Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal JURIKOM (Jurnal Riset Komputer) Bulletin of Computer Science Research
Tukino Tukino
Universitas Buana Perjuangan Karawang, Karawang
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Strategi Promosi untuk Meningkatkan Penjualan Kedai Kopi Desimal Menggunakan Algoritma K-Medoids Clustering Anggi Octa Fadilah; Baenil Huda; Agustia Hananto; Tukino Tukino
JURIKOM (Jurnal Riset Komputer) Vol 10, No 1 (2023): Februari 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v10i1.5561

Abstract

The Decimal coffee shop is a coffee shop located in the city of Karawang and is a coffee shop that is already busy with many customers, the Decimal coffee shop has been established since 2020 until now. Decimal coffee shop offers 35 diverse menu items, and sales can fluctuate, sometimes increasing and sometimes decreasing in the quality of the menu items sold. In this problem, sales data at Decimal coffee shops is not used to improve sales quality, the sales data is only used as an archive for the coffee shop, if the data is analyzed properly, it will be useful to determine which menu items are selling well and which are not selling well. By analyzing sales data, it will be possible to determine which menu needs to be improved in terms of sales. This information can then be used by the coffee shop as a reference in developing a promotional strategy aimed at increasing sales of the menu product. To find out how many menus are sold at Decimal coffee shops, a clustering study was carried out. This research was conducted by analyzing sales data in excel form, the K-Medoids method was used to create clusters based on product sales data that had been obtained from the Decimal Coffee Shop. From the clustering results, there are 3 clusters which are classified as high, medium, and low, and the accuracy is determined using the RapidMiner tool. Of the 35 items analyzed, the first cluster contains 18 items which are rated the highest, the second cluster contains 12 items which are classified as moderate, and the third cluster contains 5 items which are classified as the lowest. From these results there are 5 items on sales that are classified as low, therefore a promotional strategy is needed to increase the menu product.
Prediksi Penjualan Barang Menggunakan Metode K-Means dan Regresi Linear Henry Adam; Tukino Tukino; Elfina Novalia; April Lia Hananto
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.541

Abstract

Sales data analysis plays an important role in supporting business decision making, especially to optimise stock management and improve operational efficiency. the main problem faced by Vapestore XYZ in Karawang is the difficulty in accurately predicting the number of product sales, so there is often an imbalance between inventory and market demand. This can cause losses due to overstocks or shortages of goods. Currently, the estimation of stock requirements still relies on intuition and personal experience, without the support of objective data analysis. This research aims to build a sales prediction model by combining the K-Means method for product clustering and Linear Regression for sales quantity prediction. Sales data is taken directly from the store POS application, then goes through the stages of cleaning, labelling, and clustering into three groups, namely ‘Less Sold’, “Sold”, and ‘Very Sold’. Sales prediction is performed using Linear Regression by utilising the clustering results and time variables as inputs. Model performance evaluation is performed using error metrics, namely Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). Based on the test results, the developed Linear Regression model obtained MAE of 3.20, MSE of 52.34, and RMSE of 7.23. These error values indicate that the model is able to provide sales estimates that are close enough to the actual data to be reliable in stock planning. Visualisation of the prediction results in the form of tables and heatmaps makes it easy to identify sales trends and compare performance between products. The findings of this study prove that the combination of K-Means and Linear Regression methods is effectively used to support stock decision making and marketing strategies in vape retail stores. Further development is recommended by enriching the dataset and exploring other prediction methods to improve model performance.
Co-Authors Anggi Octa Fadilah April Lia Hananto Baenil Huda Elfina Novalia Hananto, Agustia Henry Adam