Comparison of Fuzzy Time Series Lee, Chen, and Singh on Forecasting Foreign Tourist Arrivals to Indonesia in 2023

Ade Setyani Nurmara  Sari; Ezra Putranda  Setiawan

doi:10.20956/j.v21i1.34914

Authors

Ade Setyani Nurmara Sari Universitas Negeri Yogyakarta
Ezra Putranda Setiawan Department of Mathematics Education, Yogyakarta State University

DOI:

https://doi.org/10.20956/j.v21i1.34914

Keywords:

oreign tourist arrivals, fuzzy time series, Chen fuzzy, Lee fuzzy, Singh fuzzy

Abstract

Tourism in Indonesia is one of the most reliable sectors because it can increase economic growth. Foreign tourist visits to Indonesia fluctuate every month, so forecasting needs to be done in order to help the Indonesian government in making decisions regarding the development process of the tourism industry to be right on target, efficient, and effective. The purpose of this research is to compare the Lee, Chen, and Singh fuzzy time series methods in forecasting foreign tourist visits to Indonesia.The data used in this study are monthly data on the number of foreign tourist visits to Indonesia from July 2014 to December 2023. The methods used for forecasting are Lee's fuzzy time series method, Chen's fuzzy time series, and Singh's fuzzy time series. The results of this study obtained MAPE values for in-sample data of foreign tourist visits to Indonesia using the Lee, Chen, and Singh fuzzy time series methods are 9.81%, 10.35%, and 2.77%, respectively. The MAPE values for out-sample data of foreign tourist arrivals to Indonesia using the Lee, Chen, and Singh fuzzy time series methods are 12.99%, 13.35%, 0.80%, respectively. From the MAPE value of in-sample data and out-sample data, it can be concluded that Singh's fuzzy time series has the smallest error value, so Singh's fuzzy time series is better and more accurate in forecasting foreign tourist visits to Indonesia.

Downloads

Download data is not yet available.

References

Arnita, Afnisah, N., & Marpaung, F., 2020. A Comparison of the Fuzzy Time Series Methods of Chen, Cheng and Markov Chain in Predicting Rainfall in Medan. Journal of Physics: Conference Series, 1462(1). https://doi.org/10.1088/1742-6596/1462/1/012044

BPS, 2020. Statistik Kunjungan Wisatawan Mancanegara 2019.

BPS, 2021. Statistik Kunjungan Wisatawan Mancanegara 2020.

BPS, 2022. Statistik Kunjungan Wisatawan Mancanegara 2021.

Budiawan, I., Yasin, S., Harafani, H., Kiswanto, A. D., Rusli, A. R., & Marthanti, A. S., 2024. Analisis Data Kunjungan Wisatawan Mancanegara ke Indonesia pada Era Pasca Pandemi melalui Metode Visualisasi dan Peramalan. Jurnal Pendidikan Tambusai, 8(1), 4787–4800.

Chen, S.-M., 1996. Forecasting enrollments based on fuzzy time series. In Fuzzy Sets and Systems, Vol. 81.

Fathoni, Y. M., & Wijayanto, S., 2021. Forecasting Penjualan Gas LPG di Toko Sembako Menggunakan Metode Fuzzy Time Series. JUPITER: Jurnal Penelitian Ilmu Dan Teknologi Komputer, 13(2), 87–96. https://doi.org/https://doi.org/10.5281/3541.jupiter.2021.10

Febrino, M. R., Permana, D., Syafriandi, & Amalita, N., 2023. Comparison of Forecasting Using Fuzzy Time Series Chen Model and Lee Model to Closing Price of Composite Stock Price Index. UNP Journal of Statistics and Data Science, 1(2), 74–81. https://doi.org/10.24036/ujsds/vol1-iss2/22

Habibie, A., Yahya, L., & Hasan, I. K., 2023. Perbandingan Fuzzy Time Series Lee untuk Meramalkan Nilai Tukar Petani di Provinsi Gorontalo. Jambura Journal of Probability and Statistics, 4(1), 39–46. https://doi.org/10.34312/jjps.v4i1.17453

Hadi, W., 2019. Menggali Potensi Kampung Wisata Di Kota Yogyakarta Sebagai Daya Tarik Wisatawan. Journal of Tourism and Economic, 2(2), 129–139.

Hutasuhut, A. H., Anggraeni, W., & Tyasnurita, R., 2014. Pembuatan Aplikasi Pendukung Keputusan Untuk Peramalan Persediaan Bahan Baku Produksi Plastik Blowing dan Inject Menggunakan Metode ARIMA (Autoregressive Integrated Moving Average) Di CV. Asia. JURNAL TEKNIK POMITS, 2(2), 169–174. https://doi.org/10.12962/j23373539.v3i2.8114

Intarapak, S., Supapakorn, T., & Vuthipongse, W., 2022. Classical Forecasting of International Tourist Arrivals to Thailand. Journal of Statistical Theory and Applications, 21(2), 31–43. https://doi.org/10.1007/s44199-022-00041-5

Ipan, Syaripuddin, & Nohe, D. A., 2022. Perbandingan Model Chen Dan Model Lee Pada Metode Fuzzy Time Series Untuk Peramalan Produksi Kelapa Sawit Provinsi Kalimantan Timur. Prosiding Seminar Nasional Matematika, Statistika, Dan Aplikasinya, 81–95.

