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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Roberd Saragih
Institut Teknologi Bandung
Computer Science & IT Electrical & Electronics Engineering

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Tracking control for planar nonminimum-phase bilinear control system with disturbance using backstepping Khozin Mu`tamar; Janson Naiborhu; Roberd Saragih; Dewi Handayani
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1315-1327

Abstract

This article presents the design control of a tracking problem for a non-minimum phase bilinear control system containing disturbance. The bilinear control system is assumed to have a relative degree one and non-minimum phase, which means it has unstable internal dynamics. The disturbance exists only in state variables corresponding to the control function in external dynamics. The control design was carried out using the backstepping method, which was applied to the normal form of the bilinear control system. Internal dynamics will be stabilised using virtual control to overcome unstable internal dynamics. The last step will stabilise the external dynamics and disturbance using the original control function. The simulation results show that the proposed control method can rapidly drive the output to the given trajectory. Control performance varies depending on the control parameter setting. The higher the control parameter, the better the control performance, evaluated using integral absolute error.
Genetic algorithm to optimization mobility-based dengue mathematical model Tegar Arifin Prasetyo; Roberd Saragih; Dewi Handayani
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp4535-4546

Abstract

Implementation of vaccines, mosquito repellents and several Wolbachia schemes have been proposed recently as strategies against dengue. Research showed that the use of vaccine and repellent is highly effective when implemented to individuals who are in area with high transmission rates, while the use of Wolbachia bacteria is strongly effective when implemented in area with low transmission rates. This research is to show a three-strategy combination to cope with the dengue using mathematical model. In dengue mathematical model construction, several parameters are not yet known, therefore a genetic algorithm method was used to estimate dengue model parameters. Numerical simulation results showed that the combination of three strategies were able to reduce the number of infected humans. The dynamic of the human population with the combination of three strategies on average was able to reduce the infected human population by 45.2% in immobility aspect. Furthermore, the mobility aspect in dengue model was presented by reviewing two areas; Yogyakarta and Semarang in Indonesia. The numerical solutions showed that the trend graph was almost similar between the two areas. With the maximum effort given, the combination control values decreased slowly until the 100th day.
Evaluating the efficacy of univariate LSTM approach for COVID-19 data prediction in Indonesia Tegar Arifin Prasetyo; Joshua Pratama Silitonga; Matthew Alfredo; Risky Saputra Siahaan; Roberd Saragih; Dewi Handayani; Rudy Chandra
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1353-1366

Abstract

The coronavirus disease 2019 (COVID-19) pandemic, originating in 2020, has emerged as a critical global issue due to its rapid and widespread transmission. Indonesia, among the affected nations, has taken measures to address the situation, including the development of a deep learning model for predicting future COVID-19 infection and spread. This predictive tool serves as a valuable reference for the government and stakeholders, aiding them in making informed decisions and implementing appropriate measures to contain the virus. The deep learning model employs the long short-term memory (LSTM) algorithm, chosen for its ability to recognize temporal patterns in the country’s COVID-19 data. The model creation process involves data collection, preprocessing, model architecture planning, modeling, training, and evaluation. Two LSTM models were developed: a univariate and a multivariate model. Following thorough training and evaluation, the univariate model emerged as the superior choice, boasting evaluation metrics of 16.72 for mean absolute percentage error (MAPE) and 66.36 for root mean squared error (RMSE). This model was then deployed on a publicly accessible website, presenting visualizations of past COVID-19 data and predictions of future cases through line graphs. This user-friendly platform enables the public to access and analyze the data easily.
Co-Authors Dewi Handayani Janson Naiborhu Joshua Pratama Silitonga Khozin Mu'tamar Matthew Alfredo Prasetyo, Tegar Arifin Risky Saputra Siahaan Rudy Chandra