Fashion Product Sales Prediction Using Random Forest Regression Algorithm Based on Historical Data
DOI:
https://doi.org/10.69916/jkbti.v5i3.583Keywords:
Sales Prediction, Random Forest Regression, Machine Learning, CRISP-DM, Feature EngineeringAbstract
Tulip Store at Metropolitan Mall Bekasi, managed by PT Star Asia Brothers,
relies on historical sales data for evaluation and target planning. However,
sales volume fluctuations, ranging from 385 units in October 2025 to
1,122 units in March 2026, make heuristic planning unreliable. This study
builds a daily sales volume prediction model using Random Forest Regression
based on daily recapitulation data from October 2025 to March 2026. Following
the CRISP-DM framework, data preparation involved cleaning, daily
aggregation, and feature engineering to construct 11 input features categorized
into temporal indicators, historical lag variables, and rolling statistics.
The model was trained using an 80:20 train-test split with hyperparameters
n_estimators = 200, max_depth = None, and random_state = 42. Evaluation
on 36 test instances yielded a Mean Absolute Error (MAE) of 1.7649
and a Root Mean Square Error (RMSE) of 2.9740, with 91.67% of predictions
exhibiting errors within ±5 units. Feature importance analysis revealed that
sequential month (Bulan_ke, 0.385) and previous-day sales (Lag_1, 0.294)
were the most influential variables, indicating that monthly seasonal trends
and immediate sales momentum drive daily demand. The proposed model
provides robust operational decision support for daily sales evaluation, target
setting, and stock allocation.
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Copyright (c) 2026 Lusiana Situmorang, Mukhlis, Muhammad Yasir
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