Laskarjati, S. D., & Ahmad, I. S., 2022. Perbandingan Peramalan Harga Saham Menggunakan Autoregressive Integrated Moving Average (ARIMA) dan Fuzzy Time Series Markov Chain (Studi Kasus: Saham PT Indofood CBP Sukses Makmur Tbk). Jurnal Sains Dan Seni ITS, 11(6), 397–404. https://doi.org/10.12962/j23373520.v11i6.91417

Lee, M. H., Efendi, R., & Ismail, Z., 2009. Modified Weighted for Enrollment Forecasting Based on Fuzzy Time Series. In MATEMATIKA (Vol. 25, Issue 1). https://www.researchgate.net/publication/239904841

Makoni, T., Mazuruse, G., & Nyagadza, B., 2023. International tourist arrivals modelling and forecasting: A case of Zimbabwe. Sustainable Technology and Entrepreneurship, 2(1). https://doi.org/10.1016/j.stae.2022.100027

Muhammad, M., Wahyuningsih, S., & Siringoringo, M., 2021. Peramalan Nilai Tukar Petani Subsektor Peternakan Menggunakan Fuzzy Time Series Lee. Jambura Journal of Mathematics, 3(1), 1–15. https://doi.org/10.34312/jjom.v3i1.5940

Nugroho, K., 2016. Model Analisis Prediksi Menggunakan Metode Fuzzy Time Series. Jurnal Ilmiah Infokam, 12(1), 46–50. https://doi.org/https://doi.org/10.53845/infokam.v12i1.91

Nur Rais, A., Jiwana Thira, I., Nur Kholifah, D., Purwati, N., & Meisella Kristania, Y., 2020. Evaluasi Metode Forecasting Pada Data Kunjungan Wisatawan Mancanegara ke Indonesia. Jurnal Sains Dan Manajemen, 8(2).

Prasetyo, H. R., Palupi, I., & Wahyudi, B. A., 2023. Prediksi Menggunakan Model Fuzzy Time Series Studi Kasus Curah Hujan di Kabupaten Bandung. LOGIC: Jurnal Penelitian Informatika, 1(1), 8. https://doi.org/10.25124/logic.v1i1.6405

Qiu, W., Liu, X., & Li, H., 2011. A generalized method for forecasting based on fuzzy time series. Expert Systems with Applications, 38(8), 10446–10453. https://doi.org/10.1016/j.eswa.2011.02.096

Rachim, F., Tarno, & Sugito., 2020. Perbandingan fuzzy time series dengan metode Chen dan metode S.R.Singh. Jurnal Gaussian, 9(3), 306–315. https://ejournal3.undip.ac.id/index.php/gaussian/

Ramadhani, A., Wahyuningsih, S., & Siringoringo. M., 2022. Peramalan Jumlah Kunjungan Wisatawan Mancanegara Ke Indonesia Menggunakan Autoregressive Integrated Moving Average (ARIMA). Jurnal EKSPONENSIAL, 13(2), 103–112. https://doi.org/https://doi.org/10.30872/eksponensial.v13i2.1049

Sari, D. A., Nurmayanti, W. P., & Kertanah., 2023. Perbandingan Metode Fuzzy Time Series Model Chen, Lee, Dan Singh Pada Produksi Tomat Di Nusa Tenggara Barat. Prosiding Seminar Nasional Matematika, Statistika, Dan Aplikasinya.

Setiawan, W., Junati, E., & Farida, I., 2017. The Use of Triple Exponential Smoothing Method (Winter) in Forecasting Passenger of PT Kereta Api Indonesia with Optimization Alpha, Beta, and Gamma Parameters. IEEE, 198–202. https://doi.org/10.1109/ICSITech.2016.7852633

Singh, S. R., 2007. A simple method of forecasting based on fuzzy time series. Applied Mathematics and Computation, 186(1), 330–339. https://doi.org/10.1016/j.amc.2006.07.128

Singh, S. R., 2008. A computational method of forecasting based on fuzzy time series. Mathematics and Computers in Simulation, 79(3), 539–554. https://doi.org/10.1016/j.matcom.2008.02.026

Song, Q., & Chissom, B. S., 1993. Fuzzy time series and its models. In Fuzzy Sets and Systems (Vol. 54).

Xihao, S., & Yimin, L., 2008. Average-based fuzzy time series models for forecasting Shanghai compound index *. In UK World Journal of Modelling and Simulation (Vol. 1, Issue 2).