Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal Jiko (Jurnal Informatika dan komputer)
Tavares, Ofelia Cizela da Costa
Unknown Affiliation
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
ARTIFICIAL NEURAL NETWORK MULTI-LAYER PERCEPTRON FOR DIAGNOSIS OF DIABETES MELLITUS Tavares, Ofelia Cizela da Costa; Abidin, Abdullah Zainal
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 1 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i1.7743

Abstract

Diabetes Mellitus is a disease caused by an unhealthy lifestyle, so blood sugar is not controlled, causing complications. This disease is one of the most dangerous diseases in the world. Approximately 422 million people worldwide have diabetes, the majority living in low- and middle-income countries, and 1.5 million deaths are caused by diabetes each year. The number of cases and prevalence of diabetes have continued to increase over the last few decades. Artificial Neural Networks are a part of machine learning that can solve various problems. One of them is in terms of disease diagnosis. MLP has the advantage that learning is done repeatedly to create a durable, consistent system that works well. This research aims to implement the Multi-Layer Perceptron Artificial Neural Network method for diagnosing diabetes mellitus and then evaluating the MLP by analyzing precision, recall, f1 score, and calculating accuracy. Next, it is validated with k-fold cross-validation. In the experiment in this study, several scenarios were used, and the best scenario was obtained when using eight input layers, seven hidden layers, one output layer, and 5000 iterations. The experiment results showed that the multi-layer perceptron successfully classified diabetics and non-diabetics by percentage. Precision 77.24%, Recall 72.58%, F1 Score 76.86%, accuracy 75%, and average accuracy 78.01%.
ARTIFICIAL NEURAL NETWORK MULTI-LAYER PERCEPTRON FOR DIAGNOSIS OF DIABETES MELLITUS Tavares, Ofelia Cizela da Costa; Abidin, Abdullah Zainal; Tempola, Firman
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 1 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i1.7743

Abstract

Diabetes Mellitus is a disease caused by an unhealthy lifestyle, so blood sugar is not controlled, causing complications. This disease is one of the most dangerous diseases in the world. Approximately 422 million people worldwide have diabetes, the majority living in low- and middle-income countries, and 1.5 million deaths are caused by diabetes each year. The number of cases and prevalence of diabetes have continued to increase over the last few decades. Artificial Neural Networks are a part of machine learning that can solve various problems. One of them is in terms of disease diagnosis. MLP has the advantage that learning is done repeatedly to create a durable, consistent system that works well. This research aims to implement the Multi-Layer Perceptron Artificial Neural Network method for diagnosing diabetes mellitus and then evaluating the MLP by analyzing precision, recall, f1 score, and calculating accuracy. Next, it is validated with k-fold cross-validation. In the experiment in this study, several scenarios were used, and the best scenario was obtained when using eight input layers, seven hidden layers, one output layer, and 5000 iterations. The experiment results showed that the multi-layer perceptron successfully classified diabetics and non-diabetics by percentage. Precision 77.24%, Recall 72.58%, F1 Score 76.86%, accuracy 75%, and average accuracy 78.01%.
Co-Authors Abidin, Abdullah Zainal Tempola, Firman