Optimalisasi JST dalam Memprediksi Kunjungan Wisatawan Mancanegara Untuk Perencanaan dan Pengembangan Pariwisata yang Efektif

 (*)Riki Winanjaya Mail (STIKOM Tunas Bangsa, Pematangsiantar, Indonesia)
 Harly Okprana (STIKOM Tunas Bangsa, Pematangsiantar, Indonesia)

(*) Corresponding Author

Submitted: September 1, 2023; Published: October 24, 2023

Abstract

Foreign tourist predictions assist the government and stakeholders in planning long- and short-term tourism strategies. Accurate information on the estimated number of tourists enables appropriate infrastructure development, efficient budget allocation and setting of relevant policies. Foreign tourists discussed in this study are foreign tourists based in ASEAN countries. This research will utilize historical data on foreign tourist arrivals from the Ministry of Tourism, the Ministry of Law and Human Rights (Directorate General of Immigration) and Mobile Positioning Data. The data that has been obtained will be processed and filtered to obtain relevant and accurate data before being used as input in the creation of an Artificial Neural Network (ANN) model. The algorithm proposed in this study is the Cyclical Rule algorithm with the optimization of the Bayesian Regulation algorithm, which can be used to solve data prediction problems. This study was analyzed using 10 (ten) architectural models, including 4-4-1, 4-5-1, 4-8-1, 4-10-1, 4-12-1, 4-15-1, 4-16-1, 4-20-1, 4-24-1, and 4-25-1. Based on the analysis, the results obtained from the 4-10-1 model with the optimization of the Bayesian Regulation algorithm as the best model with the smallest testing MSE compared to the other models, equal to 0.00786961. Based on the prediction results, foreign tourist arrivals from ASEAN countries in 2023 are expected to decrease compared to 2022. Tourism actors can take advantage of the results of this prediction to improve the quality and quantity of services provided to tourists, as well as adjust the needs of tourists with the resources available at tourist destinations.

Keywords


International Tourists; ASEAN; Optimization; Predictions; ANN

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References

I. Murapi, D. Ayu, O. Astarini, U. Bumigora, dan S. Pariwisata, “Potensi Sektor Pariwisata sebagai Strategi Pemulihan Ekonomi Provinsi NTB,” Riset Ekonomi, Akuntansi dan Perpajakan, vol. 3, no. 1, hal. 43–54, 2022, doi: 10.30812/rekan.v3i1.1844.

A. S. Sitanggang, D. S. Yusuf, M. A. Aridho, S. Wijaya, R. T. Bimantara, dan S. Yuda, “Penggunaan E-Tourism Sebagai Strategi Mempromosikan Pariwisata di Majalengka,” Altasia : Jurnal Pariwisata Indonesia, vol. 4, no. 2, hal. 52–60, 2022, doi: http://dx.doi.org/10.37253/altasia.v4i2.6782.

I. A. dwi Pratiwi, M. I. Fasa, dan Suharto, “Perspektif Ekonomi Islam Terhadap Pariwisata Halal Di Era,” Youth & Islamic Economic Journal, vol. 03, no. 01, hal. 14–27, 2022, [Daring]. Tersedia pada: http://jurnalhamfara.ac.id/index.php/yie/article/view/150

V. S. D. Soedarwo, I. R. Fuadiputra, M. R. Bustami, dan G. K. Jha, “Participatory Action Research (PAR) Model for Developing A Tourism Village in Indonesia,” Journal of Local Government Issues, vol. 5, no. 2, hal. 193–206, 2022, doi: 10.22219/logos.v5i2.21279.

Suwarni, U. L. S. Khadijah, dan H. Rachmat, “The Development Strategy of Educational Tourism At Rumah Atsiri Indonesia In The Era of Adapting to A New Normal,” Sosiohumaniora: Jurnal Ilmu-ilmu Sosial dan Humaniora, vol. 23, no. 1, hal. 97–106, 2021, doi: 10.24198/sosiohumaniora.v23i1.31668.

M. S. Sholehuddin, “Islamic T radition And Religious Culture in Halal T ourism : Empirical Evidence from Indonesia,” IBDA’: Jurnal Kajian Islam dan Budaya, vol. 19, no. 1, hal. 79–100, 2021, doi: 10.24090/ibda.v19i1.4470.

S. Wahyuni dan Rahmawati, “Point of View Research Economic Development Analysis of the Potential Sharia Tourism in West Nusa Tenggara,” Point of View Research Economic Development, vol. 2, no. 2, hal. 59–67, 2021, [Daring]. Tersedia pada: http://www.journal.accountingpointofview.id/index.php/POVRED/article/view/165

J. Sosial, I. Ayu, M. Sri, I. N. D. Astawa, I. Bagus, dan N. Mantra, “The Roles of English in the Development of Tourism and Economy in Indonesia,” SOSHUM : Jurnal Sosial dan Humaniora, vol. 11, no. 3, hal. 305–313, 2021, doi: https://doi.org/10.31940/soshum.v11i3.305-313.

