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Ashilah Tsuraya Izzati
Department of Statistics, Universitas Diponegoro, Jl. Prof. Sudarto, SH, Tembalang, Semarang, Indonesia 50275
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PERBANDINGAN ALGORITMA NEAREST NEIGHBOR DAN ANT COLONY OPTIMIZATION DALAM OPTIMASI RUTE WISATA SEMARANG BERBASIS MULTI-ATTRIBUTE UTILITY THEORY Ashilah Tsuraya Izzati; hasbi yasin; Triastuti Wuryandari
Jurnal Gaussian Vol 15, No 2 (2026): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.15.2.320-331

Abstract

Tourism route planning is an important aspect of tourism development to improve travel efficiency and destination quality. This study aims to determine priority tourist destinations and to compare the performance of tourism route optimization methods in Semarang. Priority destinations are identified using the Multi-Attribute Utility Theory (MAUT) with criteria weights determined by the Rank Order Centroid (ROC). Tourism destinations with the highest utility values are selected as priority destinations and modelled as Traveling Salesman Problem (TSP). Tourism route optimization is carried out by comparing three optimization problem-solving method, namely exact method, heuristic method, and metaheuristic method. The exact method employs the Branch and Bound (B&B) algorithm as a benchmark to obtain the optimal solution. The heuristic method uses the Nearest Neighbour (NN) algorithm and metaheuristic method uses the Ant Colony Optimization (ACO) algorithm. Algorithm performance is evaluated based on total travel distance, computation time, and relative error (RE) to the optimal solution. The result show that the NN algorithm yields a relative error of 8.66%, while the ACO algorithm achieves a lower relative error of 2.2%. This indicates that ACO algorithm produces routes that are closer to the optimal solution.
Co-Authors Hasbi Yasin Triastuti Wuryandari