in various industries, including business. Online shop is a business activity that uses E-Commerce in its marketing or trading operations. Knowing how interested consumers are in buying a product can be done by counting the number of sales transactions made, which is one of the information that can be collected. So that the increasing number of transaction activities by consumers there is very large and out of data. The results of this study indicate that the most optimal number of clusters is two clusters. From 45 frozen food product data, 3 frozen food products were found in cluster 1 and 42 frozen food products entered cluster 2. This study aims to apply the k-means clustering method in grouping frozen food sales to find out the grouping of consumer interest in a product. frozen food. It is hoped that this research can be useful for the company and as a reference for further research.
Co-Authors Danendra Bima Adhi Pramana Riza Ibnu Adam