The Random Forest Regression for Price Prediction and Profitability Analysis in a Mushroom Sales Enterprise System: Random Forest Regression-Based Enterprise Sales System for Mushroom Price Forecasting and Profitability Analysis

Ulya Ilhami Arsyah; Dian Eka Putra; Defni; Ikhsan; Ainil Mardiah

doi:10.24036/jtip.v20i1.1157

Authors

Ulya Ilhami Arsyah Politeknik Negeri Padang
Dian Eka Putra Politeknik Negeri Padang
Defni Politeknik Negeri Padang
Ikhsan Politeknik Negeri Padang
Ainil Mardiah Politeknik Negeri Padang

DOI:

https://doi.org/10.24036/jtip.v20i1.1157

Keywords:

▪ Random Forest Regression, Price prediction, Profitability analysis, Enterprise system, Machine learning

Abstract

Mushroom processing enterprises managed by community groups in Limau Manis Village, Pauh District, frequently fail to survive because producers lack a reliable basis for setting selling prices and estimating profit. This study aimed to develop an enterprise system that applies the Random Forest Regression method to predict the selling price of processed mushroom products and to analyze business profitability. The dataset consisted of daily sales records of three processed products (chips, nuggets, and floss) collected from an industry partner during 2023–2024, covering product type, product weight, production cost, packaging type, season, selling price, and units sold. The research stages comprised requirements analysis, model design, evaluation, deployment, and profit analysis. The model was built with 100 estimators and evaluated using Mean Absolute Error (MAE) and the coefficient of determination (R²). The evaluation produced an MAE of Rp 2,031 (approximately 14% of the average selling price) and an R² of 0.63, indicating that the model explains 63% of selling price variation. The model was deployed as a Streamlit-based web application that generates price predictions, profit estimates, and visual analyses of profit per transaction and profit over time. The system supports mushroom entrepreneurs in making more objective, data-driven pricing decisions.

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