Prediksi Penjualan Menggunakan Exponential Smoothing sebagai Pendukung Business Intelligence UMKM Kuliner

Ahmad Sonitoro; Afriza Meigi Zukhruf; Muhammad Azkia Maksalmyna; Muchamad Rizky Bagus Saputra; David Febrian

doi:10.47065/bit.v7i3.3062

Ahmad Sonitoro * Universitas Muhammadiyah Kudus, Indonesia
Afriza Meigi Zukhruf Universitas Muhammadiyah Kudus, Indonesia
Muhammad Azkia Maksalmyna Universitas Muhammadiyah Kudus, Indonesia
Muchamad Rizky Bagus Saputra Universitas Muhammadiyah Kudus, Indonesia
David Febrian Universitas Muhammadiyah Kudus, Indonesia

DOI: https://doi.org/10.47065/bit.v7i3.3062

Keywords: Business Intelligence; Exponential Smoothing; Forecasting; Time Series; Culinary MSME

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 (α) = 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.

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