Skripsi

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

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Penilaian

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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.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507003792	T177200	T1772002025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1772002025

Publisher

Indralaya : Prodi Sistem Informasi, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xv, 83 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

005.360 7

Content Type

Text

Media Type

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Carrier Type

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Edition

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Subject(s)

Sistem informasi

Specific Detail Info

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Statement of Responsibility

MI

Other version/related

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