APRIORI ALGORITHM AND TIME SERIES FOR PURCHASING PATTERNS AND SALES FORECASTING: MATAHARI CIPUTRA JAKARTA

Taufiq Muhaziz; Hendri Ardiansyah

doi:10.34288/jri.v8i4.604

Authors

Taufiq Muhaziz Universitas Pamulang
Hendri Ardiansyah Universitas Pamulang

(*) Corresponding Author

DOI:

https://doi.org/10.34288/jri.v8i4.604

Keywords:

Apriori, Association Rules, Eid Al-Fitr, Inventory Planning, Time Series

Abstract

Retail transaction data can be used to explain purchasing patterns and forecast future sales, but the two analyses are often applied separately. This study examines purchasing patterns and product sales forecasts for the Eid shopping period at Matahari Department Store Ciputra Jakarta. The dataset contained 389,588 product-level records from January-April 2025 and January-April 2026, representing 194,854 transactions. The Apriori algorithm was applied with a minimum support of 1% and a minimum confidence of 10%. Daily sales of the 50 best-selling products were forecast using ARIMA(2,1,2), SARIMA(2,1,2)(1,1,1,7), and Prophet with weekly seasonality. Apriori produced 59 frequent itemsets and 24 association rules. The strongest selected rule was Socks to Sneakers with a lift of 11.39, while Hoodie to Jeans reached 43.63% confidence. ARIMA produced the lowest average errors, with MAE 42.46, RMSE 76.86, and MAPE 102.14%, compared with SARIMA and Prophet. The relatively high MAPE indicates that the forecast should be treated as a comparative estimate rather than an exact value. The 90-day forecast was combined with association rules to generate stock priorities, bundling, and product-display recommendations. Manual recalculation of selected Apriori rules and ARIMA evaluation metrics matched the system output, while all 16 black-box scenarios were successfully executed.

Keywords: Apriori; association rules; Eid sales; purchasing patterns; time series forecasting

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