ANALISIS DESKRIPTIF DAN PREDIKTIF TIME SERIES PADA TRANSAKSI COFFEE SHOP MULTI PRODUK MENGGUNAKAN METODE SARIMA (STUDI KASUS: AMORE ESPRESSO MATARAM)

Descriptive And Predictive Time Series Analysis Of Multi-Product Coffee Shop Transactions Using The Sarima Method (A Case Study Of Amore Espresso Mataram)

Abstract

The rapid growth of the coffee shop industry has encouraged business owners to utilize transaction data as a basis for decision-making. However, transaction data are often underutilized to identify sales patterns and predict future revenue. This study aims to analyze sales patterns and forecast daily revenue at Amore Espresso Mataram using the Seasonal Autoregressive Integrated Moving Average (SARIMA) method. The dataset consists of 18,738 transaction records collected from January to December 2022, including the attributes of Date, Time, Net Sales, and Items. The research consists of data preprocessing, descriptive analysis, stationarity testing using the Augmented Dickey-Fuller (ADF) test, parameter identification through Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots, SARIMA model development, residual diagnostic checking, model evaluation using Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE), and revenue forecasting. The SARIMA model achieved a MAD value of IDR 961,097.80 and a MAPE value of 24.13%, indicating a moderate forecasting accuracy. Descriptive analysis shows that Saturday recorded the highest revenue among the days of the week, while Thursday recorded the lowest revenue. Among the operational periods, the morning period recorded the highest revenue, while the afternoon period recorded the lowest revenue. In addition to generating revenue forecasts, the study provides business insights regarding sales patterns based on operational periods, days of the week, and monthly trends. These findings can support operational planning and data-driven decision-making at Amore Espresso Mataram.