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All Journal KOMPUTIKA - Jurnal Sistem Komputer Jurnal Teknik Informatika (JUTIF)
Gazali, M. Jumli
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Implementasi Deep Feed-Forward Neural Network pada Perancangan Chatbot Berbasis Web di UPPIK RSUD M. YUNUS Faurina, Ruvita; Gazali, M. Jumli; Herani, Icha Dwi Aprilia
Komputika : Jurnal Sistem Komputer Vol. 12 No. 2 (2023): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v12i2.8914

Abstract

ABSTRACT – The UPPIK (Customer Information and Counseling Complaint Unit) at the M. Yunus Hospital has an important role in serving visitors who come to the hospital. However, visitors often complain about the UPPIK service due to limited working hours, so there is not always staff available to provide the information needed by visitors. In addition, the ongoing Covid-19 pandemic requires people to maintain distance and reduce interaction with others. To solve this problem, an automatic chatbot has been developed to provide service as if the visitor is speaking directly to the staff without any time constraints. This research uses a Deep Feed-Forward Neural Network algorithm. The dataset used is a collection of question-answer data collected through direct observation at the UPPIK, consisting of 1464 pairs of data. The best accuracy was obtained by spliting the dataset into 80% training data (1,185 data), 10% testing data (147 data), and 10% validation data (132 data) with 300 epochs, which resulted in an accuracy of 91.98%. Evaluation of these results showed a precision value of 0.99, a recall value of 0.98, and an f1-score of 0.99. Keywords - UPPIK RSUD M. Yunus Bengkulu; Artificial Intelligence; Chatbot; Deep Feed-Forward Neural Network; Deep Learning
OPTIMIZATION OF DISEASE PREDICTION ACCURACY THROUGH ARTIFICIAL NEURAL NETWORK (ANN) ALGORITHMS IN DIAGNESE APPLICATION Faurina, Ruvita; Gazali, M. Jumli; Herani, Icha Dwi Aprilia
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 2 (2024): JUTIF Volume 5, Number 2, April 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.2.1182

Abstract

This research aims to enhance the accuracy and speed of diagnoses in the Diagnese application by implementing the ANN algorithm for disease prediction. The dataset used for experimentation was featuring binary data types, containing 131 symptoms used to predict 41 types of diseases. The Diagnese application assists patients in identifying diseases and finding suitable specialist doctors based on reported symptoms. To achieve this goal, researchers explored various machine learning algorithms, such as decision trees, SVM, Random Forest, Logistic Regression, and ANN. Through comprehensive analysis, the ANN algorithm outperforms other algorithms and showcases the best performance. The research results demonstrate that integrating this application can significantly improve diagnostic accuracy and speed, thereby potentially reducing treatment delays and enhancing patient health outcomes. The neural network model displayed exceptional accuracy across training, validation, and testing datasets, scoring 97%, 99%, and 95%, respectively. Overall, this study showcases the potential of implementing the ANN algorithm within Diagnese applications to elevate the accuracy and efficiency of disease diagnosis. The application of this model is expected to augment the efficiency and precision of the medical diagnosis process, enabling doctors to make more accurate decisions and provide more effective patient care.
Co-Authors Faurina, Ruvita Herani, Icha Dwi Aprilia