PERBANDINGAN METODE FUZZY TIME SERIES MARKOV CHAIN DAN FUZZY TIME SERIES CHENG DALAM MERAMALKAN NILAI TUKAR RUPIAH TERHADAP DOLAR AMERIKA SERIKAT (AS)
DOI:
https://doi.org/10.25077/jmua.12.2.121-134.2023Abstract
Nilai tukar mata uang atau yang sering disebut dengan kurs merupakan
harga satu unit mata uang asing dalam mata uang domestik atau dapat
juga dikatakan harga mata uang domestik terhadap mata uang asing.
Nilai tukar rupiah terhadap dolar Amerika Serikat memainkan peranan sentral
dalam perdagangan internasional, karena nilai tukar rupiah terhadap dolar
Amerika Serikat memungkinkan seseorang untuk membandingkan harga-harga
segenap barang dan jasa yang dihasilkan berbagai negara. Pertumbuhan nilai
tukar mata uang yang stabil menunjukkan bahwa negara tersebut memiliki
kondisi perekonomian yang stabil. Oleh sebab itu perlu dilakukan peramalan
nilai tukar rupiah terhadap dolar Amerika Serikat untuk beberapa waktu yang
akan datang sebagai dasar pengambilan keputusan bagi pemerintah. Beberapa
metode peramalan yang dapat dilakukan untuk meramalkan data time series
nilai tukar rupiah terhadap dolar Amerika Serikat adalah metode fuzzy time
series markov chain dan fuzzy time series Cheng. Kedua metode ini akan ditentukan
hasil peramalannya kemudian dibandingkan tingkat akurasinya menggunakan
MSE, MAE, dan MAPE sehingga diperoleh metode peramalan yang
paling tepat untuk meramalkan nilai tukar rupiah terhadap dolar Amerika
Serikat. Pada penelitian ini diperoleh metode terbaik untuk meramalkan nilai
tukar rupiah terhadap dolar Amerika Serikat adalah metode fuzzy time series
markov chain.
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References
Bank Indonesia (BI). 2021. Informasi Kurs 7 November 2019-29 Februari
https://www.bi.go.id/id/defualt.aspx (diakses pada tanggal 15
Oktober 2021 jam 19.00).
Cheng, C. S., T. Chen, H. J. Teoh, and Chen-Han Chiang. 2008. Fuzzy
time-series based on adaptive expectation model for TAIEX forecasting.
Expert Systems with Applications. 34 : 1126-1132.
Devi, S. S. 2021. Pengaruh Inflasi dan Nilai Tukar/Kurs Terhadap Indeks
Harga Saham Gabungan (Ihsg) Yang Terdaftar di Bursa Efek Indonesia(
Bei) Pada Masa Pandemi Covid-19 Bulan Januari-Desember Tahun
Jurnal Inovatif Mahasiswa Manajemen. 1(2) : 139-149.
Hans-Jurgen, and Zimmermann. 1996. Fuzzy Set Theory— and Its Applications
rd Edition. Kluwer Academic, London.
Heizer, J., and B. Render. 2005. Manajemen Operasi Edisi ketujuh. Selemba
Empat, Jakarta.
Herlambang, L., A. 2021. Analisis Peramalan Penjualan Sepeda dan Motor
Listrik di PT XYZ. Jurnal Comasie. 4(1) : 130-138.
Indiyanto, R. 2008. Perencanaan dan Pengendalian Produksi. Yayasan
Humaniora, Surabaya.
Ismiyatun. 2021. Krisis Ekonomi Global Era Pandemi Covid-19 (Studi
Kasus: Menurunnya Nilai Tukar Rupiah Terhadap Dolar Amerika Serikat
Periode Februari-Maret 2020. Spektrum.
textbf18(2) : 50-68.
Kusumadewi, S. and I. Guslawudin. 2005. Fuzzy multi-criteria decision
making. Media Informatika. 3 : 27-29.
Kusumadewi, S., dan Purnomo, H. 2013. Aplikasi Logika Fuzzy untuk
Pendukung Keputusan. Graha Ilmu, Yogyakarta.
Makridakis, S., S.C. Wheelwright and V.E. McGee. 1992. Metode dan
Aplikasi Peramalan Edisi Kedua. Erlangga, Jakarta.
Montgomery D.C., C.L. Jennings, and M. Kulahci. 2008. Introduction to
Time Series Analysis and Forecasting. Jhon Wiley & Sons, Inc, New Jerse.
Prasetio, R. T. 2014. Inventory Control Using Statistics Forecasting On
Manufacture Company. Jurnal Informatika. 2(2) : 136-142.
Ross, S. M. 1996. Stochastic Process Second Edition. Jhon Wiley & Sons,
Inc, Canada.
Simorangkir, I. dan Suseno. 2004. Sistem dan Kebijakan Nilai Tukar.
PPSK, Jakarta.
Song, Q., and B. S. Chissom. 1993. Forecasting enrollments with fuzzy
time series – Part I. Fuzzy Sets and Systems. 54(1) : 1-9.
Song, Q., and B. S. Chissom. 1994. Forecasting enrollments with fuzzy
time series – Part II. Fuzzy Sets and Systems. 62(1) : 1-8.
Tsaur, R.C. 2012. A fuzzy time series-markov chain model with an application
to forecast the exchange rate beetween the taiwan and US dollar.
International Journal of Innovative Computing, Information, and Control.
(7B) : 4931-4942.
Yudaruddin, R. 2019. Forecasting Untuk Kebijakan Ekonomi dan Bisnis.
RV Pustaka Harizon, Samarinda.
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