Peramalan Nilai Tukar Rupiah Terhadap Dolar Singapura dengan Pendekatan Average Based Fuzzy Time Series Markov Chain

Syifa Ur Rahmah(1), Ayu Pratika Putri(2), Siswanto Siswanto(3*), Anisa Kalondeng(4)

(1) 
(2) Department of Statistic, Universitas Hasanuddin, Indonesia
(3) Department of Statistic, Universitas Hasanuddin, Indonesia
(4) Department of Statistic, Universitas Hasanuddin, Indonesia
(*) Corresponding Author

Abstract


Exchange rates, representing a country's currency value in terms of another, signify currency relationships between nations. Indonesia's strong economic ties with Singapore see the Singapore Dollar boasting the highest exchange rate against the Indonesian Rupiah in Asia. The Rupiah-Singapore Dollar exchange rate is marked by fluctuations, necessitating precise forecasts. One effective forecasting method is the average-based Fuzzy Time Series (FTS) Markov Chain. This method calculates intervals based on averages and leverages the Markov Chain concept, employing a transition probability matrix to enhance accuracy. The average-based FTS Markov Chain predicts the Rupiah-Singapore Dollar exchange rate from May 16, 2023, to October 13, 2023, delivering an impressively low Mean Absolute Percentage Error (MAPE) of 0.3642%. Notably, the forecast for October 14, 2023, is 11.583.73. Consistently, this method, blending interval formation through FTS and probability transition matrix from the Markov Chain, provides reliable forecasts. These insights are invaluable for decision-makers, empowering them to proactively address potential fluctuations that might contribute to inflationary pressures on Indonesia's economy.

Full Text:

PDF (Indonesian)

References


P. R. Iswardani, M. Sudarma, and L. Jasa, “Peramalan Nilai Tukar Rupiah Terhadap Mata Uang Negara Asia Menggunakan Metode Quantum Neural Network,” Majalah Ilmiah Teknologi Elektro, vol. 20, no. 1, p. 153, Apr. 2021.

D. Yulia Hidayah, “Peramalan Nilai Tukar Rupiah terhadap Dollar Amerika dengan Metode Fuzzy Time Series (FTS) Markov Chain,” UNNES Journal of Mathematics, vol. 10, no. 2, pp. 85–95, 2021.

Bank Indonesia, “Statistik Utang Luar Negeri Indonesia (SULNI),” 2023.

I. N. Hidayati, M. Al Haris, and T. W. Utami, “Metode Average Based Fuzzy Time Series Markov Chain pada Data Laju Inflasi di Indonesia Average Based Fuzzy Time Series Markov Chain Method on Inflation Rate Data in Indonesia,” Prosiding Seminar Nasional UNIMUS, vol. 5, pp. 580–597, 2022.

S. Damayanti, S. Yosmar, and N. Afandi, “Implementation of Fuzzy Time Series Chen for Forecasting Indonesian Oil and Gas Imports Value,” BAREKENG: Jurnal Ilmu Matematika dan Terapan, vol. 17, no. 2, pp. 0685–0694, Jun. 2023.

S. Hariyanto, Y. D. Sumanto, S. Khabibah, and Zaenurrohman, “Average-Based Fuzzy Time Series Markov Chain Based on Frequency Density Partitioning,” J Appl Math, vol. 2023.

K. Aritonang et al., “Analisis Pertambahan Pasien COVID-19 di Indonesia Menggunakan Metode Rantai Markov,” Jurnal Rekayasa Sistem Industri, vol. 9, no. 2, pp. 69–76, Jul. 2020.

Y. R. Safitri, “Comparison of Fuzzy Time Series Markov Chain and Average Based Fuzzy Time Series Markov Chain in Forecasting Composite Stock Price Index,” J. Ris. & Ap. Mat, vol. 06, no. 02, pp. 193–203, 2022.

A. Surahman and R. B. M. Tua, “Pengaruh Dolar Singapura dan Fluktuasi Inflasi Terhadap Pergerakan Harga IHSG,” Jurnal SEKURITAS, vol. 4, no. 1, 2020.

B. Pradito and D. Silvi Purnia, “Komparasi Algoritma Linear Regression dan Neural Network untuk Memprediksi Nilai Kurs Mata Uang,” Jurnal Sains dan Manajemen, vol. 10, no. 2, 2022.

Bank Indonesia, “Kurs Transaksi Bank Indonesia,” 2023.

I. Fikri, Admi Salma, Dodi Vionanda, and Zilrahmi, “Comparison of Fuzzy Time Series Markov Chain and Fuzzy Time Series Cheng to Predict Inflation in Indonesia,” UNP Journal of Statistics and Data Science, vol. 1, no. 4, pp. 306–312, Aug. 2023.

S. Rusdiana, D. Febriana, I. Maulidi, and V. Apriliani, “Comparison of Weighted Markov Chain and Fuzzy Time Series-Markov Chain Methods in Air Temperature Prediction in Banda Aceh City,” BAREKENG: Jurnal Ilmu Matematika dan Terapan, vol. 17, no. 3, pp. 1301–1312, Sep. 2023.

E. Habinuddin, “Penerapan Fuzzy Time Series untuk Memprediksi Curah Hujan Kota Bandung,” Jurnal Digit, vol. 12, no. 2, pp. 115–122, 2022.

R. R. Fadillah, D. Permana, Y. Kurniawati, and A. Salma, “The Application of the Fuzzy Time Series-markov Chain Method to the Rupiah Exchange Rate Against the US Dollar (USD),” UNP Journal of Statistics and Data Science, vol. 1, no. 4, pp. 369–376, Aug. 2023.

S. Handoyo and A. P. S. Prasojo, Sistem Fuzzy Terapan dengan Software R. Malang: Universitas Brawijaya Press, 2017.




DOI: http://dx.doi.org/10.30998/faktorexacta.v17i1.21164

Refbacks

  • There are currently no refbacks.


Bookmark and Share


Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

template doaj grammarly tools mendeley crossref SINTA sinta faktor exacta   Garuda Garuda Garuda Garuda Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Flag Counter

site stats View Faktor Exacta Stats


pkp index