Jaringan Syaraf Tiruan Memprediksi Penyakit Gerd menggunakan Metode Backpropagation

The rapid development of technology in the globalization era has significantly impacted various aspects of life, including the healthcare sector. RSU Bidadari Binjai, as a healthcare provider, faces challenges in diagnosing and preventing Gastroesophageal Reflux Disease (GERD), a condition with high prevalence and serious complications such as Barrett’s esophagus and esophageal cancer. Therefore, a predictive system capable of early detection is needed to ensure quicker and more effective medical intervention. This research develops a computer-based predictive system using the backpropagation method in artificial neural networks to assist in diagnosing GERD by processing patient symptom data. The system's test results show an accuracy rate of 100% in predicting GERD complications based on the given symptoms, thus supporting more timely and accurate medical interventions.

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Publication Details

Journal
Bridge
Published
2024-09-19
DOI
https://doi.org/10.62951/bridge.v2i4.251
Primary Topic
Computer Science and Engineering
Type
article
Field-Weighted Citation Impact
0.00
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article

Jaringan Syaraf Tiruan Memprediksi Penyakit Gerd menggunakan Metode Backpropagation

Siswan Syahputra, Akim Manaor Hara Pardede, Diaz Kuncoro
Bridge
Computer Science and Engineering
article

Jaringan Syaraf Tiruan Memprediksi Penyakit Gerd menggunakan Metode Backpropagation

Siswan Syahputra, Akim Manaor Hara Pardede, Diaz Kuncoro
article en

Abstract

The rapid development of technology in the globalization era has significantly impacted various aspects of life, including the healthcare sector. RSU Bidadari Binjai, as a healthcare provider, faces challenges in diagnosing and preventing Gastroesophageal Reflux Disease (GERD), a condition with high prevalence and serious complications such as Barrett’s esophagus and esophageal cancer. Therefore, a predictive system capable of early detection is needed to ensure quicker and more effective medical intervention. This research develops a computer-based predictive system using the backpropagation method in artificial neural networks to assist in diagnosing GERD by processing patient symptom data. The system's test results show an accuracy rate of 100% in predicting GERD complications based on the given symptoms, thus supporting more timely and accurate medical interventions.

BridgeVol. 2(4)
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Computer Science and Engineering
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