Jumlah Wisman
KESIMPULAN DAN SARAN A. Kesimpulan
Bali merupakan provinsi di Indonesia yang memiliki keunggulan dalam bidang pariwisata. Bali sebagai destinasi wisata yang cukup terkenal dalam kancah internasional membuat pariwisata menjadi sektor unggulan. Keindahan alam dan keunikan budaya menjadi daya tarik tersendiri bagi Provinsi Bali. Hal tersebut yang membuat daya tarik bagi para wisatawan untuk mengunjungi Bali untuk berwisata. Kunjungan dari para wisatawan baik wisatawan domestik maupun internasional jumlahnya terus meningkat setiap tahunnya. Wisatawan asal China merupakan satu diantara negara dengan wisatawan terbanyak kedua setelah Australia yang berkunjung ke Bali. China sebagai negara maju dengan laju perekonomian yang cukup pesat membuat China memiliki potensi untuk melakukan outbond tourism.
Hasil dari analisis dengan menggunakan metode error correction model (ECM) mengungkapkan bahwa variabel GDP per kapita yang digunakan dalam penelitian ini berpengaruh terhadap kedatangan wisatawan China ke Bali. Dalam jangka pendek variabel nilai tukar dan GDP per kapita tidak berpengaruh terhadap kedatangan wisatawan China ke Bali. Untuk nilai tukar, hal ini dimungkinkan karena adanya praktik subsidi yang dilakukan oleh pemerintah China untuk masyarakatnya yang ingin pergi berwisata ke Bali. Selain itu, subsidi untuk wisatawan China dari toko-toko yang ada di Bali
93 membuat perjalanan wisata ke Bali menjadi murah sehingga wisatawan tidak lagi perlu memperhatikan nilai tukar sebagai hambatan untuk melakukan
outbound tourism ke Bali.
Secara jangka panjang, variabel GDP per kapita berpengaruh positif dan signifikan terhadap kedatangan wisatawan China ke Bali. Kenaikan GDP per kapita China akan meningkatkan kunjungan wisatawan China ke Bali. Pendapatan wisatawan yang cukup tinggi akan memberikan banyak pilihan untuk mereka dalam memilih destinasi wisata terutama outbound tourism. Wisatawan dapat mempertimbangkan tujuan wisata dengan menyesuaikan keadaan finansial mereka.
B. Saran
Berdasarkan hasil dari penelitian yang telah dilakukan, maka terdapat beberapa saran yang mungkin dapat digunakan oleh pihak-pihak yang memiliki keterkaitan dengan industri pariwisata, baik itu pemerintah ataupun instansi terkait yakni sebagai berikut :
1. Pemerintah harus memperhatikan aspek-aspek yang memengaruhi nilai tukar rupiah terhadap nilai mata uang asing. Wisatawan cenderung memperhatikan nilai tukar untuk mempertimbangkan keputusannya untuk melakukan outbound tourism. Menjaga kestabilan nilai tukar rupiah dengna instrument moneter atau yang lain dapat menjadi penarik wisatawan dalam berwisata di Bali.
94 2. Menyediakan akomodasi dan transportasi yang terjangkau bagi para wisatawan. Biaya wisata yang terjangkau tentu akan lebih memberikan ketertarikan para wisatawan untuk mengunjungi Bali sebagai destinasi wisata internasional.
95 DAFTAR PUSTAKA
Agarwal, V. B., & Yochum, G. R. (1999). Tourist spending and race of visitors.
Journal of Travel Research, 38(2), 173–176.
https://doi.org/10.1177/004728759903800211
Agiomirgianakis, G., Serenis, D., & Tsounis, N. (2014). Exchange Rate Volatility
And Tourist Flows Into Turkey. 29(4), 700–725.
