Analisa Perbandingan Metode Trend Moment dan Regresi Linear dalam Prediksi Kurs Mata Uang Rupiah terhadap Mata Uang Riyal
- Rahmadan Alam Ardan Ananda Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
-
Alwis Nazir
*
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru,
Indonesia
- Lola Oktavia Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
- Elin Haerani Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
- Fitri Insani Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia (*) Corresponding Author
Abstract
Currency exchange rates play an important role in the economic stability of a country, especially in the context of international trade and global financial mobility. In Indonesia, fluctuations in the Rupiah exchange rate against the Saudi Arabian Riyal (SAR) have become a strategic issue, especially ahead of the Hajj season. This study aims to predict the exchange rate of Rupiah against Riyal in that period by using two forecasting approaches, namely Linear Regression and Trend Moment. The performance evaluation of both methods is conducted based on historical data using Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) indicators. The results show that Linear Regression provides a better level of accuracy with an MAE of 330.36 and a MAPE of 17.32%, compared to Trend Moment which has an MAE of 412.41 and a MAPE of 18.88%. This finding shows that Linear Regression is more effective in capturing the pattern of exchange rate changes that tend to be linear. The prediction results also show an increasing trend in the exchange rate ahead of the Hajj month, which correlates with the increasing demand for foreign exchange. The implications of these results can be utilized by prospective pilgrims, business actors, and the government in formulating more appropriate and adaptive financial strategies
Downloads
References
Divka Avedish, Faqihuddin Tri Wibowo, Nahdiyah Ulul Azmi, Qothrotun Nada, and Sarpini Sarpini, “Peran Nilai Tukar Rupiah Dan Fluktuasi Valuta Asing Terhadap Ketahanan Ekonomi Indonesia,” Jurnal Kajian dan Penalaran Ilmu Manajemen, vol. 3, no. 1, pp. 223–235, Dec. 2024, doi: 10.59031/jkpim.v3i1.542.
GBP Are, “Prediksi Nilai Tukar Mata Uang Rupiah Terhadap Dolar Amerika Menggunakan Metode Hidden Markov Model,” Coding Jurnal Komputer dan Aplikasi, vol. 8, no. 1, Jan. 2020, doi: 10.26418/coding.v8i1.39192.
N. Rozaini, G. A. M. Gultom, T. Yani, D. B. Ananda, A. Batubara, and B. S. Surbakti, “Analisis Pengaruh Inflasi, Kurs, dan Ekspor Terhadap Pdbr 2001-2021 Indonesia,” MESIR: Journal of Management Education Social Sciences Information and Religion, vol. 1, no. 2, pp. 110–119, Aug. 2024, doi: 10.57235/mesir.v1i2.2720.
D. Sihombing, P. Sari Ramadhan, and E. Fahmi Ginting, “Penerapan Data Mining Dalam Memprediksi Kurs Rupiah Terhadap Ringgit Malaysia Dengan Menggunakan Metode Trend Keyword: Data Mining Trend Moment Nilai Tukar Mata Uang,” Jurnal CyberTech, vol. 3, no. 1, 2020, doi: https://doi.org/10.53513/jct.v3i1.2739.
G. Ardesfira, H. F. Zedha, I. Fazana, J. Rahmadhiyanti, S. Rahima, and S. Anwar, “Peramalan Nilai Tukar Rupiah Terhadap Dollar Amerika Dengan Menggunakan Metode Autoregressive Integrated Moving Average (Arima),” Jambura Journal of Probability and Statistics, vol. 3, no. 2, pp. 71–84, Nov. 2022, doi: 10.34312/jjps.v3i2.15469.
I. Hidayat, L. A. Syamsul, I. Akbar, and A. S. Rachman, “Prediksi Nilai Tukar Mata Uang Menggunakan Algoritma Long Short-Term Memory dan Random Forest,” Journal of Computer System and Informatics (JoSYC), vol. 6, no. 1, pp. 107–116, 2024, doi: 10.47065/josyc.v6i1.6200.
R. R. Elhakim, “Prediksi Nilai Tukar Rupiah Ke Dollar As Menggunakan Metode Arima,” MATHunesa: Jurnal Ilmiah Matematika, vol. 8, no. 2, pp. 145–150, Jun. 2020, doi: 10.26740/mathunesa.v8n2.p145-150.
N. Nurunnasikin, I. Indra, and K. Afifah, “Analisis Pengaruh Variabel Makroekonomi terhadap Biaya Perjalanan Ibadah Haji,” Bukhori: Kajian Ekonomi dan Keuangan Islam, vol. 4, no. 1, pp. 1–11, Jul. 2024, doi: 10.35912/bukhori.v4i1.3056.
