Penerapan Support Vector Regression (Svr) Dalam Memprediksi Jumlah Kunjungan Wisatawan Domestik Ke Bali
Abstract
Bali is one of the most popular tourism sectors in Indonesia. In the arena of international tourism, the island of Bali is considered as the most famous national destination compared to other destinations. The high level of domestic tourism visits to Bali annually must be strictly noted especially for local governments and Bali provincial tourism agencies in optimizing facilities, infrastructure to the safety of tourists Visit. Therefore, it takes a method that can predict the number of tourists visiting Bali annually. One method used to predict the number of tourists visiting Bali is Support Vector Regression (SVR). SVR is a method to estimate a mapped function from an input object to a real amount based on the training data. SVR has the same properties about maximizing margins and kernel tricks for mapping nonlinear data. Results of this research. Based on forecasting using MAPE value training data obtained by 11.34% while use data testing of MAPE value obtained by 7.30%. Based on the resulting MAPE value can be categorized well for the number of tourism visitors.
References
[2] “Hubungan Pertumbuhan Ekonomi dengan Sektor Pariwisata,” JEJAK J. Ekon. dan Kebijak., vol. 9, no. 1, 2016.
[3] M. A. Nizar, “Pengaruh Pariwisata Terhadap Pertumbuhan Ekonomi di Indonesia” J. Kepariwisataan Indones., vol. 6, no. 2, pp. 195 – 211, 2011.
[4] D. P. E. P. N. D. P. C. AYOMI, “Balinese Arts and Culture as Tourism Commodity in Bali Tourism Promotion Videos,” Mudra, pp. 299–307, 2017.
[5] W. Himawan, S. Sabana, and A. R. Kusmara, “Pengaruh Pariwisata pada Keberagaman Seni Rupa sebagai Modal Kultural Bali : Studi pada Komunitas dan Perhelatan Seni,” J. Urban Soc. Arts, vol. 3, no. April, pp. 96–101, 2016.
[6] J. Supranto, “Statistik teori dan aplikasi jilid 1 / oleh J. Supranto,” Stat. Teor. dan Apl. jilid 1 / oleh J. Supranto, vol. 2000, no. 2000, pp. 1–99, 2000.
[7] “Methods of Statistical Forecasting,” in Business Forecasting, 2016, pp. 81–141.
[8] L. Surtiningsih, M. T. Furqon, and S. Adinugroho, “Prediksi Jumlah Kunjungan Wisatawan Mancanegara Ke Bali Menggunakan Support Vector Regression dengan Algoritma Genetika,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 8, pp. 2578–2586, 2018.
[9] A. Pratama, R. C. Wihandika, and D. E. Ratnawati, “Implementasi Algoritme Support Vector Machine ( SVM ) untuk Prediksi Ketepatan Waktu Kelulusan Mahasiswa,” J. Pengemb. Teknol. Inf. dan Ilmu Komput. e-ISSN, 2548, 964X, vol. 2, no. 4, pp. 1704–1708, 2018.
[10] R. Amanda, H. Yasin, and P. Alan, “Analisis Support Vector Regression (SVR) dalam Memprediksi Kurs Rupiah Terhadap Dollar Amerika Serikat,” J. Gaussian, vol. 3, no. 4, pp. 849–857, 2014.
[11] M. Jändel, “A neural support vector machine,” Neural Networks, vol. 23, no. 5, pp. 607–613, 2010.
[12] S. Budi, Data Mining Teknik Pemanfaatan Data untuk Keperluan Bisnis. 2007.
[13] S. Makridakis, S. Wheelwright C, and V. E. McGee, Metode dan Aplikasi Peramalan. 1999.