Agiomirgianakis, G., Serenis, D., & Tsounis, N. (2015). Effects of Exchange Rate Volatility on Tourist Flows into Iceland. Procedia Economics and Finance,
24(July), 25–34. https://doi.org/10.1016/s2212-5671(15)00608-5
Agista, A., & Darmawan, M. A. (2018). Pengaruh inflasi, nilai tukar dan
pertumbuhan ekonomi singapura terhadap kunjungan wisatawan singapura di indonesia. 56(1), 82–89.
Agus, W. (2013). Ekonometrika Pengantar dan Aplikasinya. Yogyakarta: UPP
STIM YKPN.
Akadiri, S. Saint, Alola, A. A., & Akadiri, A. C. (2019). The role of globalization, real income, tourism in environmental sustainability target. Evidence from Turkey. Science of the Total Environment, 687, 423–432. https://doi.org/10.1016/j.scitotenv.2019.06.139
Athari, S. A., Alola, U. V., Ghasemi, M., & Alola, A. A. (2020). The (Un)sticky role of exchange and inflation rate in tourism development: insight from the low and high political risk destinations. Current Issues in Tourism, 0(0), 1–16. https://doi.org/10.1080/13683500.2020.1798893
Baloglu, S., & Uysal, M. (1996). Market segments of push and pull motivations: A canonical correlation approach. International Journal of Contemporary
Hospitality Management, 8(3), 32–38.
https://doi.org/10.1108/09596119610115989
Bayih, B. E., & Singh, A. (2020). Modeling domestic tourism: motivations, satisfaction and tourist behavioral intentions. Heliyon, 6(9), e04839. https://doi.org/10.1016/j.heliyon.2020.e04839
Berbel-Pineda, J. M., Palacios-Florencio, B., Ramírez-Hurtado, J. M., & Santos-Roldán, L. (2019). Gastronomic experience as a factor of motivation in the tourist movements. International Journal of Gastronomy and Food Science,
18(July), 100171. https://doi.org/10.1016/j.ijgfs.2019.100171
Brida, J. G., Carrera, E. J. S., & Segarra, V. (2020). Clustering and regime dynamics for economic growth and income inequality. Structural Change and Economic
Dynamics, 52, 99–108. https://doi.org/10.1016/j.strueco.2019.09.010
Chang, C. L., & Mcaleer, M. (2012). Aggregation, heterogeneous autoregression and volatility of daily international tourist arrivals and exchange rates.
96
Japanese Economic Review, 63(3), 397–419.
https://doi.org/10.1111/j.1468-5876.2011.00563.x
Chao, C. C., Lu, L. J., Lai, C. C., Hu, S. W., & Wang, V. (2013). Devaluation, pass-through and foreign reserves dynamics in a tourism economy. Economic
Modelling, 30(1), 456–461. https://doi.org/10.1016/j.econmod.2012.09.050
Cheng, K. M. (2012). Tourism demand in Hong Kong: Income, prices, and visa restrictions. Current Issues in Tourism, 15(3), 167–181. https://doi.org/10.1080/13683500.2011.569011
De Vita, G. (2014). The long-run impact of exchange rate regimes on international tourism flows. Tourism Management, 45, 226–233. https://doi.org/10.1016/j.tourman.2014.05.001
Dritsakis, N. (2012). Tourism development and economic growth in seven Mediterranean countries: A panel data approach. Tourism Economics, 18(4), 801–816. https://doi.org/10.5367/te.2012.0140
Dritsakis, N., & Athanasiadis, S. (2000). An Econometric Model of Tourist Demand. Journal of Hospitality & Leisure Marketing, 7(2), 39–49. https://doi.org/10.1300/j150v07n02_03
Durán-Román, J. L., Cárdenas-García, P. J., & Pulido-Fernández, J. I. (2021). Tourists’ willingness to pay to improve sustainability and experience at destination. Journal of Destination Marketing and Management, 19(March 2020). https://doi.org/10.1016/j.jdmm.2020.100540
Firdaus, M. (2020). Aplikasi Ekonometrika dengan Eviews, Stata, dan R (Elviana (ed.); Cetakan-1). PT Penerbit IPB Press.
