Predicting Inflation in Indonesia Using Bi- predictors Semiparametric Model Based on Local Polynomial Estimator
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Published
Feb 4, 2025
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Abstract
Inflation is a condition of general and continuous increases in the prices of goods and services over a certain period of time. High and fluctuating inflation rates are a sign of economic instability. This fluctuating nature is due to factors that influence it, causing the relationship patterns in the data to not form a certain pattern and are also used for predictions. This research applies a semiparametric regression model, which combines parametric and nonparametric model, using a local polynomial estimator for inflation data with 2 factors that influence inflation, namely the Bank Indonesia (BI) interest rate in one previous month and the change rate of Money Supply (JUB) in one previous month. The local polynomial method estimates nonparametric functions by considering the local polynomial order and the optimum bandwidth value based on the lowest GCV value. In this research, a semiparametric regression model was obtained with an optimum bandwidth value of order 1, with high accuracy (MAPE 9.61%). The inflation predictions for September 2024, where the value is not yet known, with the BI interset rate and the change rate of JUB values in one previous month, using the model resulted in this research, the predicted value is 2.12%.
Article Details
How to Cite
AZIZ, Abdul; CHAMIDAH, Nur; SAIFUDIN, Toha.
Predicting Inflation in Indonesia Using Bi- predictors Semiparametric Model Based on Local Polynomial Estimator.
Proceedings of the International Conference on Green Technology, [S.l.], v. 14, n. 1, feb. 2025.
ISSN 2580-7099.
Available at: <https://conferences.uin-malang.ac.id/index.php/ICGT/article/view/3299>. Date accessed: 07 feb. 2026.
Section
Pure and Applied Mathematics
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investigation of five machine learning algorithms for
optimizing mixed-asset portfolios including REITs,”
Expert Syst Appl, vol. 235, 2024, doi:
10.1016/j.eswa.2023.121102.
[2] V. P. Ariyanti and Tristyanti Yusnitasari, “Comparison
of ARIMA and SARIMA for Forecasting Crude Oil
Prices,” Jurnal RESTI (Rekayasa Sistem dan Teknologi
Informasi), vol. 7, no. 2, 2023, doi:
10.29207/resti.v7i2.4895.
[3] M. Abdelaziz, A. Ahmed, A. Riad, G. Abderrezak, and A.
A. Djida, “Forecasting daily confirmed COVID-19 cases
in Algeria using ARIMA models,” Eastern
Mediterranean Health Journal, vol. 29, no. 7, 2023, doi:
10.26719/emhj.23.054.
[4] C. D. Setiawan, W. Sulandari, and Y. Susanti,
“Peramalan Harga Saham PT Unilever Indonesia
Menggunakan Metode Hibrida ARIMA-Neural
Network,” Semnas Ristek (Seminar Nasional Riset dan
Inovasi Teknologi), vol. 7, no. 1, 2023, doi:
10.30998/semnasristek.v7i1.6270.
[5] S. R. Gusman, S. Suparti, and A. Rusgiyono,
“Perbandingan Model Arima Dengan Model
Nonparametrik Polinomial Lokal Dan Spline
Truncated Untuk Peramalan Harga Minyak Mentah
West Texas Intermediate (WTI) Dilengkapi GUI R,”
Jurnal Gaussian, vol. 12, no. 1, 2023, doi:
10.14710/j.gauss.12.1.20-29.
[6] R. Pratiwi and H. Yundari, “Model Arima
Semiparametrik,” Buletin Ilmiah Math. Stat. dan
Terapannya (Bimaster), vol. 11, no. 1, pp. 129–138,
2022.
[7] J. Iqbalullah and W. S. Winahju, “Peramalan Jumlah
Penumpang Pesawat Terbang di Pintu Kedatangan
Bandar Udara Internasional Lombok dengan Metode
ARIMA Box-Jenkins, ARIMAX, dan Regresi Time
Series,” Jurnal Sains dan Seni ITS; Vol 3, No 2 (2014);
D212-D21, Jul. 2014, [Online]. Available:
http://ejurnal.its.ac.id/index.php/sains_seni/article/
view/8138
[8] F. Bravo, “Local polynomial estimation of
nonparametric general estimating equations,” Stat
Probab Lett, vol. 197, 2023, doi:
10.1016/j.spl.2023.109805.
