Perbandingan Hasil Peramalan Uang M1 di Indonesia Menggunakan Metode SARIMA dan Metode SVR
-
Etik Zukhronah
*
Universitas Sebelas Maret, Kota Surakarta,
Indonesia
- Nurrul Hidayah Universitas Sebelas Maret, Kota Surakarta, Indonesia
- Irwan Susanto Universitas Sebelas Maret, Kota Surakarta, Indonesia (*) Corresponding Author
Abstract
M1 money is the most liquid form of money supply because all its components (currency and giral) can be directly used for daily transactions and reflect the dynamics of public consumption. M1 money forecasting is necessary to anticipate its fluctuations that can affect price stability and inflation. This study aims to compare the results of M1 money forecasting with the Seasonal Autoregressive Integrated Moving Average (SARIMA) and Support Vector Regression (SVR) methods. The M1 Money data is divided into two, 80% training data from January 2010 to February 2021 and 20% testing data from March 2021 to December 2023. SARIMA and SVR modeling were carried out separately and then the best model was selected based on the smallest Mean Absolute Percentage Error (MAPE). The results of the study found that the best SARIMA model is SARIMA (1,1,0)(1,1,0)₁₂ with a MAPE of 2,250%, while the best SVR model uses a linear kernel with optimal hyperparameters C=100; ε=0,001; and γ=0,001 resulting in a MAPE of 2,254%. Thus, the SARIMA model has a better level of accuracy in predicting M1 money in Indonesia. The application of this model in predicting is expected to help related parties in evaluating the direction of monetary policy and understanding economic conditions.
Downloads
References
Allorerung, P. P., Erna, A., Bagussahrir, M., & Alam, S. (2024). Analisis Performa Normalisasi Data untuk Klasifikasi K-Nearest Neighbor pada Dataset Penyakit. JISKA(Jurnal Informatika Sunan Kalijaga), 9(3), 178–191. https://doi.org/https://doi.org/10.14421/jiska.2024.9.3.178-191
Almahdy, R. F., & D, W. M. P. (2024). Prediksi Harga Rumah Di Kabupaten Bantul Menggunakan Algoritma Support Vector Regression. Jurnal Teknik Informatika dan Sistem Informasi, 11(2), 152–165. https://doi.org/https://doi.org/10.35957/jatisi.v11i2.6001
Ambarwati, A. D., Sara, I. M., & Aziz, I. S. A. (2021). Pengaruh Jumlah Uang Beredar (JUB), BI Rate dan Inflasi Terhadap Pertumbuhan Ekonomi di Indonesia Periode 2009-2018. Warmadewa Economic Development Journal (WEDJ), 4(1), 21–27. https://doi.org/https://doi.org/10.22225/wedj.4.1.3144.21-27
Avinash, A., Widjaja, A., & Karnalim, O. (2024). Analisis Perbandingan Algoritma Machine Learning Untuk Forecasting Persediaan Produk Barang Pokok. JuTISI, 10(2), 361–378. https://doi.org/https://doi.org/10.28932/jutisi.v10i2.9357
Badan Pusat Statistik. (2024). Uang Beredar (Milyar Rupiah). https://www.bps.go.id/id/statistics-table/2/MTIzIzI=/uang-beredar.html
Deng, A. (2023). Time Series Cross Validation: Theoretical Properties and Empirical Performance. Economics Letters, 233, 111369. https://doi.org/10.1016/j.econlet.2023.111369
Dimashanti, A. R., & Sugiman, S. (2021). Peramalan Indeks Harga Konsumen Kota Semarang Menggunakan SARIMA Berbantuan Software Minitab. PRISMA, Prosiding Seminar Nasional Matematika, 4, 565–576.
