Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Sistemasi: Jurnal Sistem Informasi
Nadhilla H Wahyudiana
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Using Regression Model Analysis for Forecasting the Likelihood of Particular Symptoms of COVID-19 Agung Pangestu; Ucu Sumirat; Rosyid Ridlo Al-Hakim; Muhammad Yusro; Risma Ekawati; Mahmmoud H. A. Alrahman; Machnun Arif; Achmad Muchsin; Nadhilla H Wahyudiana
Sistemasi: Jurnal Sistem Informasi Vol 13, No 1 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i1.3463

Abstract

A certainty factor (CF) rule-based technique is frequently used by traditional expert systems (TES) in the medical industry to compute several symptoms and identify the inference solutions. The primary concern for this TES was predicting the likelihood of a particular ailment in the circumstances of new patients. Based on symptoms connected to clinical indicators in patients' diagnosis, CF is estimated. This TES probably won't be able to forecast unknown things, like the possibility of a particular ailment. Therefore, supervised learning techniques like linear regression can address this issue. We attempted to analyze the current COVID-19 TES by modeling the regression equation to forecast the chance of a particular disease that is COVID-like based on the CF value and the confidence level of the symptoms. To examine the most effective regression model to address the issue, we employed multi-linear regression (MLR) and multi-polynomial regression (MPR). The findings demonstrate that the MLR and MPR models are the most accurate regression models for estimating the chance of a disease associated with COVID-like symptoms. Our work built a basis for the creation of expert systems by concentrating more on MLES (machine learning expert systems) analytical techniques than TES.
Co-Authors Agung Pangestu Machnun Arif Mahmmoud H. A. Alrahman Muhammad Yusro Risma Ekawati Rony, Zahara Tussoleha Rosyid Ridlo Al-Hakim Ucu Sumirat