[9] M. Ulya, N. Chamidah, and T. Saifudin, “Prediction Of
pH And Total Soluble Solids Content Of Mango Using
Biresponse Multipredictor Local Polynomial
Nonparametric Regression,” Communications in
Mathematical Biology and Neuroscience, vol. 2023,
2023, doi: 10.28919/cmbn/7941.
[10] S. E. Ahmed, D. Aydın, and E. Yılmaz, “Penalty and
Shrinkage Strategies Based on Local Polynomials for
Right-Censored Partially Linear Regression,” Entropy,
vol. 24, no. 12, 2022, doi: 10.3390/e24121833.
[11] Y. Wang, Y. Wu, and S. S. Du, “Near-Linear Time
Local Polynomial Nonparametric Estimation with Box
Kernels,” INFORMS J Comput, vol. 33, no. 4, 2021, doi:
10.1287/ijoc.2020.1021.
[12] V. Fibriyani and N. Chamidah, “Prediction of
Inflation Rate in Indonesia Using Local Polynomial
Estimator for Time Series Data,” in Journal of Physics:
Conference Series, IOP Publishing Ltd, Feb. 2021. doi:
10.1088/1742-6596/1776/1/012065.
[13] N. S. Rahmi, “Peramalan Inflow Uang Kartal Bank
Indonesia KPW Tasikmalaya Jawa Barat Dengan
Metode Klasik Dan Modern,” Jurnal Statistika
Universitas Muhammadiyah Semarang; Vol 8, No 2
(2020): Jurnal Statistika; 166-174 ; 2528-1070 ; 2338-
3216, Jul. 2020, [Online]. Available:
https://jurnal.unimus.ac.id/index.php/statistik/articl
e/view/6412
[14] N. Chamidah and B. Lestari, “Estimation Of
Covariance Matrix Using Multi-Response Local
Polynomial Estimator For Designing Children Growth
Charts: A Theoretically Discussion,” in Journal of
Physics: Conference Series, Institute of Physics
Publishing, Dec. 2019. doi: 10.1088/1742-
6596/1397/1/012072.
[15] V. Fibriyani, N. Chamidah, and T. Saifudin,
“Modeling Case Fatality Rate Of COVID-19 In Indonesia
Using Time Series Semiparametric Regression Based
On Local Polynomial Estimator,” Commun. Math. Biol.
Neurosci., vol. 2024, p. Article–ID, 2024.
[16] S. E. Ahmed, D. Aydin, and E. Yilmaz,
“Semiparametric Time-Series Model Using Local
Polynomial: An Application on the Effects of Financial
Risk Factors on Crop Yield,” Journal of Risk and
Financial Management, vol. 15, no. 141, pp. 1–12, Mar.
2022, doi: 10.3390/jrfm15030141.
[17] G. Aryal, M. F. Gabrielli, and Q. Vuong,
“Semiparametric Estimation of First-Price Auction
Models,” Journal of Business & Economic Statistics, vol.
39, no. 2, pp. 373–385, Apr. 2021, doi:
10.1080/07350015.2019.1665530.
[18] A. H. Welsh and T. W. Yee, “Local regression for
vector responses,” J Stat Plan Inference, vol. 136, no. 9,
pp. 3007–3031, Sep. 2006, doi:
10.1016/j.jspi.2004.01.024.
[19] N. G. Zia, S. Suparti, and D. Safitri, “Pemodelan
Regresi Spline Menggunakan Metode Penalized Spline
Pada Data Longitudinal,” Jurnal Gaussian, vol. 6, no. 2,
2017, [Online]. Available:
https://ejournal3.undip.ac.id/index.php/gaussian/ar
ticle/view/16951