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All Journal Reaktor Telematika : Jurnal Informatika dan Teknologi Informasi Prosiding SI MaNIs (Seminar Nasional Integrasi Matematika dan Nilai-Nilai Islami) Prosiding Seminar Nasional Teknoka
ZURAIDAH FITRIAH
Jurusan Matematika, Universitas Brawijaya Malang
Arts Humanities Chemical Engineering, Chemistry & Bioengineering Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Mathematics

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Algoritma Pencarian Kunang-Kunang dengan Reduksi Langkah Acak untuk Optimasi Fungsi Tanpa Kendala Zuraidah Fitriah; Mohamad Handri Tuloli
Prosiding SI MaNIs (Seminar Nasional Integrasi Matematika dan Nilai-Nilai Islami) Vol 3 No 1 (2019): Prosiding SI MaNIs (Seminar Nasional Integrasi Matematika dan Nilai Islami)
Publisher : Mathematics Department

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (270.468 KB)

Abstract

Firefly Algorithm (FA) atau algoritma kunang-kunang adalah salah satu algoritma optimasi yang terinspirasi oleh perilaku flashing kunang-kunang. FA memiliki beberapa kelemahan seperti terperangkap ke dalam optimum lokal, parameter FA ditetapkan tanpa perubahan selama iterasi, dan tidak mengingat sejarah setiap situasi di setiap iterasi. Dalam artikel ini, diperkenalkan Firefly Photinus search Algorithm (FPA) yang merupakan variasi baru dari Firefly Algorithm (FA) yang bertujuan untuk mengatasi terjebaknya solusi dalam beberapa optimum lokal dan mempelajari sejarah setiap iterasi selama proses pencarian dengan mengembangkan koefisien reduksi arbsorpsi cahaya dan daftar pasangan (matelist). Dalam FPA ditambahkan parameter baru, yaitu reduksi langkah acak yang bertujuan untuk memaksimalkan kinerja FPA. FPA disimulasikan untuk mengoptimalkan lima fungsi tes dan dibandingkan dengan FA standar dan variasi FA lainnya yaitu Wise Step Strategy for Firefly Algorithm (WSSFA) dan Firefly Algorithm with Random Attraction (RaFA). Hasil simulasi menunjukkan bahwa FPA berhasil mengungguli FA, WSSFA, dan RaFA.
Backpropagation with BFGS Optimizer for Covid-19 Prediction Cases in Surabaya Zuraidah Fitriah; Mohamad Handri Tuloli; Syaiful Anam; Noor Hidayat; Indah Yanti; Dwi Mifta Mahanani
Telematika Vol 18, No 2 (2021): Edisi Juni 2021
Publisher : Jurusan Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v18i2.5454

Abstract

Covid-19 is a new type of corona virus called SARS-CoV-2. One of the cities that has contributed the most to infected Covid-19 cases in Indonesia is Surabaya, East Java. Predicting the Covid-19 is the important thing to do. One of the prediction methods is Artificial Neural Network (ANN). The backpropagation algorithm is one of the ANN methods that has been successfully used in various fields. However, the performance of backpropagation is depended on the architecture and optimization method. The standard backpropagation algorithm is optimized by gradient descent method. The Broyden - Fletcher - Goldfarb - Shanno (BFGS) algorithm works faster then gradient descent. This paper was predicting the Covid-19 cases in Surabaya using backpropagation with BFGS. Several scenarios of backpropagation parameters were also tested to produce optimal performance. The proposed method gives better results with a faster convergence then the standard backpropagation algorithm for predicting the Covid-19 cases in Surabaya.
Study on Particle Swarm Optimization Variant and Simulated Annealing in Vapor Liquid Equilibrium Calculation Rama Oktavian; Agung Ari Wibowo; Zuraidah Fitriah
Reaktor Volume 19 No. 2 June 2019
Publisher : Dept. of Chemical Engineering, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (415.86 KB) | DOI: 10.14710/reaktor.19.2.77-83

Abstract

Phase equilibrium calculation plays a major rule in optimization of separation process in chemical processing. Phase equilibrium calculation is still very challenging due to highly nonlinear and non-convex of mathematical models. Recently, stochastic optimization method has been widely used to solve those problems. One of the promising stochastic methods is Particle Swarm Optimization (PSO) due to its simplicity and robustness. This study presents the capability of particle swarm optimization for correlating isothermal vapor liquid equilibrium data of water with methanol and ethanol system by optimizing Wilson, Non-Random Two Liquids (NRTL), and Universal Quasi Chemical (UNIQUAC) activity coefficient model and also presents the comparison with bare-bones PSO (BBPSO) and simulated annealing (SA). Those three optimization methods were successfully tested and validated to model vapor liquid equilibrium calculation and were successfully applied to correlate vapor liquid equilibrium data for those types of systems with deviation less than 2%. In addition, BBPSO shows a consistency result and faster convergence among those three optimization methods. Keywords: Phase equilibrium, stochastic method, particle swarm optimization, simulated annealing and activity coefficient model
Prediksi Jumlah Penderita COVID-19 di Kota Malang Menggunakan Jaringan Syaraf Tiruan Backpropagation dan Metode Conjugate Gradient Syaiful Anam; Mochamad Hakim Akbar Assidiq Maulana; Noor Hidayat; Indah Yanti; Zuraidah Fitriah; Dwi Mifta Mahanani
Prosiding Seminar Nasional Teknoka Vol 5 (2020): Prosiding Seminar Nasional Teknoka ke - 5
Publisher : Fakultas Teknik, Universitas Muhammadiyah Prof. Dr. Hamka, Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

COVID-19 is an infectious disease caused by infection with a new type of corona virus. This disease is very dangerous and causes death, especially for sufferers who have congenital diseases or who have low immunity. The disease is spread through droplets from the nose or mouth that come out when a person infected with COVID-19 coughs, sneezes or talks. The prediction of the number of COVID-19 sufferers is very important to prevent and combat the spread of this disease. The backpropagation neural network is a method that can be used to solve predictive problems with good results, but its performance is influenced by the optimization method used during training. In general, the optimization method used is the gradient descent method, but this method has slow convergence. The Conjugate Gradient method has very good convergence when compared to the gradient descent method. In this paper, we will discuss how to make a prediction model for the number of COVID-19 sufferers in Malang using the backpropagation neural network and the conjugate gradient method. The experimental results show that the prediction model gets good results when compared to artificial neural networks that are optimized by the gradient descent method.
Co-Authors Agung Ari Wibowo Anam, Syaiful Dwi Mifta Mahanani Hidayat, Noor Indah Yanti Mochamad Hakim Akbar Assidiq Maulana Mohamad Handri Tuloli Mohamad Handri Tuloli Rama Oktavian