Peramalan Kunjungan Wisatawan Mancanegara Menggunakan Generalized Regression Neural Networks

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Sri Herawati

Abstract

Peramalan kunjungan wisatawan mancanegara (wisman) sangat penting bagi pemerintah dan industri, karena peramalan menjadi dasar dalam perencanaan kebijakan yang efektif. Penelitian ini menggunakan Generalized Regression Neural Network (GRNN) untuk meramalkan kunjungan wisman menurut 19 pintu masuk utama dan kebangsaan, seperti: Ngurah Rai, Soekarno-Hatta, Batam, Tanjung Uban, Polonia, Juanda, Husein Sastranegara, Tanjung Balai Karimun, Tanjung Pinang, Tanjung Priok, Adi Sucipto, Minangkabau, Entikong, Adi Sumarmo, Sultan Syarif Kasim II, Sepinggan, Sam Ratulangi, Bandara Internasional Lombok, dan Makassar. GRNN memiliki kelebihan tidak memerlukan estimasi jumlah bobot jaringan untuk mendapatkan arsitektur jaringan optimal, sehingga tidak memerlukan pengaturan parameter bebas. Uji coba penelitian dilakukan dengan menggunakan spread dari 0,1 sampai 1,0. Hasil uji coba menunjukkan bahwa kinerja Peramalan terbaik dengan menggunakan spread 0,1 baik untuk data latih maupun data uji

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HERAWATI, Sri. Peramalan Kunjungan Wisatawan Mancanegara Menggunakan Generalized Regression Neural Networks. JURNAL INFOTEL, [S.l.], v. 8, n. 1, p. 35-39, may 2016. ISSN 2460-0997. Available at: <http://ejournal.st3telkom.ac.id/index.php/infotel/article/view/49>. Date accessed: 19 aug. 2019. doi: https://doi.org/10.20895/infotel.v8i1.49.
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References

[1] Rukini, P. S. Arini and E. Nawangsih, “Peramalan Jumlah Kunjungan Wisatawan Mancanegara (Wisman) ke Bali Tahun 2019: Metode ARIMA,” pp. 136–141, 2015
[2] D. C. Wu, G. Li, and H. Song “Economic Analysis Of Tourism Consumption Dynamics?: A Time-varying Parameter Demand System Approach,” Annals of Tourism Reseach., vol. 39, no. 2, pp. 667–685, 2012.
[3] S. K. Lee, “Quality differentiation and conditional spatial price competition among hotels,” Tour. Manag., vol. 46, pp. 114–122, 2015.
[4] F. Chu, “Using a logistic growth regression model to forecast the demand for tourism in Las Vegas,” TMP, vol. 12, pp. 62–67, 2014.
[5] P. Pai, K. Hung, and K. Lin. “Expert Systems with Applications Tourism demand forecasting using novel hybrid system: clustering center,” Expert Systems with applications Vol. 41, No. 8, pp. 3691 -3702, 2014.
[6] F. L. Chu, “A fractionally integrated autoregressive moving average approach to forecasting tourism demand, “ Tourism Management Vol. 29, pp. 79-88, 2008.
[7] M. Khasei, M. Bijari, and G. A. R. Ardali, “Improvement of auto-regression integrated moving average models using fuzzy logic and artificial neural network (ANNs), “ Neurocomputing Vol. 72, No.4, pp. 956-967, 2009.
[8] J. L. Ticknor, “ A Bayesian regularized artificial neural network for stock market forecasting,” Expert Systems with Applications Vol. 40, No. 14, pp. 5501 – 5506, 2013.
[9] B. Warsito, A. Rusgiyono, and M. A. Amirillah, “Pemodelan General Regression Neural Network untuk Prediksi Tingkat Pencemaran Udara Kota Semarang, “ Media Statistika, Vol 1, No. 1, pp. 43-51, 2008.
[10] L. W. Adnyani, and Subanar, “ General Regression Neural Network (GRNN) pada Peramalan Kurs Dolar dan Indeks Harga Saham Gabungan (IHSG),” Faktor Exacta, Vol. 8.No. 2, pp. 137-144, 2015.
[11] S. R. Patil, and V. N. Ghate, “Ageneralized Regression Neural Network Based on Soft Sensor for Multicomponent Distillation Column,” International Journal of Computer and Communication Engineering Vol. 4, No. 6, pp. 371-378, 2015.
[12] S. Santoso, Business Forecasting: Metode Peramalan Bisnis Masa Kini dengan Minitab dan SPSS. Elek Media Komputindo, Jakarta, 2009.
[13] Suyanto, Artificial Intelligence : Searching, Reasoning,Planning, Learning. Informatika Bandung, 2011.
[14] D. F. Specht, “ A General Regression Neural Network,“ IEEE Transaction on Neural Network, Vol. 2, No. 6, pp. 568-576, 1991.
[15] M. Theodosiou, “Disaggregation & Aggregation of Time Series Components: A Hybrid Forecasting Approach Using Generalized Regression Neural Networks and Theta Method,” Neurocomputing 74, pp. 896-905, 2011.
[16] N. Djarfour, J. Ferahtia, F. Babaia, K. Baddari, E. Said, and M. Farfour, “Seismic noise filtering based on Generalized Regression Neural Networks,“ Computers & Geosciences, Vol. 69, pp. 1-9, 2014.
[17] L. Chaofeng, C. B. Alan, W. Xiaojun, “Blind image quality assessment using a general regression neural network, “ IEEE Trans. Neural Network,Vol. 22, pp. 793-799, 2011.