FIKRI, ALIEF MAULANA (2026) APLIKASI PENUNJUK JALAN UNTUK MENGHINDARI BANJIR MENGGUNAKAN KONSEP MULTI-AGEN PARTICLE SWARM OPTIMIZATION PADA FLUTTER. S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
Floods are natural disasters that frequently occur in various regions of Indonesia, significantly impacting community activities, particularly mobility. Existing navigation applications often lack the capability to provide alternative routes that account for real-time flood conditions. This research aims to develop a Flutterbased navigation application designed to assist users in selecting the safest routes by avoiding flood-affected areas. The method employed is Multi-Agent Particle Swarm Optimization (MAPSO), an artificial intelligence approach combining the Particle Swarm Optimization (PSO) algorithm with a multi-agent system to enhance the effectiveness of optimal route searching. Flood condition data is acquired from open sources or APIs and integrated into a digital mapping system. PSO is utilized to determine the fastest path solution, while the multi-agent concept facilitates coordination among agents to formulate adaptive routes. The application is designed to operate dynamically in response to changing field conditions. Utilizing this approach, the application is expected to provide safer and more efficient alternative routes for users, particularly during emergency situations such as floods. The results of this research offer a solution for the development of AIbased navigation systems and contribute to the realization of smart cities that are responsive to disasters. Keywords: Flutter, Flood, Multi-Agent, Particle Swarm Optimization, Smart City. Banjir merupakan bencana alam yang kerap terjadi di berbagai wilayah Indonesia dan berdampak signifikan terhadap aktivitas masyarakat, terutama dalam mobilitas. Aplikasi navigasi saat ini belum sepenuhnya mampu menyediakan rute alternatif yang mempertimbangkan kondisi banjir secara real-time. Penelitian ini bertujuan untuk mengembangkan aplikasi penunjuk jalan berbasis Flutter yang dapat membantu pengguna memilih jalur teraman dengan menghindari daerah terdampak banjir. Metode yang digunakan adalah MultiAgen Particle Swarm Optimization (MAPSO), sebuah pendekatan kecerdasan buatan yang menggabungkan algoritma Particle Swarm Optimization (PSO) dengan sistem multiagen untuk meningkatkan efektivitas pencarian rute optimal. Data kondisi banjir diperoleh dari sumber terbuka atau API dan diintegrasikan dengan sistem pemetaan digital. PSO digunakan untuk mencari solusi jalur tercepat, sedangkan konsep multiagen memungkinkan koordinasi antar agen dalam menyusun rute adaptif. Aplikasi ini dirancang agar mampu beroperasi secara dinamis sesuai perubahan kondisi lapangan. Dengan menggunakan pendekatan ini, aplikasi diharapkan mampu memberikan alternatif rute yang lebih aman dan efisien bagi pengguna, khususnya dalam situasi darurat seperti banjir. Hasil dari penelitian ini dapat menjadi solusi dalam pengembangan sistem navigasi berbasis kecerdasan buatan serta berkontribusi pada pembangunan kota pintar yang responsif terhadap bencana. Kata kunci: Flutter, Banjir, Multi-Agen, Particle Swarm Optimization, Kota Pintar.
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