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All Journal LOGIK@ Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI CAUCHY: Jurnal Matematika Murni dan Aplikasi Journal of Mathematical and Fundamental Sciences Jurnal Fourier Desimal: Jurnal Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan Zero : Jurnal Sains, Matematika, dan Terapan AXIOM : Jurnal Pendidikan dan Matematika M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika InPrime: Indonesian Journal Of Pure And Applied Mathematics Majalah Ilmiah Matematika dan Statistika (MIMS) Jambura Journal of Biomathematics (JJBM) Jurnal Eurekamatika Innovative: Journal Of Social Science Research The Indonesian Journal of Computer Science
Nina Fitriyati
UIN Syarif Hidayatullah Jakarta
Agriculture, Biological Sciences & Forestry Humanities Astronomy Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Journal : Desimal: Jurnal Matematika

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Forecasting Indonesian inflation using a hybrid ARIMA-ANFIS Fitriyati, Nina; Mahmudi, Mahmudi; Wijaya, Madona Yunita; Maysun, Maysun
Desimal: Jurnal Matematika Vol. 5 No. 3 (2022): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/djm.v5i3.14093

Abstract

This paper discusses the prediction of the inflation rate in Indonesia. The data used in this research is assumed to have both linear and non-linear components. The ARIMA model is selected to accommodate the linear component, while the ANFIS method accounts for the non-linear component in the inflation data. Thus, the model is known as the hybrid ARIMA-ANFIS model. The clustering method is performed in the ANFIS model using Fuzzy C-Mean (FMS) with a Gaussian membership function. Consider 2 to 6 clusters. The optimal number of clusters is assessed according to the minimum value of the error prediction. To evaluate the performance of the fitted hybrid ARIMA-ANFIS model, it can be compared to the classical ARIMA model and with the ordinary ANFIS model. The result reveals that the best ARIMA model for inflation prediction in Indonesia is ARIMA(2,1,0). In the hybrid ARIMA(2,1,0)-ANFIS model, two clusters are optimal. Meanwhile, the optimum number of clusters in the ordinary ANFIS model is six. The comparison of prediction accuracy confirms that the hybrid model is superior to the individual model alone of either ARIMA or ANFIS model.
Indonesia’s total fertility rate (TFR) using the brown and holt double exponential smoothing with grid search Rahma, Chelsea Fatihah; Fitriyati, Nina; Inna, Suma; Adjie, Dharma Syadhi Putra
Desimal: Jurnal Matematika Vol. 8 No. 1 (2025): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/djm.v8i1.26332

Abstract

The Brown and Holt DES method effectively captures trends in time-series data. Its forecasting accuracy heavily depends on the selection of optimal smoothing parameters. Often, the smoothing parameters are selected manually using trial and error methods. This method is time-consuming, unsystematic, prone to bias, not scalable, less reproducible, and increases the risk of overfitting or underfitting. To overcome these problems, in this study, we propose optimization of smoothing parameters using Grid Search. This new approach will be applied to predict Indonesia’s TFR. Grid Search optimization is employed to systematically explore the parameter space and identify the best combination that minimizes forecasting errors. To ensure model robustness, cross-validation is implemented, allowing the evaluation of model performance across multiple training and validation splits. The results show that the Holt DES method with Grid Search is more accurate than the Brown DES with Grid Search, with the smallest Mean Absolute Percentage Error (MSE) value of 0.00972 at  and . Predictions with Holt DES with Grid Search show a downward trend in the national TFR until 2027, potentially falling below the ideal level of 2.1. TFR predictions at the provincial level show pattern variations, with several regions experiencing significant declines. The difference in results between the Brown and Holt methods emphasizes the importance of optimizing smoothing parameters and selecting an appropriate population-analysis prediction model to support demographic policy.
Co-Authors . Mahmudi Aceng Komarudin Mutaqin Adjie, Dharma Syadhi Putra Agus Yodi Gunawan Agus Yodi Gunawan Alkadrie, Syarifah Syila Amelia Emaras, Tamara Anas Qolbu, Adinda Andrew Fiade Anggita Septiani Ayu Puji Rahayu Azzahra Benita Bisyri, M. Alvi Devi Ila Octaviyani Dino Agustin Putra Eliskar, Yulinda Faizah, Salma Abidah Fathu Rahman Gina Isma Kusuma Handarbeni, Zalfa Talitha Haris Hamzah Inna, Suma Irene, Yanne Irma Fauziah Iskandar Fahmi Khusnul Khotimah Mahmudi Mahmudi Mahmudi Mahmudi Maysun Maysun Maysun, Maysun Muhammad Manaqib, Muhammad Nadilia, Nindita Nindita Nadilia Nur Inayah Nuraini, Vita Nurmaleni Nurmaleni NURTITI SUNUSI Rahma, Chelsea Fatihah Rahman, Fathu Rini Marwati Rustam, Rustam S. Sutawanir Saleh, Khaerudin Sari, Flori Ratna Sri Maliska Sri Wahyuningsih Sutanto, Taufik Edy Sutawanir Darwis Taufik Edy Sutanto Wahri Irawan Wijaya, Madona Yunita Wulandari, Fitria Sisca Yupinto, Ikhwal Zharifah, Zharifah Zulkifli, Dhea Urfani Zulkifli, Dhea Urfina