Gounopoulos, D., Petmezas, D., & Santamaria, D. (2012). Forecasting Tourist Arrivals in Greece and the Impact of Macroeconomic Shocks from the Countries of Tourists’ Origin. Annals of Tourism Research, 39(2), 641–666. https://doi.org/10.1016/j.annals.2011.09.001
Gusti, U. I. (2016). Pengantar Industri Pariwisata. CV Budi Utama. Yogyakarta. Hanafiah, M. H. M., Harun, M. F. M., Rawani, A. M., Kettani, H., & Lian, Z.
(2010). Application of Gravity Model in Estimating Tourism Demand in Malaysia. Proceedings of 2010 International Conference on Business,
Economics and Tourism Management, February 2010.
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2& SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestAp p=WOS_CPL&KeyUT=WOS:000284085200059&KeyUID=WOS:0002840 85200059
Hasan, I. (2003). Pokok-pokok materi statistik 2. Jakarta: Bumi Aksara.
He, Q., Liu, J., & Zhang, C. (2021). Exchange rate exposure and its determinants in China. China Economic Review, 65, 101579.
97 https://doi.org/10.1016/j.chieco.2020.101579
Irandoust, M. (2019). On the relation between exchange rates and tourism demand: A nonlinear and asymmetric analysis. Journal of Economic Asymmetries,
20(January), e00123. https://doi.org/10.1016/j.jeca.2019.e00123
Jangra, R., Kaushik, S. P., & Saini, S. S. (2021). An analysis of tourist’s perceptions toward tourism development: Study of cold desert destination, India.
Geography and Sustainability, 2(1), 48–58.
https://doi.org/10.1016/j.geosus.2021.02.004
Jayadi, E. K., Mahadewi, N. P. E., & Mananda, S. (2017). Karakteristik Dan Motivasi Wisatawan Berkunjung Ke Pantai Green Bowl , Ungasan , Kuta Selatan , Bali. Jurnal Analisis Pariwisata, 17(2), 69–77.
Klenosky, D. B. (2002). The “Pull” of Tourism Destinations: A Means-End Investigation. Journal of Travel Research, 40(4), 396–403. https://doi.org/10.1177/004728750204000405
Lee, J. J., Lee, Y. M., & Huang, S. J. (2018). Dynamic relationships among tourist arrivals, crime rate, and macroeconomic variables in Taiwan. Asia Pacific
Journal of Tourism Research, 23(9), 896–906.
https://doi.org/10.1080/10941665.2018.1500380
Luh, N., Suciptawati, P., Dharmawan, K., & Sudiarta, I. N. (2015). Pemodelan
Kunjungan Wisatawan Mancanegara ke Provinsi Bali.
Marrocu, E., Paci, R., & Zara, A. (2015). Micro-economic determinants of tourist expenditure: A quantile regression approach. Tourism Management, 50, 13– 30. https://doi.org/10.1016/j.tourman.2015.01.006
Meo, M. S., Chowdhury, M. A. F., Shaikh, G. M., Ali, M., & Masood Sheikh, S. (2018). Asymmetric impact of oil prices, exchange rate, and inflation on tourism demand in Pakistan: new evidence from nonlinear ARDL. Asia Pacific
Journal of Tourism Research, 23(4), 408–422.
https://doi.org/10.1080/10941665.2018.1445652
Mosahab, R., Mahamad, O., Ramayah, T., RA Nur Amalina, Ekonomi, F., Diponegoro, U., Citraluki, J., Studi, P., Fakultas, A., Dan, E., Surakarta, U. M., Efendi, P., Mandala, K., رهام،اللهدبع, Fayzollahi, S., Shirmohammadi, A., Latifian, B., 崔宇红, 楚恒亚, … Akuntansi, J. R. (2011). No Title福島県況 調査. 图书情报工作, 4(3), 410–419.
