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Kalsum, Dayang Nur
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Computer Science & IT Electrical & Electronics Engineering

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Implementation of Data Mining to Predict Dengue Prone Areas Using C4.5 Algorithm (Case Study: Sanggau Regency) Kalsum, Dayang Nur; Alkadri, Syarifah Putri Agustini; Istikoma, Istikoma
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 14, No 1 (2025): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v14i1.7540

Abstract

Applying data mining to predict areas prone to dengue fever is the right thing. The C4.5 algorithm or also known as the decision tree algorithm is a data mining technique that can be used to create predictive models based on historical data. This research aims to develop a prediction model for dengue-prone areas in Sanggau Regency using the C4.5 algorithm with research methodology such as problem identification, data collection, data needs analysis, system design, system development, system testing, analysis of test results system, drawing conclusions. The author can build a website application to help predict areas prone to dengue fever. The application that was built can help the Health Service in predicting dengue fever even though there is a lack of accuracy. In this context, historical data regarding dengue cases, risk factors and regional characteristics in Sanggau Regency can be used to make accurate predictions regarding dengue-prone areas. It is hoped that the creation of a prediction application for dengue-prone areas by taking data from the Sanggau Regency office from 2018-2023 will be more helpful in providing information, especially for areas experiencing dengue fever in the future
Co-Authors Alkadri, Syarifah Putri Agustini Istikoma Istikoma