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Rien Difitria
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Penerapan Support Vector Regression dan Particle Swarm Optimization untuk Prediksi Jumlah Kunjungan Wisatawan Mancanegara ke Daerah Istimewa Yogyakarta Rien Difitria; Imam Cholissodin; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 5 (2020): Mei 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The tourism sector is a contributor to national income, foreign exchange and a large provider of employment for Indonesia. With the increase in the number of foreign tourist arrivals and the value of foreign exchange tourism can strengthen the Rupiah exchange rate against the US dollar. Yogyakarta region still contributes a small foreign exchange tourism sector which is only 1.2% of all regions in Indonesia. There was an increase in visitors in 2011 which touched 508,476 visitors where in the previous year it only reached 368,906 visitors. Increasing the number of visitors accompanied by facilities and infrastructure that is inadequate or inadequate to the expectations of tourists can result in a decrease in visitor interest in the future and can threaten the economic sector of the people of Yogyakarta. Prediction of the number of tourist arrivals to the Special Region of Yogyakarta is very necessary to know the range of the number of visits in the future, so that tourism actors can prepare operations better, optimize facilities and infrastructure, and develop better marketing strategies. Prediction in this study uses the Support Vector Regression (SVR) and Particle Swarm Optimization (PSO) methods. Prediction results from this study produce the best range of SVR parameters from Complexity (C) = 100-500, Sigma (s) = 5-20, Lamda (l) = 1-5, Epsilon (e) = 0,0001-0.1 , cLR = 0.001-0.1 iteration SVR = 500, Particles = 30, PSO iteration = 50, number of features = 3 and number of prediction periods of 1 month by producing the smallest mean Absolute Percentage Error (MAPE) value of 1.088%. The MAPE value produced in this study is less than 10% so this prediction is able to predict the number of foreign tourist visits to Yogyakarta Special Region very well.
Co-Authors Imam Cholissodin Indriati Indriati