Oka, I. K., Yasa, A., Arka, S., Pembangunan, J. E., Ekonomi, F., Bisnis, D., & Udayana, U. (2015). Pengaruh Pertumbuhan Ekonomi Dan Disparitas Pendapatan Antardaerah Terhadap Kesejahteraan Masyarakat Provinsi Bali.
E-Jurnal Ekonomi Pembangunan Universitas Udaya, 4(2), 129–137.
98 Rahmiati, F., Othman, N. A., & Bonavisi, V. (2018). Travel Motivation and Domestic Tourist Satisfaction in Bali, Indonesia. International Journal of
Business Studies, 2(2), 105–110. https://doi.org/10.32924/ijbs.v2i2.58
Sabon, V. L., Perdana, M. T. P., Koropit, P. C. S., & Pierre, W. C. D. (2018). Strategi Peningkatan Kinerja Sektor Pariwisata Indonesia Pada ASEAN Economic Community. Esensi: Jurnal Bisnis Dan Manajemen, 8(2), 163–176. https://doi.org/10.15408/ess.v8i2.5928
Samimi, A. J., Sadeghi, S., & Sadeghi, S. (2013). The relationship between foreign direct investment and tourism development: Evidence from developing countries. Institutions and Economies, 5(2), 59–68.
Shao, W., & Perkins, H. (2017). Taoist and Confucian Values Evident in the Travel Motivations of Contemporary Chinese Tourists: The Importance of Self-cultivation. Journal of China Tourism Research, 13(3), 276–297. https://doi.org/10.1080/19388160.2017.1397579
Song, H., Li, G., Witt, S. F., & Fei, B. (2010). Tourism demand modelling and forecasting: How should demand be measured? Tourism Economics, 16(1), 63–81. https://doi.org/10.5367/000000010790872213
Suryani, S., & Hendriyadi, H. (2016). Metode riset kuantitatif: Teori dan aplikasi
pada penelitian bidang manajemen dan ekonomi Islam. Prenada Media.
Tang, J., Sriboonchitta, S., Ramos, V., & Wong, W. K. (2016). Modelling dependence between tourism demand and exchange rate using the copula-based GARCH model. Current Issues in Tourism, 19(9), 876–894. https://doi.org/10.1080/13683500.2014.932336
Uysal, M., & Crompton, J. L. (1984). Determinants of demand for international tourist flows to Turkey. Tourism Management, 5(4), 288–297.
https://doi.org/10.1016/0261-5177(84)90025-6
Vihikan, W. O., Putra, I. K. G. D., & Dharmaadi, I. P. A. (2017). Foreign tourist arrivals forecasting using Recurrent Neural Network Backpropagation Through Time. Telkomnika (Telecommunication Computing Electronics and
Control), 15(3), 1257–1264.
https://doi.org/10.12928/TELKOMNIKA.v15i3.5993
Walida Mustamin, S., & Undai Nurbayani, S. (2015). Pengaruh Variabel Ekonomi Makro Terhadap Kemiskinan di Kota Makassar Propinsi Sulawesi Selatan.
Jurnal Analisis, 4(2), 165–173.
Wen, J., Huang, S. (Sam), & Ying, T. (2019). Relationships between Chinese cultural values and tourist motivations: A study of Chinese tourists visiting Israel. Journal of Destination Marketing and Management, 14(January), 100367. https://doi.org/10.1016/j.jdmm.2019.100367
99 Xu, H., Ding, P., & Packer, J. (2008). Tourism research in China: Understanding the unique cultural contexts and complexities. Current Issues in Tourism,
100 LAMPIRAN
Lampiran 1 Uji Stasioneritas Level VARIABEL WISMAN
VARIABEL GDP PER KAPITA Null Hypothesis: GDP has a unit root Exogenous: Constant
Bandwidth: 12 (Newey-West automatic) using Bartlett kernel
Adj. t-Stat Prob.* Phillips-Perron test statistic -1.746816 0.4034 Test critical values: 1% level -3.531592
5% level -2.905519 10% level -2.590262 *MacKinnon (1996) one-sided p-values.
