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Contact Name
Ahmad Ilham
Contact Email
ahmadilham@unimus.ac.id
Phone
+6282225426654
Journal Mail Official
jichi.informatika@unimus.ac.id
Editorial Address
Jl. Kedungmundu Raya No. 18 Semarang, Jawa Tengah - Indonesia 50273
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Kota semarang,
Jawa tengah
INDONESIA
Journal of Intelligent Computing and Health Informatics (JICHI)
ISSN : 27156923     EISSN : 27219186     DOI : https://doi.org/10.26714/jichi
Journal of Intelligent Computing & Health Informatics (JICHI) was printed in March 2020. JICHI is a scientific review journal publishing that focus on exchanging information relating to intelligent computing and health informatics applied in industry, hospitals, government, and universities. All articles should include a validation of the idea presented, e.g. through case studies, experiments, or systematic comparisons with other approaches already in practice. Two types of papers are accepted: (1) A short paper that discusses a single contribution to a specific new trend or a new idea, and; (2) A long paper that provides a survey of a specific research trend using a systematic literature review (SLR) method, as well as a traditional review method. Topics of interest include, but are not limited to: Intelligent Computing Include Machine Learning; Reinforcement Learning; Computer Vision; Image Processing; Scheduling and Optimization; Bio-inspired Algorithms; Business Intelligence; Chaos theory and intelligent control systems; Robotic Intelligent; Multimedia & Application; Web and mobile Intelligence and Big Data, etc.) Health Informatics Include Electronic health record; E-Health Information; Medical Image Processing & Techniques; Data Mining in Healthcare; Bioinformatics & Biostatistics; Mobile applications for patient care; Medical Image Processing & Techniques; Hospital information systems; Document handling systems; Electronic medical record systems; standardization, and systems integration; ICT in health promotion programmes e-health Guidelines and protocols; E-learning & education in healthcare; Telemedicine Software- Portals-Devices & Telehealth; Public health & consumer informatics; Data Mining & Knowledge Discovery in Medicine; ICT for Patient empowerment; ICT for Patient safety; Medical Databanks-Databases & Knowledge Bases; Healthcare Quality assurance; Nursing Informatics; Evaluation & Technology Assessment; Home-based eHealth; Health Management Issues; Health Research; Health Economics Issues; Statistical Method for Computer Medical Decision Support Systems; Medical Informatics or medicine in general; Organizational, economic, social, clinical impact, ethical and cost-benefit aspects of IT applications in health care.
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Shortest Route Optimization with Genetic Algorithm Implementation in JavaScript. Fitriyadi, Farid
Journal of Intelligent Computing & Health Informatics Vol 6, No 1 (2025): March
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v6i1.15777

Abstract

Shortest route optimization is a classic problem in the field of combinatorial optimization with wide applications in various domains, such as transportation, logistics, and path planning. This research aims to solve the shortest route optimization problem using a genetic algorithm implemented in the JavaScript programming language. Genetic algorithms are metaheuristic methods inspired by the principles of biological evolution, such as natural selection, crossover, and mutation. In this study, the representation of individuals in the genetic algorithm is the sequence of cities visited, and the fitness function is based on the total distance traveled. The initial population is randomly generated, and the evolution of the population occurs through a series of generations by applying genetic operators. The experimental results show that after 200 generations, the genetic algorithm successfully finds a route with a total distance of 26.540475042607596, which is close to the optimal solution. The implementation of the genetic algorithm in JavaScript demonstrates its potential as an effective tool for solving the shortest route optimization problem. However, several suggestions for further development are also discussed, including parameter tuning, alternative selection strategies, variations of genetic operators, hybridization with other algorithms, and visualization of results. This research provides insights into the application of genetic algorithms in shortest route optimization and demonstrates how this technique can be implemented using JavaScript as a popular programming language.

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