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Afriza Meigi Zukhruf
Universitas Muhammadiyah Kudus
Computer Science & IT

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Prediksi Penjualan Menggunakan Exponential Smoothing sebagai Pendukung Business Intelligence UMKM KulinerĀ  Ahmad Sonitoro; Afriza Meigi Zukhruf; Muhammad Azkia Maksalmyna; Muchamad Rizky Bagus Saputra; David Febrian
Bulletin of Information Technology (BIT) Vol 7 No 3: September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i3.3062

Abstract

Micro, Small, and Medium Enterprises (MSMEs) in the culinary sector face challenges in sales management due to unpredictable demand fluctuations. This study aims to apply the Exponential Smoothing method as part of a time series approach to predict daily sales of fried chicken and catfish pecel, and integrate the results into a Business Intelligence (BI) framework to support operational and strategic decision-making. Primary data were collected through unstructured interviews with the business owner during Januari-December 2025, with a total of 313 operating days. Modeling used alpha (Ī±) = 0.3 selected through trial and error to obtain the best accuracy. Model evaluation used MAPE, MAE, and RMSE metrics. Results showed MAPE values of 19.33% for fried chicken (good accuracy) and 21.12% for catfish pecel (reasonably acceptable). MAE values were 3.58 and 3.25 portions/day respectively, while RMSE values were 4.42 and 4.09. Sales forecasts for January 2026 projected 23 portions/day for fried chicken and 19 portions/day for catfish pecel. Prediction results were visualized in a Business Intelligence dashboard using Microsoft Excel, producing data-driven managerial recommendations for production planning and inventory management.
Co-Authors Ahmad Sonitoro David Febrian Muchamad Rizky Bagus Saputra Muhammad Azkia Maksalmyna