PENERAPAN DATA MINING UNTUK IDENTIFIKASI RISIKO TERKENA PENYAKIT JANTUNG MENGGUNAKAN RANDOM FOREST

DWISARI, GALIH ANANDA and Syahbani, M. Husni (2025) PENERAPAN DATA MINING UNTUK IDENTIFIKASI RISIKO TERKENA PENYAKIT JANTUNG MENGGUNAKAN RANDOM FOREST. Undergraduate thesis, Sriwijaya University.

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Abstract

Heart disease is one of the leading causes of death worldwide, making early detection of its risks crucial. This research aims to apply data mining techniques to identify the risk of heart disease using the Random Forest algorithm. This algorithm was chosen for its ability to handle complex data and provide high accuracy in classification processes. This study produced an information system consisting of two main components: an API that contains the prediction model and handles the classification process, and a website that serves as a user interface for inputting data and viewing prediction results. The dataset used was sourced from the Kaggle platform. The model was trained with parameters optimized using Optuna. The evaluation results show that the trained model is capable of classifying heart disease risk with an average accuracy rate of 85.83%, as well as providing additional information on the most influential risk factors. It is hoped that this system can be used as an educational tool to increase public awareness of heart disease risk factors.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	data mining, penyakit jantung, Random Forest, prediksi risiko, sistem informasi
Subjects:	T Technology > T Technology (General) > T58.6-58.62 Management information systems > T58.6 General works Industrial engineering Information technology. Information systems (General) Management information systems -- Continued
Divisions:	09-Faculty of Computer Science > 57201-Information Systems (S1)
Depositing User:	Galih Ananda Dwisari
Date Deposited:	09 Jul 2025 03:35
Last Modified:	09 Jul 2025 03:35
URI:	http://repository.unsri.ac.id/id/eprint/177200

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