A. Khaliq, S. Karimi, W. D. Taifur, and E. Ridwan, “The quest for explosive bubbles in the Indonesian Rupiah/US exchange rate: Does the uncertainty trinity matter?,” Decision Science Letters, vol. 13, no. 2, pp. 415–426, 2024, doi: 10.5267/j.dsl.2024.1.005.
R. Komansilan, V. Tarigan, and A. Yusupa, “Analisis Perbandingan Metode Trend Moment dan Regresi Linear Untuk Meramal Harga Saham Bank BRI,” J-SISKO TECH (Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD), vol. 7, no. 1, p. 24, Jan. 2024, doi: 10.53513/jsk.v7i1.9474.
A. S. Mubarok, “Analisis Peramalan dalam Manajemen Operasi,” Ebisnis Manajemen, vol. 3, no. 1, pp. 1–07, Dec. 2024, doi: 10.62951/ijss.v3i1.630.
M. Masruroh and K. F. Mauladi, “Penerapan Metode Regresi Linear Berganda Dalam Sistem Prediksi Nilai Ujian Nasional Siswa Smp,” Jurnal Teknika, vol. 12, no. 1, p. 1, Mar. 2020, doi: 10.30736/jt.v12i1.393.
A. Devi and P. Hendikawati, “Prediksi Kurs Rupiah Terhadap Dolar dengan Menggunakan Model Long-Short Term Memory,” PRISMA, Prosiding Seminar Nasional Matematika, vol. 7, pp. 882–891, 2024, [Online]. Available: https://proceeding.unnes.ac.id/prisma
C. Oliviasandrea and M. Sukur, “Implementasi Metode Trend Moment Pada Sistem Pendukung Keputusan Peramalan Penjualan Truk,” Kumpulan Jurnal Ilmu Komputer (KLIK), vol. 9, no. 3, pp. 402–15, Oct. 2022, doi: https://dx.doi.org/10.20527/klik.v9i3.474.
A. F. Ningrum and Z. A. Hisani, “Analisis Kinerja Model ARIMA dan LSTM dalam Memprediksi Jakarta Interbank Spot Dollar Rate (JISDOR),” Prosiding Seminar Nasional Sains Data, vol. 4, no. 1, pp. 478–486, Sep. 2024, doi: 10.33005/senada.v4i1.268.
E. S. Harahap, M. Rispan Affandi, E. Situmorang, and L. Sitorus, “Analisis Faktor yang Mempengaruhi Nilai Tukar,” Journal Pusat Studi Pendidikan Rakyat, vol. 4, no. 3, Aug. 2024
D. Nur Fitriani, and P. Aisyiyah Rakhma Devi, “Implementasi Metode Trend Moment Pada Jumlah Produksi Baju Distro Jatirogo,” Jurnal Nuansa Informatika, vol. 16, no. 1, Jan. 2022
M. Hanif, M. Abdurohman, and A. G. Putrada, “Rice consumption prediction using linear regression method for smart rice box system,” Jurnal Teknologi dan Sistem Komputer, vol. 8, no. 4, pp. 284–288, Oct. 2020, doi: 10.14710/jtsiskom.2020.13353.
A. Fitri Boy, “Implementasi Data Mining Dalam Memprediksi Harga Crude Palm Oil (CPO) Pasar Domestik Menggunakan Algoritma Regresi Linier Berganda (Studi Kasus Dinas Perkebunan Provinsi Sumatera Utara),” Journal of Science and Social Research, vol. 3, no. 2, pp. 78–85, Aug. 2020, doi: https://doi.org/10.54314/jssr.v3i2.421.
L. S. Ihzaniah, A. Setiawan, and R. W. N. Wijaya, “Perbandingan Kinerja Metode Regresi K-Nearest Neighbor dan Metode Regresi Linear Berganda pada Data Boston Housing,” Jambura Journal of Probability and Statistics, vol. 4, no. 1, pp. 17–29, May 2023, doi: 10.34312/jjps.v4i1.18948.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Analisa Perbandingan Metode Trend Moment dan Regresi Linear dalam Prediksi Kurs Mata Uang Rupiah terhadap Mata Uang Riyal
Pages: 563-571
Copyright (c) 2025 Rahmadan Alam Ardan Ananda, Alwis Nazir, Lola Oktavia, Elin Haerani, Fitri Insani
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