Residual variance (no correction) 0.011537 HAC corrected variance (Bartlett kernel) 0.002052 Null Hypothesis: WIS has a unit root
Exogenous: Constant
Bandwidth: 66 (Newey-West automatic) using Bartlett kernel
Adj. t-Stat Prob.* Phillips-Perron test statistic -1.861771 0.3481 Test critical values: 1% level -3.531592
5% level -2.905519 10% level -2.590262 *MacKinnon (1996) one-sided p-values.
Residual variance (no correction) 0.206958 HAC corrected variance (Bartlett kernel) 0.048151
101 VARIABEL NILAI TUKAR
Null Hypothesis: KURS has a unit root Exogenous: Constant
Bandwidth: 1 (Newey-West automatic) using Bartlett kernel
Adj. t-Stat Prob.* Phillips-Perron test statistic -1.094713 0.7133 Test critical values: 1% level -3.531592
5% level -2.905519 10% level -2.590262 *MacKinnon (1996) one-sided p-values.
Residual variance (no correction) 0.002213 HAC corrected variance (Bartlett kernel) 0.002551
102 Lampiran 2 Uji Stasioneritas Orde Pertama
VARIABEL WISMAN
VARIABEL GDP PER KAPITA Null Hypothesis: D(WIS) has a unit root Exogenous: Constant
Bandwidth: 4 (Newey-West automatic) using Bartlett kernel
Adj. t-Stat Prob.* Phillips-Perron test statistic -15.42733 0.0000 Test critical values: 1% level -3.533204
5% level -2.906210 10% level -2.590628 *MacKinnon (1996) one-sided p-values.
Residual variance (no correction) 0.138068 HAC corrected variance (Bartlett kernel) 0.123190
Null Hypothesis: D(GDP) has a unit root Exogenous: Constant
Bandwidth: 13 (Newey-West automatic) using Bartlett kernel
Adj. t-Stat Prob.* Phillips-Perron test statistic -19.54215 0.0000 Test critical values: 1% level -3.533204
5% level -2.906210 10% level -2.590628 *MacKinnon (1996) one-sided p-values.
Residual variance (no correction) 0.009136 HAC corrected variance (Bartlett kernel) 0.003036
103 VARIABEL NILAI TUKAR
Null Hypothesis: D(KURS) has a unit root Exogenous: Constant
Bandwidth: 2 (Newey-West automatic) using Bartlett kernel
Adj. t-Stat Prob.* Phillips-Perron test statistic -7.020938 0.0000 Test critical values: 1% level -3.533204
5% level -2.906210 10% level -2.590628 *MacKinnon (1996) one-sided p-values.
Residual variance (no correction) 0.002121 HAC corrected variance (Bartlett kernel) 0.001955
104 Null Hypothesis: RESID03 has a unit root
Exogenous: Constant
Bandwidth: 1 (Newey-West automatic) using Bartlett kernel
Adj. t-Stat Prob.* Phillips-Perron test statistic -7.130523 0.0000 Test critical values: 1% level -3.531592
5% level -2.905519 10% level -2.590262 *MacKinnon (1996) one-sided p-values.
