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Abstract
Global economic changes have necessitated the development of inflation models that can accurately describe Indonesia's economic dynamics. This study aims to compare two optimization methods, Newton Raphson and Stochastic Gradient Descent (SGD), in binary logistic regression modeling to analyze the effectiveness of monetary policy. This study contributes to evaluating the performance of both methods in terms of convergence speed and accuracy of inflation model parameter estimation. The results of the analysis show that the Newton Raphson method is more efficient in achieving convergence with an iteration value of 0.2933 compared to SGD, while both methods produce equivalent model quality based on the Akaike Information Criterion (AIC) values of 34.4008 and 34.4254. These findings emphasize the importance of selecting the right optimization method to support more efficient monetary policy analysis.
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Copyright (c) 2026 Fatma Novalia Kussumarani, Nerissabila Uswatun Istiqomah, Siva Ifin Azzahra, Anggraini Puspita Sari, Sischa Wahyuning Tyas
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References
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References
W. Bank, “Deepening the Financial Sector for Stronger Growth and Sustainable Recovery Executive Summary,” Indones. Econ. Prospect., 2022, [Online]. Available: https://www.worldbank.org/in/country/indonesia/publication/indonesia-economic-prospects-iep-june-2022-financial-deepening-for-stronger-growth-and-sustainable-recovery
Syifatul Husna, “Monetary Policy Strategies for Inflation in Indonesia,” J. Cent. Publ., vol. 1, no. 8, pp. 888–895, Sep. 2024, doi: 10.60145/jcp.v1i8.183.
D. Y. Aharon, M. I. Azman Aziz, and I. Kallir, “Oil price shocks and inflation: A cross-national examination in the ASEAN5+3 countries,” Resour. Policy, vol. 82, p. 103573, May 2023, doi: 10.1016/j.resourpol.2023.103573.
H. Handri, H. D. Mulyaningsih, A. K. Hidayat, R. Kurniawan, and A. W. Rachmawati, “The impact of Indonesian oil price (CPI) and macroeconomics on investments in the manufacturing sector in Indonesia,” F1000Research, vol. 10, p. 338, May 2021, doi: 10.12688/f1000research.27958.1.
W. Thorbecke, “How oil prices impact the Indonesian economy: Evidence from the stock market,” Asia Glob. Econ., vol. 5, no. 2, p. 100122, Dec. 2025, doi: 10.1016/j.aglobe.2025.100122.
A. Kliber, M. Szyszko, M. Próchniak, and A. Rutkowska, “Impact of uncertainty on inflation forecast errors in Central and Eastern European countries,” Eurasian Econ. Rev., vol. 13, no. 3–4, pp. 535–574, Dec. 2023, doi: 10.1007/s40822-023-00237-9.
A. R. N. Fauzi, S. Abusini, and C. Karim, “Comparing Newton Raphson and Stochastic Gradient Descent Methods for Traffic Accident in Malang,” CAUCHY J. Mat. Murni dan Apl., vol. 10, no. 2, pp. 519–532, Jun. 2025, doi: 10.18860/cauchy.v10i2.33177.
N. Lianingsih, Y. Audina, and S. Purwani, “Implementation of Newton-Raphson Iterative Method for Solving Non-Linear Equations in the Solow Economic Growth Model,” World Sci. News, vol. 199, no. November 2024, pp. 90–98, 2025.
R. Risawandi and F. Fadlisyah, “Recommendation System for Zakat Recipients at Baitul Mal Kota Lhokseumawe Using the Z-Score Method,” J. Teknol. Terap. Sains 4.0, vol. 4, no. 3, p. 136, Nov. 2023, doi: 10.29103/tts.v4i3.14887.
R. Wadui, J. S. Kekenusa, and D. Hatidja, “Binary Logistic Regression Analysis to Determine Inpatient Satisfaction with the Quality of Services at the Tobelo Regional General Hospital,” d’Cartesian, vol. 13, no. 1, pp. 43–48, Mar. 2024, doi: 10.35799/dc.13.1.2024.52520.
