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Contact Name
Aji Setiawan
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aji_setiawan@ft.unsada.ac.id
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+6287885025203
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Faculty of Engineering, Darma Persada University. Terusan Casablanca Streets, Pondok Kelapa, East Jakarta, Indonesia.
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INDONESIA
Journal Technology Information and Data Analytic
ISSN : -     EISSN : 30640660     DOI : https://doi.org/10.70491/tifda.v1i2.43
Journal of Technology Information and Data Analytic is a scientific journal managed by the Faculty of Engineering, Darma Persada University. TIFDA is an open access journal that provides free access to the full text of all published articles without charging access fees from readers or their institutions. Readers are entitled to read, download, copy, distribute, print, search, or link to the full text of all articles in the TIFDA Journal. This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge. Focus & Scope Informatics: Software Engineering, Information Technology, Information System, Data Mining, Multimedia, Mobile Programming, Artificial Intelligence, Computer Graphic, Computer Vision, Augmented/Virtual Reality, Games Programming, Privacy and Data Security, Security, Machine learning, Database Internet of Things Information System : Software Management, Life Cycle Development Tools.
Articles 11 Documents
Search results for , issue "Vol 2 No 2 (2025)" : 11 Documents clear
Decision Tree Regression Approach to Modeling Dengue, Tuberculosis, and Diarrhea Case Numbers Muhammad Dzaki Zahirsyah; Timor Setiyaningsih
Journal TIFDA (Technology Information and Data Analytic) Vol 2 No 2 (2025)
Publisher : Prodi Teknologi Informasi Universitas Darma Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70491/tifda.v2i2.121

Abstract

The increasing incidence of Dengue Hemorrhagic Fever (DHF), Tuberculosis (TB), and Diarrhea in a district area highlights the urgent need for a data-driven prediction system to support public health policy. This study develops a predictive model of case numbers at the sub-district level using the Decision Tree Regression algorithm within the CRISP-DM methodology. Secondary data from 2020-2023 were utilized, including disease case records (Health Office), demographic data (BPS), and environmental data (BMKG). The system was implemented as a web-based application built with PHP and Python/Flask, enabling dataset management, model retraining, and interactive visualization of predictions, complemented by risk classification and recommended interventions. Experimental results demonstrate high predictive accuracy, with R² values of 0.9130 for TB, 0.8805 for DHF, and 0.8228 for Diarrhea. Overall, the proposed system serves as an objective and measurable decision-support tool, assisting the District Health Office in formulating preventive policies more rapidly and effectively.

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