Article Per Year (5 Year)
p-Index From 2021 - 2026
0.751
P-Index
This Author published in this journals
All Journal JURNAL SISTEM INFORMASI BISNIS Jurnal Kreativitas PKM Journal of Computer Networks, Architecture and High Performance Computing TIERS Information Technology Journal
Aryasa, Jiyestha Aji Dharma
Unknown Affiliation
Computer Science & IT Economics, Econometrics & Finance Education Health Professions Medicine & Pharmacology Nursing Public Health

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : JURNAL SISTEM INFORMASI BISNIS

 1 
Prediksi Perubahan Hemodinamik Pasien setelah Pemberian Premedikasi menggunakan Machine Learning Neural Network Guna Meningkatkan Kinerja Penanganan Medis Aryasa, Jiyestha Aji Dharma; Widodo, Aris Puji; Widodo, Catur Edi
Jurnal Sistem Informasi Bisnis Vol 14, No 3 (2024): Volume 14 Nomor 3 Tahun 2024
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21456/vol14iss3pp256-266

Abstract

This research presents the development process of a machine learning neural network model for predicting hemodynamic changes in patients after premedication, aiming to enhance the performance of medical interventions. The model was constructed using 3055 patients’ data who underwent premedication processes. The developed neural network model has an architecture consisting of 10 nodes in the input layer, 10 nodes in the hidden layer, and 3 nodes in the output layer. The evaluation results of the model indicate an overall accuracy of 85%. The precision values are high for normal class predictions at 0.85 and for hypertension class predictions at 0.81 with corresponding recalls of 1 (high) and 0.6 (moderate), respectively. However, predictions for the hypotension class still have a low precision of 0.6 and a recall of 0.04 (very low) due to the significantly lower number of samples in the hypotension class compared to the normal and hypertension classes. While testing with new data, the model has successfully predicted whether patients will experience hemodynamic pressure changes. It is expected that this model can contribute to improving the performance of medical interventions, thereby minimizing undesirable hemodynamic pressure changes.
Co-Authors Agustini, Ni Komang Tri Anggaraeni, Komang Rossa Tri Aris Puji Widodo Catur Edi Widodo Dewi, Ni Putu Dita Ariani Sukma Diyu, Ida Ayu Ningrat Pangruating Gusti Ayu Dwina Mastryagung I Gede Hendrayana I KetutSwarjana, I KetutSwarjana Ida Ayu Ary Widnyani, Ida Ayu Ary Kusuma, I Gusti Agung Ari Lewar, Emanuel Iileatan Ni Luh Putu Dina Susanti Prayoga, I Made Ade Putri, Sulin Monica Raharjo, Rizki Anom Rittiruang, Amonwan