Penerapan Automatic Clustering pada Fuzzy Time series pada Data Wisatawan Mancanegara Kalimantan Timur

  • Made Angga Juliartha Universitas Mulawarman
  • Ika Purnamasari Universitas Mulawarman
  • Rito Goejantoro Universitas Mulawarman

Abstract

Tourism played a significant role in national foreign exchange earnings and Regional Original Revenue (PAD), therefore accurate statistical analysis was needed as a preventive measure. Forecasting was one of the accurate statistical analyses that could assist the government in determining more effective policies in the future. The method used was the Automatic Clustering Fuzzy Logical Relationship (ACFLR) for time series data. Automatic Clustering was used to determine the length of data intervals, while the Fuzzy Logical Relationship was used to obtain forecasting results. The research objective was to forecast the number of foreign tourist visits for the next period using data on the number of foreign tourist visits to East Kalimantan from January 2023 to March 2024. The accuracy of the forecast was measured using the Mean Absolute Percentage Error (MAPE). The research findings indicated that the forecast for April 2024 was 261 visits with a MAPE value of 7.72%, indicating a very good level of accuracy. The conclusion of this research showed that the ACFLR method was effective in forecasting the number of foreign tourists, thus it could be used as a decision-making tool by local governments.

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References

Abdy, M., Syam, R., & Haryanensi, E. (2019). Metode Automatic clustering-fuzzy logical relationships pada Peramalan Jumlah Penduduk di Kota Makassar. Journal of Mathematics, Computations, and Statistics, 1(2), 193. https://doi.org/10.35580/jmathcos.v1i2.9242
Aditya, F., Devianto, D., & Maiyastri, M. (2019). Peramalan Harga Emas Indonesia Menggunakan Metode Fuzzy Time Series Klasik. Jurnal Matematika UNAND, 8(2), 45–52.
Aprilini, W., Rini, D. P., & Satria, H. (2023). Automatic Clustering and Fuzzy Logical Relationship to Predict the Volume of Indonesia Natural Rubber Export. Sriwijaya Journal of Informatics and Applications, 4(1). https://doi.org/10.36706/sjia.v4i1.51
Chen, S.-M. (1996). Forecasting enrollments based on fuzzy time series. Fuzzy sets and systems, 81(3), 311–319.
Chen, S.-M., Wang, N.-Y., & Pan, J.-S. (2009). Forecasting enrollments using automatic clustering techniques and fuzzy logical relationships. Expert Systems with Applications, 36(8), 11070–11076.
Durrah, F. I., Yulia, Y., Parhusip, T. P., & Rusyana, A. (2018). Peramalan Jumlah Penumpang Pesawat di Bandara Sultan Iskandar Muda Dengan Metode SARIMA (Seasonal Autoregressive Integrated Moving Average). Journal of Data Analysis, 1(1), 1–11.
Fauziah, N., Wahyuningsih, S., & Nasution, Y. N. (2016). Peramalan Mengunakan Fuzzy Time Series Chen (Studi Kasus: Curah Hujan Kota Samarinda). Jurnal Statistika Universitas Muhammadiyah Semarang, 4(2).
Huarng, K. (2001). Effective lengths of intervals to improve forecasting in fuzzy time series. Fuzzy sets and systems, 123(3), 387–394.
Makridakis, S., Wheelwright, S. C., McGee, V. E., Andriyanto, U. S., & Basith, A. (1999). Metode dan Aplikasi Peramalan Jilid 1 Edisi Kedua. Terjemahan oleh Ir. Hari Sumito. Jakarta: Bina Rupa Aksara.
Naba, A. (2009). Belajar cepat fuzzy logic menggunakan matlab. Yogyakarta: Andi.
Poulsen, J. R. (2009). Fuzzy time series Forecasting. Aalborg University Esbjerg.
Pratama, Y. A., & Indriani, D. (2018). Peramalan KB Baru IUD dengan Metode Automatic Clustering and Fuzzy Logical Relationship. Jurnal Biometrika dan Kependudukan, 6(2), 144. https://doi.org/10.20473/jbk.v6i2.2017.144-153
Rahmawati, R., Sarbaini, S., Rahma, A. N., Lestari, T. U., & Aryani, F. (2023). Forecasting the Number of Tuberculosis Patients Using Automatic Clustering and Fuzzy Logical Relationship Method. CAUCHY: Jurnal Matematika Murni dan Aplikasi, 8(2), 49–61. https://doi.org/10.18860/ca.v8i2.18073
Rusli, M. (2017). Dasar Perancangan Kendali Logika Fuzzy. Universitas Brawijaya Press.
Song, Q., & Chissom, B. S. (1993). Forecasting enrollments with fuzzy time series—Part I. Fuzzy sets and systems, 54(1), 1–9.
Suhadiyah, E., Sajaratud Dur, & Cipta, H. (2022). Forecasting of The Crime Rate Using Automatic Clustering and Fuzzy Logic Relationship Method in North Sumatra. International Journal of Science and Environment (IJSE), 2(1), 14–23. https://doi.org/10.51601/ijse.v2i1.14
Udayantini, K. D., Bagia, I. W., & Suwendra, I. W. (2015). Pengaruh jumlah wisatawan dan tingkat hunian hotel terhadap pendapatan sektor pariwisata di Kabupaten Buleleng Periode 2010-2013. Jurnal Manajemen Indonesia, 3(1).
Yu, H.-K. (2005). A refined fuzzy time-series model for forecasting. Physica A: Statistical Mechanics and its Applications, 346(3–4), 657–681.
Yulmaini. (2018). Logika Fuzzy: Studi Kasus & Penyelesaian Menggunakan Microsoft Excel dan Matlab. Penerbit Andi. https://books.google.co.id/books?id=1dsBEAAAQBAJ
Published
2024-11-17
How to Cite
JULIARTHA, Made Angga; PURNAMASARI, Ika; GOEJANTORO, Rito. Penerapan Automatic Clustering pada Fuzzy Time series pada Data Wisatawan Mancanegara Kalimantan Timur. EKSPONENSIAL, [S.l.], v. 15, n. 2, p. 110-118, nov. 2024. ISSN 2798-3455. Available at: <https://jurnal.fmipa.unmul.ac.id/index.php/exponensial/article/view/1326>. Date accessed: 10 dec. 2024. doi: https://doi.org/10.30872/eksponensial.v15i2.1326.