Fadhil, N. (2025). Perbandingan akurasi algoritma xgboost dan svr dalam prediksi harga cryptocurrency. Jurnal Ilmu Komputer dan Sistem Informasi, 13(1), 1–6. https://doi.org/https://doi.org/10.24912/jiksi.v13i1.32865
Fadilah, A. D., Adinda, N. T., Rahma, M. M., Lauda, R. S., & Suminar, L. (2024). Dampak kenaikan tarif PPN terhadap inflasi dan daya beli masyarakat Indonesia. Media Akuntansi Perpajakan, 9(2), 67–78. https://doi.org/https://doi.org/10.52447/map.v9i2.7940
Habibi, M. S. A., Abdillah, A. H., Idhom, M., & Trimono, T. (2025). Perbandingan Kinerja Gru dan SVR untuk Prediksi Emas di Indonesia. Informatika: Jurnal Teknik Informatika dan Multimedia, 5(1), 141–150. https://doi.org/https://doi.org/10.51903/informatika.v5i1.1105
Hayati, L. A., & Agustina, N. (2024). Aplikasi Model ARIMA dalam Peramalan Produk Domestik Regional Bruto (PDRB) Pertambangan dan Penggalian Kalimantan Selatan. Seminar Nasional Official Statistics, 2024(1), 2020–2024. https://doi.org/https://doi.org/10.34123/semnasoffstat.v2024i1.2055
Isnaeni, R., Sudarmin, S., & Rais, Z. (2022). Analisis Support Vector Regression (Svr) Dengan Kernel Radial Basis Function (Rbf) Untuk Memprediksi Laju Inflasi Di Indonesia. VARIANSI: Journal of Statistics and Its application on Teaching and Research, 4(1), 30–38. https://doi.org/https://doi.org/10.35580/variansiunm13
Ivanda, S. T., & SZS, J. A. (2025). Implementasi Metode Autoregressive Integrated Moving Average Untuk Analisis Peramalan Permintaan Kalibrasi Pada PT XYZ. Jurnal Serambi Engineering, 10(1), 12021–12028. https://jse.serambimekkah.id/index.php/jse/article/view/661
Junaedi, L., Damastuti, N., Latipah, L., & Widodo, A. (2025). Penerapan Metode Seasonal ARIMA (SARIMA) untuk Peramalan Penjualan Barang dengan Pola Musiman Tahunan. JISEM (Jurnal Informatika, Sistem Informasi, dan Elektro Modern), 1(01), 38–48. https://doi.org/https://doi.org/10.33508/jisem.v1i01.7403
Kruba, R., Sofyan, H., Marshanda, D., Rahmadhania, & Syazana, N. (2025). Peramalan Saham Indofood Di Indonesia Menggunakan Metode Seasonal Autoregressive Integrated Moving Average (SARIMA). Jurnal Manajemen dan Keuangan, 14(1), 102–117. https://doi.org/https://doi.org/10.33059/jmk.v14i1.11501
Pratama, Y. D., & Salam, A. (2025). Comparison of Data Normalization Techniques on KNN Classification Performance for Pima Indians Diabetes Dataset. Journal of Applied Informatics and Computing (JAIC), 9(3), 693–706. https://doi.org/https://doi.org/10.30871/jaic.v9i3.9353
Putri, I., & Razi, A. (2024). Implementasi Metode SVM RBF (Radial Basis Function) Kernel Untuk Klasifikasi Status Gizi ada Balita. Jurnal Teknologi Terapan dan Sains, 5(2), 43–56. https://doi.org/https://doi.org/10.29103/tts.v5i2.19156
Ramadita, M., Mahmudi, & Wijaya, M. Y. (2024). Prediksi Curah Hujandi Jakarta Menggunakan Model Hybrid (DWT-SVR-Prophet). The Indonesian Journal of Computer Science, 13(5), 8179–8194. https://doi.org/https://doi.org/10.33022/ijcs.v13i5.4357
Rangkuti, S. P., Pratama, A., & Fhonna, R. P. (2025). Prediksi Produksi Beras Untuk Mendukung Ketahanan Pangan Di Kabupaten Aceh Utara Menggunakan Metode Seasonal Autoregressive Integrated Moving Average (SARIMA). JATI (Jurnal Mahasiswa Teknik Informatika), 9(4), 7136–7143. https://doi.org/https://doi.org/10.36040/jati.v9i4.14456
Rizkiani, J. R., Zukhronah, E., & Respatiwulan. (2024). Perbandingan peramalan nilai tukar rupiah terhadap dolar amerika serikat menggunakan singular spectrum analysis (ssa) dan support vector regression (svr). Prosiding Seminar Nasional Sains dan Teknologi, 1(2), 172–180. https://conference.ut.ac.id/index.php/saintek/article/view/2530#pkp_content_footer
Ruliana, Rais, Z., Marni, & Saleh Ahmar, A. (2024). Implementation of the Support Vector Regression (SVR) Method in Inflation Prediction in Makassar City. ARRUS Journal of Mathematics and Applied Science, 4(1), 28–35. https://doi.org/https://doi.org/10.35877/mathscience2608
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Perbandingan Hasil Peramalan Uang M1 di Indonesia Menggunakan Metode SARIMA dan Metode SVR
Pages: 190-198
Copyright (c) 2025 Etik Zukhronah, Nurrul Hidayah, Irwan Susanto
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).
