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All Journal EKSAKTA: Journal of Sciences and Data Analysis VARIANSI: Journal of Statistics and Its Application on Teaching and Research
Sischa Wahyuning Tyas
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Chemical Engineering, Chemistry & Bioengineering Chemistry Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Materials Science & Nanotechnology Mathematics

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COMPARISON OF NEWTON RAPHSON AND SECANT METHODS TO DETERMINE THE OPTIMAL POINT OF TIKTOK APPLICATION Pratama, Fabio Arayya; Muhammad Shaquille Syafiq; Muhammad Rudmardiansyah Pratama Putra; Anggraini Puspita Sari; Sischa Wahyuning Tyas
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 8 No. 1 (2026)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm499

Abstract

The growth of digital application users generally follows a non-linear pattern that can be modeled using the logistics growth function, which has the characteristic of an inflection point, which is a condition when the growth rate reaches the maximum value. Optimal point determination involves solving non-linear equations that cannot always be solved directly, so a numerical approach is required. This study aims to determine the optimal growth point of TikTok application users and compare the performance of the Newton–Raphson and Secant methods in solving non-linear equations in the logistics model. User growth data was obtained from the Google Play Store and simulated using logistics growth parameters that represent the characteristics of applications with a high level of virality, with analytics solutions as an evaluation reference. The calculation results show that the optimal point of growth of TikTok users is around the 6th week. The Secant method yielded an optimal point estimate of 5.972 with an RMSE value of 0.0150 and a relative error of 0.25%, while the Newton–Raphson method yielded an estimate of 5.773 with an RMSE value of 0.2140 and a relative error of 3.57%. The difference in error rate and convergence stability shows that the Secant method provides a more effective approach in determining the optimal growth point of digital application users based on the logistics model.
Inflation Convergence Modeling Using Binary Logistic Regression With SGD-Newton Raphson Optimization Methods in Indonesia Fatma Novalia Kussumarani; Istiqomah, Nerissabila Uswatun; Siva Ifin Azzahra; Anggraini Puspita Sari; Sischa Wahyuning Tyas
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 7, ISSUE 1, April 2026
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol7.iss1.art9

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.
Co-Authors Anggraini Puspita Sari Fatma Novalia Kussumarani Istiqomah, Nerissabila Uswatun Muhammad Rudmardiansyah Pratama Putra Muhammad Shaquille Syafiq Pratama, Fabio Arayya Siva Ifin Azzahra