Article Per Year (5 Year)
p-Index From 2020 - 2025
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 2 Documents
Search

 1 
Penerapan dan Manfaat Machine Learning di Rumah Sakit Sodikin, Muh Ikbal
Multiverse: Open Multidisciplinary Journal Vol. 2 No. 2 (2023)
Publisher : Medan Resource Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57251/multiverse.v2i2.1207

Abstract

Machine learning is a highly useful method for solving various problems, streamlining task execution, and making significant contributions in various fields, including the healthcare industry. For instance, within the realm of hospitals or healthcare, the use of machine learning enables doctors to swiftly diagnose heart diseases, reducing the time required for the diagnostic process. This technology also has the capability to learn autonomously without the need for continuous supervision. However, like any other technology, machine learning has its strengths and weaknesses. Strengths of machine learning include efficient problem-solving, rapid data analysis, and autonomous learning capabilities. However, weaknesses encompass susceptibility to biased training data, lack of interpretability in complex models, and potential challenges in handling unforeseen scenarios. Balancing these aspects is crucial for maximizing the benefits of machine learning across diverse applications.
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