Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal Journal of Information Systems Engineering and Business Intelligence The Indonesian Journal of Computer Science
Sasmito, Ayomi
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Design and Development of a Hybrid Tricopter Fixed-Wing UAV for Precision Agriculture Febrianto, Rokhmat; Yeoh, Jessie Charydon; Putra, I Gede Arinata Kusuma; Sasmito, Ayomi; Alfiansyah, Agung
The Indonesian Journal of Computer Science Vol. 13 No. 6 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i6.4489

Abstract

Precision Agriculture (PA) relies on innovative technologies to enhance efficiency and sustainability in agricultural practices. This study focuses on the design, simulation, and evaluation of a Hybrid Tricopter VTOL UAV tailored for PA applications. The UAV combines hover and fixed-wing flight modes, enabling versatility in data collection and farmland monitoring. Through rigorous simulations, the hover mission demonstrated the effectiveness of PID controllers in stabilizing roll, pitch, and yaw dynamics, achieving high positional accuracy with minimal error rates. The transition mission validated the UAV’s adaptability, showcasing smooth transitions between flight modes under varying tilt rates. Additionally, electronic component simulations confirmed the propulsion system operates efficiently within thermal and electrical limits, ensuring durability and energy efficiency. The findings highlight the UAV’s reliability, adaptability, and operational readiness, laying a foundation for advanced UAV applications in PA and beyond. This work underscores the potential of UAVs in optimizing agricultural productivity and sustainability.
Comparison of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for Estimating the Susceptible-Exposed-Infected-Recovered (SEIR) Model Parameter Values Sa'adah, Aminatus; Sasmito, Ayomi; Pasaribu, Asysta Amalia
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 2 (2024): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.2.290-301

Abstract

Background: The most commonly used mathematical model for analyzing disease spread is the Susceptible-Exposed-Infected-Recovered (SEIR) model. Moreover, the dynamics of the SEIR model depend on several factors, such as the parameter values. Objective: This study aimed to compare two optimization methods, namely genetic algorithm (GA) and particle swarm optimization (PSO), in estimating the SEIR model parameter values, such as the infection, transition, recovery, and death rates. Methods: GA and PSO algorithms were compared to estimate parameter values of the SEIR model. The fitness value was calculated from the error between the actual data of cumulative positive COVID-19 cases and the numerical data of cases from the solution of the SEIR COVID-19 model. Furthermore, the numerical solution of the COVID-19 model was calculated using the fourth-order Runge-Kutta algorithm (RK-4), while the actual data were obtained from the cumulative dataset of positive COVID-19 cases in the province of Jakarta, Indonesia. Two datasets were then used to compare the success of each algorithm, namely, Dataset 1, representing the initial interval for the spread of COVID-19, and Dataset 2, representing an interval where there was a high increase in COVID-19 cases. Results: Four parameters were estimated, namely the infection rate, transition rate, recovery rate, and death rate, due to disease. In Dataset 1, the smallest error of GA method, namely 8.9%, occurred when the value of , while the numerical error of PSO was 7.5%. In Dataset 2, the smallest error of GA method, namely 31.21%, occurred when , while the numerical error of PSO was 3.46%. Conclusion: Based on the parameter estimation results for Datasets 1 and 2, PSO had better fitting results than GA. This showed PSO was more robust to the provided datasets and could better adapt to the trends of the COVID-19 epidemic.   Keywords: Genetic algorithm, Particle swarm optimization, SEIR model, COVID-19, Parameter estimation.  
Co-Authors Alfiansyah, Agung Febrianto, Rokhmat Pasaribu, Asysta Amalia Putra, I Gede Arinata Kusuma Sa'adah, Aminatus Yeoh, Jessie Charydon