S. Mouloodi, H. Rahmanpanah, S. Gohari, C. Burvill, K. Ming, dan H. M. S. Davies, “Journal of the Mechanical Behavior of Biomedical Materials What can artificial intelligence and machine learning tell us ? A review of applications to equine biomechanical research,” Journal of the Mechanical Behavior of Biomedical Materials, vol. 123, hal. 104728, 2021, doi: 10.1016/j.jmbbm.2021.104728.

P. Singh, P. Dimri, P. Gupta, dan G. P. Saroha, “Resource provisioning in scalable cloud using bio-inspired artificial neural network model,” Applied Soft Computing Journal, vol. 99, hal. 106876, 2021, doi: 10.1016/j.asoc.2020.106876.

M. Doborjeh dkk., “Personalised predictive modelling with brain-inspired spiking neural networks of longitudinal MRI neuroimaging data and the case study of dementia,” Neural Networks, vol. 144, hal. 522–539, 2021, doi: 10.1016/j.neunet.2021.09.013.

A. Kontogianni, E. Alepis, dan C. Patsakis, “Promoting smart tourism personalised services via a combination of deep learning techniques,” Expert Systems with Applications, vol. 187, no. October 2021, hal. 115964, 2022, doi: 10.1016/j.eswa.2021.115964.

K. Kaya, Y. Yılmaz, Y. Yaslan, Ş. G. Öğüdücü, dan F. Çıngı, “Demand forecasting model using hotel clustering findings for hospitality industry,” Information Processing and Management, vol. 59, no. 1, hal. 102816, 2022, doi: 10.1016/j.ipm.2021.102816.

A. Kulshrestha, V. Krishnaswamy, dan M. Sharma, “Bayesian BILSTM approach for tourism demand forecasting,” Annals of Tourism Research, vol. 83, hal. 102925, 2020, doi: 10.1016/j.annals.2020.102925.

G. Xie, Y. Qian, dan S. Wang, “Forecasting Chinese cruise tourism demand with big data: An optimized machine learning approach,” Tourism Management, vol. 82, hal. 104208, 2021, doi: 10.1016/j.tourman.2020.104208.

G. Li, D. C. Wu, M. Zhou, dan A. Liu, “The combination of interval forecasts in tourism,” Annals of Tourism Research, vol. 75, hal. 363–378, 2019, doi: 10.1016/j.annals.2019.01.010.

C. Zhang, S. Wang, S. Sun, dan Y. Wei, “Knowledge mapping of tourism demand forecasting research,” Tourism Management Perspectives, vol. 35, no. 28, hal. 100715, 2020, doi: 10.1016/j.tmp.2020.100715.

M. Cuhadar, “Modelling and Forecasting Inbound Tourism Demand to Croatia using Artificial Neural Networks: A Comparative Study,” Journal of Tourism and Services, vol. 11, no. 21, hal. 55–70, 2020, doi: 10.29036/jots.v11i21.171.

S. M. Chen, D. J. Bauer, W. M. Belzak, dan H. Brandt, “Advantages of Spike and Slab Priors for Detecting Differential Item Functioning Relative to Other Bayesian Regularizing Priors and Frequentist Lasso,” Structural Equation Modeling, vol. 29, no. 1, hal. 122–139, 2022, doi: 10.1080/10705511.2021.1948335.

BPS, “Jumlah Kunjungan Wisatawan Mancanegara ke Indonesia Menurut Kebangsaan (Kunjungan),” Publikasi Statistik Indonesia, 2023. https://www.bps.go.id/indicator/16/1821/1/jumlah-kunjungan-wisatawan-mancanegara-ke-indonesia-menurut-kebangsaan.html (diakses 15 Juni 2023).

M. Hu, H. Li, H. Song, X. Li, dan R. Law, “Tourism demand forecasting using tourist-generated online review data,” Tourism Management, vol. 90, no. January, hal. 104490, 2022, doi: 10.1016/j.tourman.2022.104490.

R. Ye dan Q. Dai, “A relationship-aligned transfer learning algorithm for time series forecasting,” Information Sciences, vol. 593, hal. 17–34, 2022, doi: 10.1016/j.ins.2022.01.071.

W. Wei dan J. Chuan, “A combination forecasting method of grey neural network based on genetic algorithm,” Procedia CIRP, vol. 109, hal. 191–196, 2022, doi: 10.1016/j.procir.2022.05.235.

S. Sun, Z. Du, C. Zhang, dan S. Wang, “Improving multi-step ahead tourism demand forecasting: A strategy-driven approach,” Expert Systems with Applications, vol. 210, no. April, hal. 118465, 2022, doi: 10.1016/j.eswa.2022.118465.

K. He, L. Ji, C. W. D. Wu, dan K. F. G. Tso, “Using SARIMA–CNN–LSTM approach to forecast daily tourism demand,” Journal of Hospitality and Tourism Management, vol. 49, no. September, hal. 25–33, 2021, doi: 10.1016/j.jhtm.2021.08.022.

Y. Dong, L. Xiao, J. Wang, dan J. Wang, “A time series attention mechanism based model for tourism demand forecasting,” Information Sciences, vol. 628, no. January, hal. 269–290, 2023, doi: 10.1016/j.ins.2023.01.095.