Residual variance (no correction) 0.199408 HAC corrected variance (Bartlett kernel) 0.193587
Phillips-Perron Test Equation Dependent Variable: D(RESID03) Method: Least Squares
Date: 04/12/21 Time: 00:30
Sample (adjusted): 2003Q2 2019Q4 Included observations: 67 after adjustments
Lampiran 3 Uji Kointegrasi Engel Granger UJI STASIONERITAS RESIDUAL
105 Lampiran 4 Model Jangka Panjang
Dependent Variable: WIS Method: Least Squares Date: 04/12/21 Time: 02:04 Sample: 2003Q1 2019Q4 Included observations: 68
Variable Coefficient Std. Error t-Statistic Prob. C -15.70530 2.387947 -6.576903 0.0000 GDP 2.289965 0.246694 9.282595 0.0000 KURS 0.817806 0.584301 1.399631 0.1664 R-squared 0.923979 Mean dependent var 10.81712 Adjusted R-squared 0.921640 S.D. dependent var 1.627189 S.E. of regression 0.455496 Akaike info criterion 1.308253 Sum squared resid 13.48595 Schwarz criterion 1.406173 Log likelihood -41.48061 Hannan-Quinn criter. 1.347052 F-statistic 395.0160 Durbin-Watson stat 1.769824 Prob(F-statistic) 0.000000
106 Lampiran 5 Uji Estimasi ECM
MODEL JANGKA PENDEK
Dependent Variable: D(WIS) Method: Least Squares Date: 04/12/21 Time: 02:05 Sample (adjusted): 2003Q2 2019Q4 Included observations: 67 after adjustments
Variable Coefficient Std. Error t-Statistic Prob. C 0.078093 0.047089 1.658411 0.1022 D(GDP) -0.476886 0.446905 -1.067085 0.2900 D(KURS) 1.577245 0.935520 1.685955 0.0968 ECT(-1) -0.620971 0.110705 -5.609246 0.0000 R-squared 0.430061 Mean dependent var 0.070345 Adjusted R-squared 0.402921 S.D. dependent var 0.469429 S.E. of regression 0.362732 Akaike info criterion 0.867541 Sum squared resid 8.289204 Schwarz criterion 0.999165 Log likelihood -25.06263 Hannan-Quinn criter. 0.919625 F-statistic 15.84607 Durbin-Watson stat 1.786341 Prob(F-statistic) 0.000000
107 Lampiran 6 Uji Asumsi Gauss Markov
UJI MULTIKOLINIERITAS
UJI AUTOKORELASI
Breusch-Godfrey Serial Correlation LM Test:
F-statistic 0.139380 Prob. F(2,61) 0.8702 Obs*R-squared 0.304785 Prob. Chi-Square(2) 0.8587
Test Equation:
Dependent Variable: RESID Method: Least Squares Date: 04/12/21 Time: 02:06 Sample: 2003Q2 2019Q4 Included observations: 67
Presample missing value lagged residuals set to zero.
Variable Coefficient Std. Error t-Statistic Prob. C -0.004206 0.048639 -0.086475 0.9314 D(GDP) 0.115783 0.526703 0.219826 0.8267 D(KURS) 0.007613 0.949007 0.008022 0.9936 ECT(-1) -0.033898 0.202775 -0.167169 0.8678 RESID(-1) 0.020061 0.226045 0.088746 0.9296 RESID(-2) 0.078770 0.151806 0.518883 0.6057 R-squared 0.004549 Mean dependent var -2.15E-17 Adjusted R-squared -0.077045 S.D. dependent var 0.354392 S.E. of regression 0.367791 Akaike info criterion 0.922683 Sum squared resid 8.251496 Schwarz criterion 1.120119 Log likelihood -24.90989 Hannan-Quinn criter. 1.000809 F-statistic 0.055752 Durbin-Watson stat 1.772169 Prob(F-statistic) 0.997918
Variance Inflation Factors Date: 04/12/21 Time: 02:06 Sample: 2003Q1 2019Q4 Included observations: 67
Coefficient Uncentered Centered Variable Variance VIF VIF
C 0.002217 1.129116 NA D(GDP) 0.199724 1.311315 1.206376 D(KURS) 0.875198 1.041244 1.003367 ECT(-1) 0.012256 1.203475 1.202688
108 UJI HETEROKEDASTISITAS
Heteroskedasticity Test: Breusch-Pagan-Godfrey
F-statistic 0.663802 Prob. F(3,63) 0.5774 Obs*R-squared 2.052952 Prob. Chi-Square(3) 0.5615 Scaled explained SS 4.683937 Prob. Chi-Square(3) 0.1965
109 Lampiran 7 Data Penelitian
NO TAHUN WISMAN KURS
GDP PER