Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : Journal of Artificial Intelligence and Engineering Applications (JAIEA)

Analysis of Beverage Sales Data Using the FP-Growth Algorithm at Sini Aja Cafe Widisa Adi Kumara; Rini Astuti; Willy Prihartono; Tati Suprapti
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.772

Abstract

The growth of information technology and data mining techniques has greatly helped analyze consumer purchasing behavior, particularly in marketing and inventory management. This study aims to uncover association patterns between products frequently bought by customers at Sini Aja Cafe and to measure these patterns' support and confidence values. The research uses Knowledge Discovery in Databases (KDD), including stages like data selection, preprocessing, transformation, applying the FP-Growth algorithm, and interpreting results. Data from 1,083 beverage sales transactions at Sini Aja Cafe from August 1 to October 31, 2024. The findings reveal five significant association rules when applying a minimum support of 0.1 (10%) and confidence of 0.3 (30%). Notably, if customers buy Red Velvet Oreo, there is a 56% chance they will also buy Thai Tea. Thai Tea sales dominate with a support value 0.557 (55.7%). The support values of the association rules range from 0.141, categorized as medium, and the confidence values range from 0.235, categorized as low. These findings offer valuable insights for the cafe owner to optimize operations, enhance customer satisfaction, and increase profits.
Website Based Digital Branding Strategy for Increase Sales of Gunung Puntang Coffee In Mekarjaya, Bandung Regency Juliyanti; Rini Astuti; Willy Prihartono
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.802

Abstract

This research aims to develop a branding strategy through optimizing a website-based digital company profile to increase sales of Gunung Puntang coffee. Gunung Puntang coffee is a high-quality local product that requires a digital approach to marketing to reach a broader market and enhance competitiveness. In today's digital era, a website plays a crucial role as a promotional and informational medium, providing customers with easy access to product information and enabling online purchases. The research employs the Prototype method, consisting of problem identification, planning, requirements analysis, system design, implementation, and testing phases. Data collection was conducted through observation, interviews with the coffee business owner, and documentation studies related to business processes and branding strategies. The collected data serves as a basis for designing a website system that optimizes the company's profile and supports coffee sales transactions. The system development includes creating use case diagrams, activity diagrams, and system architecture designs to outline functional and non-functional requirements. The research outcome is a website functioning as a digital information medium for branding Gunung Puntang coffee products and supporting sales transactions. Key features include customer registration, product selection, quantity adjustment, payment methods, order confirmation, and order cancellation. Testing results indicate that the system operates effectively and meets user needs. This website enhances operational efficiency, expands market reach, and improves the shopping experience for customers. It serves as an effective medium for strengthening branding and marketing strategies in the digital era, ensuring the sustainability of local businesses in the global market. Regular evaluations and feature upgrades are recommended to maintain system relevance to customer needs and technological advancements..
Application of K-Means for Product Grouping Best Sellers at Planet Tire Jatibarang Branch Risnawati; Rini Astuti; Willy Prihartono
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.845

Abstract

This research aims to identify the best-selling products at Planet Tire Workshop Jatibarang Branch using the K-Means Clustering method. Understanding product sales patterns is important in designing effective marketing strategies and managing stock efficiently. This research uses sales transaction data for one year, including the number of sales, product types, and total transaction value. The analysis process includes data preprocessing, selection of relevant attributes, application of the K-Means algorithm, and validation of the optimal number of clusters with the Elbow method. As a result, products were grouped into three categories: high, medium, and low sales. The high sales cluster contributes significantly to revenue, while the medium sales cluster shows potential for improvement through promotion, and the low sales cluster requires further evaluation. This research helps management manage stock, prioritize promotions, and optimize resource allocation. However, the research has limitations as it has not considered external factors such as seasonal trends and promotions, and focuses on one branch. Development of the research in other branches can expand its benefits. The results of this study are expected to improve operational efficiency, support data-driven strategies, and enrich academic literature related to the application of K-Means in retail management and sales data analysis.