F. Mu’minin dan A. Gunaryati, “Prediksi Kunjungan Wisatawan Mancanegara Melalui Pintu Udara Menggunakan ARIMA, Glmnet, dan Prophet,” Februari, vol. 21, no. 1, hal. 149–156, 2022, doi: 10.33633/tc.v21i1.5695.

H. Mukhtar, R. Gunawan, A. Hariyanto, Syahril, dan Wide Mulyana, “Peramalan Kedatangan Wisatawan ke Suatu Negara Menggunakan Metode Support Vector Machine (SVM),” Jurnal CoSciTech (Computer Science and Information Technology), vol. 3, no. 3, hal. 274–282, 2022, doi: 10.37859/coscitech.v3i3.4211.

M. A. Ridla, N. Azise, dan M. Rahman, “Perbandingan Model Time Series Forecasting Dalam Memprediksi Jumlah Kedatangan Wisatawan Dan Penumpang Airport,” Jurnal Sistem Informasi dan Sistem Komputer, vol. 8, no. 1, hal. 1–14, 2023, doi: 10.51717/simkom.v8i1.103.

F. Riestiansyah, D. Damayanti, M. Reswara, dan R. Susetyoko, “Perbandingan metode ARIMA dan ARIMAX dalam Memprediksi Jumlah Wisatawan Nusantara di Pulau Bali,” Jurnal Infomedia: Teknik Informatika, Multimedia & Jaringan, vol. 7, no. 2, hal. 58–62, 2022, doi: 10.30811/jim.v7i2.3336.

F. Fatimatuzzahra, R. Hammad, A. Z. Amrullah, dan P. Irfan, “Optimasi Neural Network Dengan Menggunakan Algoritma Genetika Untuk Prediksi Jumlah Kunjungan Wisatawan,” JTIM : Jurnal Teknologi Informasi dan Multimedia, vol. 3, no. 4, hal. 227–235, 2022, doi: 10.35746/jtim.v3i4.190.

W. Saputra dkk., “Implementation of ANN for Predicting the Percentage of Illiteracy in Indonesia by Age Group,” Journal of Physics: Conference Series, vol. 1255, no. 012043, hal. 1–6, 2019, doi: 10.1088/1742-6596/1255/1/012043.

M. K. Z. Sormin, P. Sihombing, A. Amalia, A. Wanto, D. Hartama, dan D. M. Chan, “Predictions of World Population Life Expectancy Using Cyclical Order Weight / Bias,” Journal of Physics: Conference Series, vol. 1255, no. 012017, hal. 1–6, 2019, doi: 10.1088/1742-6596/1255/1/012017.

S. Setti dan A. Wanto, “Analysis of Backpropagation Algorithm in Predicting the Most Number of Internet Users in the World,” JOIN (Jurnal Online Informatika), vol. 3, no. 2, hal. 110–115, 2018, doi: 10.15575/join.

A. Wanto, S. Defit, dan A. P. Windarto, “Algoritma Fungsi Perlatihan pada Machine Learning berbasis ANN untuk Peramalan Fenomena Bencana,” RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 5, no. 2, hal. 254–264, 2021, doi: https://doi.org/10.29207/resti.v5i2.3031.

M. O. Shabani dan A. Mazahery, “Prediction Performance of Various Numerical Model Training Algorithms in Solidification Process of A356 Matrix Composites,” Indian Journal of Engineering and Materials Sciences, vol. 19, no. 2, hal. 129–134, 2012, [Daring]. Tersedia pada: file:://WOS:000305550200006

M. Julham, S. Sumarno, F. Anggraini, A. Wanto, dan S. Solikhun, “Penerapan Jaringan Syaraf Tiruan dalam Memprediksi Tingkat Kriminal di Kabupaten Simalungun Menggunakan Algoritma Backpropagation,” BRAHMANA: Jurnal Penerapan Kecerdasan Buatan, vol. 1, no. 1, hal. 64–73, 2019, doi: 10.30645/brahmana.v1i1.9.

N. Z. Purba, A. Wanto, dan I. O. Kirana, “Implementation of ANN for Prediction of Unemployment Rate Based on Urban Village in 3 Sub-Districts of Pematangsiantar,” International Journal of Information System & Technology, vol. 3, no. 1, hal. 107–116, 2019.

I. C. Saragih, D. Hartama, dan A. Wanto, “Prediksi Perkembangan Jumlah Pelanggan Listrik Menurut Pelanggan Area Menggunakan Algoritma Backpropagation,” Building of Informatics, Technology and Science (BITS), vol. 2, no. 1, hal. 48–54, 2020.

M. Syafiq, D. Hartama, I. O. Kirana, I. Gunawan, dan A. Wanto, “Prediksi Jumlah Penjualan Produk di PT Ramayana Pematangsiantar Menggunakan Metode JST Backpropagation,” JURIKOM (Jurnal Riset Komputer), vol. 7, no. 1, hal. 175, 2020, doi: 10.30865/jurikom.v7i1.1963.

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