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All Journal Journal of Artificial Intelligence and Digital Economy
Azmuri Wahyu Azinar
Muhammadiyah University of Sidoarjo, Indonesia

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IMPLEMENTATION OF DATA MINING FOR ANGKRINGAN SALES ANALYSIS USING THE APRIORI METHOD Aprilia Widiya Umaroh; Nuril Lutfi Azizah; Novia Ariyanti; Azmuri Wahyu Azinar
Journal of Artificial Intelligence and Digital Economy Vol. 1 No. 12 (2024): Journal of Artificial Intelligence and Digital Economy
Publisher : PT ANTIS INTERNATIONAL PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61796/jaide.v1i12.1542

Abstract

Objective: This study aims to address the challenges faced by Angkringan Mdpl in analyzing sales data that affect stock management efficiency. The research seeks to identify purchasing patterns that can serve as a foundation for better inventory and marketing decisions. Method: The Apriori algorithm, a data mining technique, is employed to discover associations among sold items by calculating support and confidence values to generate valid association rules. The analysis uses transaction data from June and July 2024, with a minimum support threshold of 2% and a minimum confidence level of 5%. Results: The testing process produced five pairs of item combinations with strong and valid association rules, as confirmed by their lift values. These findings enable Angkringan Mdpl to enhance stock control by prioritizing frequently purchased products and recommending complementary items effectively. Novelty: This study provides a data-driven approach to micro-scale food business management by applying the Apriori algorithm to optimize stock planning and sales strategies in small local enterprises, demonstrating the algorithm’s practical value beyond large-scale retail contexts.
APPLICATION OF FUZZY LOGIC IN PREDICTING GROCERY STORE REVENUE Hamzah Dwi Kusuma; Hindarto Hindarto; Arief Senja Fitrani; Azmuri Wahyu Azinar
Journal of Artificial Intelligence and Digital Economy Vol. 3 No. 1 (2026): Journal of Artificial Intelligence and Digital Economy
Publisher : PT ANTIS INTERNATIONAL PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61796/jaide.v3i1.1559

Abstract

Objective: This study aims to predict grocery store income accurately using the Mamdani fuzzy logic method to address challenges caused by price fluctuations and market uncertainty in retail operations. Method: Data were collected through direct observation at a grocery store in Sidoarjo over one month, including variables such as the number of items sold, total price, and operational costs. Each variable was classified into fuzzy sets using triangular and shoulder-shaped membership functions. The fuzzy inference system consisted of 27 if–then rules, with output values determined through the Weighted Average defuzzification method. Results: Based on input data of 1,004 units sold, a total price of Rp. 14,124,500, and operational costs of Rp. 4,500,000, the system successfully predicted an income of Rp. 10,000,000. Evaluation using Mean Absolute Percentage Error (MAPE) indicated an error rate of 0%, signifying exceptional predictive accuracy. Novelty: The study demonstrates that fuzzy logic can serve as a highly effective decision-support tool for income prediction in small-scale retail businesses, providing a reliable framework for managing financial uncertainty.
Co-Authors Aprilia Widiya Umaroh Arief Senja Fitrani Hamzah Dwi Kusuma Hindarto Hindarto Novia Ariyanti Nuril Lutfi Azizah