Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
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This Author published in this journals
All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING The IJICS (International Journal of Informatics and Computer Science)
Dameria Esterlina Br Jabat
STMIK Pelita Nusantara
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Laundry Performance Analysis Using KPI and Linear Regression Dashboard Dameria Esterlina Br Jabat; Megaria Purba; Putri D Br Sitorus; Eva S Saragih
The IJICS (International Journal of Informatics and Computer Science) Vol. 10 No. 2 (2026): July
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v10i2.9859

Abstract

This study develops an integrated approach for measuring and forecasting the revenue performance of a small laundry business using Key Performance Indicators (KPIs), simple linear regression, and a dashboard prototype. The dataset consists of operational records from one Indonesian laundry enterprise covering January–June 2024. Daily records were validated, anonymized, and aggregated into six monthly observations containing transaction volume, customer count, laundry weight, revenue targets, and actual revenue. The KPIs comprised total revenue, month-to-month revenue growth, average revenue per transaction, and target achievement. A chronological holdout was used to evaluate the regression model: January–April formed the training set, whereas May–June formed the test set. The holdout evaluation produced MAE of Rp1,287,500, RMSE of Rp1,319,209, and MAPE of 6.31%. After validation, the model was refitted to all six observations, resulting in Ŷ = 13,216,666.67 + 1,235,714.29X with R² = 0.9198. Total observed revenue was Rp105,250,000; the highest monthly revenue was Rp21,000,000 in June; average revenue per transaction remained Rp50,000; and overall target achievement was 105.25%. The model forecast revenues of Rp21.87 million, Rp23.10 million, and Rp24.34 million for July, August, and September, respectively. The dashboard consolidates the KPI and forecast results into a concise decision-support view. Because the analysis uses one business and only six monthly observations, the forecasts should be interpreted as an exploratory trend estimate rather than a generalizable long-term model.
Predictive Analytics of Food Retail Seasonal Trends with Advanced Forecasting Modeling Anita Sindar Sinaga; Dameria Esterlina Br Jabat; Amalia Rossa; Dini Auliah
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.13173

Abstract

Food sales in food retailers generally increase on certain days. Three food categories served as data sources in this study: staple foods, ready-to-eat foods, and dairy products. Predictive analysis of seasonal trends in food retailers shows that macroeconomic factors, seasonal patterns, and religious holiday indicators play a significant role in shaping sales. Staples is the highest-revenue category, while frozen foods has the lowest volume of the three. Each highlighted sector, including dairy, is expected to experience a measurable increase in turnover over the coming period. All models exhibit varying accuracy in predicting 2026 sales compared to actual 2025 sales, evaluated using MAPE, RMSE, and MAE for key products. Moving Average and LSTM tend to be conservative, while ETS and ARIMA are more optimistic but remain limited by limited data. Random Forest also struggles to capture complex relationships. Prophet stands out for its ability to incorporate exogenous variables and handle seasonality, although caution is needed when interpreting future values. The MAPE values ranged from 1.89% to 5.34%, indicating excellent predictive accuracy, as MAPE values below 10% are generally considered high accuracy. The low RMSE and MAE values also indicate a relatively small difference between the 2026 prediction and the actual 2025 values.
Co-Authors Amalia Rossa Dini Auliah Eva S Saragih Megaria Purba Putri D Br Sitorus Sinaga, Anita Sindar R M