Prediction of Poultry Farmer Performance Recapitulation Values in Broiler Chicken Farming Using Random Forest Regression

DOI

10.2991/978-94-6463-926-1_46How to use a DOI?

Keywords

Information System; Machine Learning; Prediction; Random Forest Regression; RHPP

Abstract

This study aims to develop a prediction system for the Farmer’s Maintenance Result Recapitulation (RHPP) in broiler farming using the Random Forest Regression algorithm. The research addresses the delay in RHPP availability, which typically occurs several days after harvest, reducing management efficiency and transparency in partnerships between plasma farmers and core companies. The study uses one year of historical data from Misjiwati Farm, including variables such as chicken population, depletion, harvest age, chicken weight, feed quantity, Feed Conversion Ratio, and contract prices. The research methodology follows the CRISP-DM framework, covering stages of business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Data processing involved cleaning, transformation, normalization, and feature engineering to obtain relevant input features. The prediction model was built using Random Forest Regression, known for effectively handling complex numerical data. The model was trained using a 90% training and 10% testing split. Model performance was evaluated using regression metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-Squared (R²), showing accuracy with an R² of 0.999 and low prediction errors.This system allows users to predict automatically through manual input or bulk data uploads. It also provides data visualization, prediction history storage, and export options.Testing confirmed that all system functionalities worked properly. This study demonstrates that integrating machine learning into prediction improves management efficiency, accelerates evaluations, and enhances transparency in broiler farming partnerships, serving as a prototype for future livestock applications.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Andi Supriadi Chan
AU  - Helvira Audrey Dwisastri
AU  - Ferry Fachrizal
PY  - 2025
DA  - 2025/12/31
TI  - Prediction of Poultry Farmer Performance Recapitulation Values in Broiler Chicken Farming Using Random Forest Regression
BT  - Proceedings of the International Conference on Applied Science and Technology on Engineering Science 2025 (iCAST-ES 2025)
PB  - Atlantis Press
SP  - 406
EP  - 414
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-926-1_46
DO  - 10.2991/978-94-6463-926-1_46
ID  - Chan2025
ER  -