Pengembangan Aplikasi Prediksi Harga Tandan Buah Segar (Tbs) Kelapa Sawit Menggunakan Random Forest Regressor

Wardana, Pandu Adi Wijaya Kusuma and , Azizah Fatmawati, S.T., M.Cs. (2026) Pengembangan Aplikasi Prediksi Harga Tandan Buah Segar (Tbs) Kelapa Sawit Menggunakan Random Forest Regressor. Skripsi thesis, Universitas Muhammadiyah Surakarta.

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Abstract

Fluctuations in the price of Fresh Fruit Bunches (FFB) of oil palm, influenced by Crude Palm Oil (CPO) prices, the K Index, and global market dynamics, create uncertainty for farmers and collectors when planning sales. This study aims to develop a Kotlin-based Android application to predict the average FFB price while providing a transaction-recording feature. Historical FFB price data were preprocessed and modeled using machine learning techniques, specifically the Random Forest Regressor, with linear regression employed as a baseline model. The best-performing model was integrated into the application through a Flask-based API. Firebase was used to handle authentication and data storage, while a cloud scheduler enabled automatic prediction updates. Evaluation using MAE, RMSE, and R² indicated that the Random Forest Regressor outperformed Linear Regression, generating more stable predictions that closely reflect real-world conditions. The developed application is considered suitable as a decision support system for predicting oil palm FFB prices.

Item Type:	Thesis (Skripsi)
Uncontrolled Keywords:	Android application, machine learning, palm oil, price prediction, random forest regressor, fresh fruit bunch (FFB).
Subjects:	T Technology > Technical Information > Artificial Intelligence T Technology > Technical Information > Software. Aplication T Technology > Technical Information > Software. Aplication > Pemograman T Technology > Technical Information > Software. Aplication > Software Engineering
Divisions:	Fakultas Komunikasi dan Informatika > S1 Teknik Informatika
Depositing User:	PANDU ADI WIJAYA KUSUMA WARDANA
Date Deposited:	25 Feb 2026 02:56
Last Modified:	25 Feb 2026 02:56
URI:	http://eprints.ums.ac.id/id/eprint/143545

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