D. P. Handayani and K. Kismiantini, “Binary Logistic Regression Analysis of Quarter-Life Crisis Symptoms on Sleep Difficulties in Early Indonesian Adulthood,” J. Mat. Stat. dan Komputasi, vol. 22, no. 1, pp. 61–77, Sep. 2025, doi: 10.20956/j.v22i1.44765.
A. A. Putri, D. T. Salaki, and J. Titaley, “Binary Logistic Regression Model of Gastric Symptom Tendencies in Mathematics Students at the FMIPA, UNSRAT,” J. Mat. dan Apl., vol. 11, no. 1, pp. 39–43, 2022, [Online]. Available: https://ejournal.unsrat.ac.id/v3/index.php/decartesian/article/view/37847
A. J. Scott, D. W. Hosmer, and S. Lemeshow, Applied Logistic Regression., vol. 47, no. 4. 1991. doi: 10.2307/2532419.
S. Bahrami and K. Amini, “An efficient two-step trust-region algorithm for exactly determined consistent systems of nonlinear equations,” J. Comput. Appl. Math., vol. 367, p. 112470, Mar. 2020, doi: 10.1016/j.cam.2019.112470.
Y. Tian, Y. Zhang, and H. Zhang, “Recent Advances in Stochastic Gradient Descent in Deep Learning,” Mathematics, vol. 11, no. 3, p. 682, Jan. 2023, doi: 10.3390/math11030682.
D. T. Ailobhio and J. A. Ikughur, “A Review of Some Goodness-of-Fit Tests for Logistic Regression Model,” Asian J. Probab. Stat., vol. 26, no. 7, pp. 75–85, Jun. 2024, doi: 10.9734/ajpas/2024/v26i7631.
N. Surjanovic and T. M. Loughin, “Improving the Hosmer-Lemeshow goodness-of-fit test in large models with replicated Bernoulli trials,” J. Appl. Stat., vol. 51, no. 7, pp. 1399–1411, May 2024, doi: 10.1080/02664763.2023.2272223.
Syarifah fatimah tuz zahro and Khusnul Khuluqi, “The Effect of Audit Fees, Management Change and Company Growth on Auditor Change,” J. Akunt. Keuang. Dan Perpajak. | E-ISSN 3063-8208, vol. 1, no. 3, pp. 366–376, Feb. 2025, doi: 10.62379/jakp.v1i3.277.
A. Miranda, S. Suyitno, and M. Fauziyah, “Modelling the Probability of River Water Pollution Using Geographically Weighted Logistic Regression Model (Case Study: River Water DO Data in East Kalimantan),” J. Mat. Stat. dan Komputasi, vol. 21, no. 2, pp. 408–430, Jan. 2025, doi: 10.20956/j.v21i2.40346.
B. Akturk, U. Beyaztas, H. L. Shang, and A. Mandal, “Robust functional logistic regression,” Adv. Data Anal. Classif., vol. 19, no. 1, pp. 121–145, Mar. 2025, doi: 10.1007/s11634-023-00577-z.
L. I. Harlyan, E. S. Yulianto, Y. Fitriani, and Sunardi, “Application of the Akaike Information Criterion (AIC) in Calculating the Technical Efficiency of Purse Seine Fishing in Tuban, East Java,” Mar. Fish. J. Mar. Fish. Technol. Manag., vol. 11, no. 2, pp. 181–188, Dec. 2021, doi: 10.29244/jmf.v11i2.38550.
E. Civitelli, M. Lapucci, F. Schoen, and A. Sortino, “An effective procedure for feature subset selection in logistic regression based on information criteria,” Comput. Optim. Appl., vol. 80, no. 1, pp. 1–32, Sep. 2021, doi: 10.1007/s10589-021-00288-1.