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Yanuar Zulardiansyah Arief
Universiti Malaysia Sarawak; Jakarta Global University
Computer Science & IT Electrical & Electronics Engineering

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Implementation of Dijkstra Algorithm with React Native to Determine Covid-19 Distribution Rosyid Ridlo Al Hakim; Purwono Purwono; Yanuar Zulardiansyah Arief; Agung Pangestu; Muhammad Haikal Satria; Eko Ariyanto
Sistemasi: Jurnal Sistem Informasi Vol 11, No 1 (2022): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (823.345 KB) | DOI: 10.32520/stmsi.v11i1.1667

Abstract

Since Covid-19 was declared a global pandemic because it has spread throughout the world, every effort has been made to help prevent and tackle the transmission of Covid-19, including information technology. Information technology developed to determine the shortest distance for Covid-19 cases around us needs to be developed. This research implements Dijkstra's Algorithm written in the React Native programming language to build a Covid-19 tracking application. The system can display the closest distance with a radius of at least one meter, and the test results can map the nearest radius of 41 meters and the most immediate radius of 147 meters. This system is built for the compatibility of Android OS and iOS applications with React Native programming.
Predict the thyroid abnormality particular disease likelihood of the symptoms’ certainty factor value and its confidence level: A regression model analysis Rosyid Ridlo Al-Hakim; Yanuar Zulardiansyah Arief; Agung Pangestu; Hexa Apriliana Hidayah; Aditia Putra Hamid; Aviasenna Andriand; Nur Fauzi Soelaiman; Machnun Arif; Mahmmoud Hussein Abdel Alrahman
Sistemasi: Jurnal Sistem Informasi Vol 12, No 2 (2023): 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.v12i2.2542

Abstract

The traditional expert system (TES) in the medical field commonly uses a certainty factor (CF) rule-based algorithm that can be calculated several symptoms to determine the inference solutions. The main issue for this TES included a prediction for some particular disease likelihood in the cases of new patients. CF is calculated based on symptoms related to clinical signs in patients’ diagnoses. For some reason, this TES probably won’t predict uncertain things, such as particular disease likelihood of some diseases. So, supervised learning, such as linear regression, can solve this problem. We tried to analyse the existing TES for thyroid disorders due to modelling the regression equation to predict the thyroid abnormality particular disease likelihood, based on the symptoms’ CF value and its confidence level. We used multiple linear regression (MLR) and multiple polynomial regression (MPR) to analyse the best regression model to solve the problem. The results show that the MPR model indicates the best regression model for predicting particular disease likelihood of thyroid abnormality, supported by R-squared 94.7%, R-squared adjusted 94.4%, F-value 265.925, and p-value < 0.05, which are higher than MLR model. Our study proposed a foundation for expert system development by focusing more on machine learning expert system (MLES) analysis approaches than TES.
Co-Authors Agung Pangestu Aviasenna Andriand Eko Ariyanto Hamid, Aditia Putra Hexa Apriliana Hidayah Machnun Arif Mahmmoud Hussein Abdel Alrahman Muhammad Haikal Satria Nur Fauzi Soelaiman Purwono, Purwono Rosyid Ridlo Al-Hakim