Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal sudo Jurnal Teknik Informatika Multiverse: Open Multidisciplinary Journal
Sodikin, Muh Ikbal
Unknown Affiliation
Religion Arts Humanities Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Medicine & Pharmacology Physics

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

Found 1 Documents
Search
Journal : sudo Jurnal Teknik Informatika

 1 
Pengembangan Machine Learning dalam Preskripsi Obat Pasien untuk Mengurangi Kesalahan Penggunaan Obat dan Mencegah Kerugian Rumah Sakit akibat Pemakaian Obat yang Tidak Tepat Sodikin, Muh Ikbal; Utami, Ema
sudo Jurnal Teknik Informatika Vol. 4 No. 2 (2025): Edisi Juni
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/sudo.v4i2.819

Abstract

Prescribing medications to patients with chronic diseases or complications such as diabetes and stroke requires special attention, especially when patients are co-treated by an internist and a neurologist. The risk of polypharmacy and inappropriate drug administration can adversely affect patient health. This study uses the Support Vector Machine (SVM) algorithm to classify and analyze drug administration patterns in patients with chronic diseases or complications. The data used includes patient medication history, diagnosis, and prescriptions from various specialists. The SVM algorithm was implemented to identify potential overlaps or similarities in drug administration. The results of the analysis using SVM successfully identified drug administration patterns that could potentially lead to polypharmacy. The model was able to detect the similarity of drug content with 92% accuracy. The results showed that 15% of the total prescriptions analyzed had the potential for overlapping drug content. The use of the SVM algorithm in the analysis of drug prescribing proved effective in reducing the risk of polypharmacy and inappropriate drug administration in patients with chronic diseases or complications. The implementation of this machine learning-based system can help doctors make more informed prescribing decisions, improve patient safety, and optimize treatment outcomes.
Co-Authors Ema Utami