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All Journal The Journal of Experimental Life Sciences (JELS) MAJAMATH: Jurnal Matematika dan Pendidikan Matematika
Tuloli, Mohamad Handri
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Biochemistry, Genetics & Molecular Biology Education Immunology & microbiology Mathematics Medicine & Pharmacology Neuroscience

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PENYELESAIAN NUMERIK MASALAH SYARAT BATAS ROBIN PADA PERSAMAAN DIFERENSIAL CAUCHY-EULER Habibah, Ummu; Tuloli, Mohamad Handri; Rimanada, Viva; Ferreira, Tomas Goncalves
MAJAMATH: Jurnal Matematika dan Pendidikan Matematika Vol 3 No 1 (2020): Vol. 3 No. 1 Maret 2020
Publisher : Prodi Pendidikan matematika Universitas Islam Majapahit (UNIM), Mojokerto, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (784.337 KB) | DOI: 10.36815/majamath.v3i1.615

Abstract

This research studied how the numerical solution of the Cauchy-Euler differential equation with Robin boundary conditions. There were several numerical methods that can be used to get the numerical solution of a boundary value problem, namely the finite-difference method, the shooting method, the collocation method, and others. In this study, the numerical solution of Robin's boundary condition problem was obtained by the center finite-difference and the shooting methods. From the two methods, the numerical error was compared to the exact solution. The simulation results shown that the shooting method produces a better numerical solution for approximating the completion of the Cauchy-Euler differential equation than the finite-difference method since it produced smaller numerical errors.
Particle Swarm Optimization – Extreme Learning Machine with Decreasing Inertia Weight for COVID-19 Prediction in Surabaya Tuloli, Mohamad Handri; Anam, Syaiful; Nur Shofianah
The Journal of Experimental Life Science Vol. 13 No. 3 (2023)
Publisher : Graduate School, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jels.2023.013.03.01

Abstract

COVID-19 has spread all throughout the world, even to Indonesia. Surabaya becomes one of Indonesia's major cities where COVID-19 is fast spreading, culminating in a large number of positive cases and over 1000 deaths from the disease by November 2020. The number of positive COVID-19 cases predicted can be utilized to limit hospital facility availability and develop plans and policies for tackling the illness outbreak. One of the many prediction systems identified is the Extreme Learning Machine (ELM). ELM has a quick and precise training speed. However, the performance of ELM depends on the number of neurons. When the number of neurons is not precisely specified, prediction accuracy suffers. Particle Swarm Optimization (PSO) has the ability to optimize the number of node ELM neurons so the ELM can achieve better results. The number of neurons is determined using Particle Swarm Optimization (PSO) with decreased inertia weight. As a result, this research proposes predicting COVID-19 instances in Surabaya using a hybrid of PSO and ELM (PSO-ELM) with decreased inertia weight. The studies reveal that the offered techniques with different activation functions work comparably well in predicting COVID-19 instances in Surabaya. The best MAPE is achieved using the sigmoid activation function with the number of hidden layer nodes around . Keywords: Covid-19, Optimization, Prediction, PSO-ELM.
Co-Authors Ferreira, Tomas Goncalves Nur Shofianah Rimanada, Viva Syaiful Anam Ummu Habibah