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All Journal JURNAL DERIVAT: JURNAL MATEMATIKA DAN PENDIDIKAN MATEMATIKA Desimal: Jurnal Matematika
Saputra, Rika Ardiansyah
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Education Mathematics Social Sciences

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Pemodelan Dan Analisa Produksi Kerupuk Subur Dengan Menggunakan Aljabar-Max-Plus Savina, Dessy Agustin; Saputra, Rika Ardiansyah; Andy Rudhito, Marcellinus
Jurnal Derivat: Jurnal Matematika dan Pendidikan Matematika Vol. 12 No. 3 (2025): Jurnal Derivat (Desember 2025)
Publisher : Pendidikan Matematika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/j.derivat.v12i3.6667

Abstract

This study aims to model and analyze the optimal time from the time of the cracker production process from the beginning to completion using the Max-Plus algebraic approach at the cracker factory. The study was conducted by collecting data from the field, following the diagram of the production process, modeling mathematically using the Max-Plus algebra, and conducting a simulation calculation using the Scilab program. The results showed that the production process could be required in the Max-Plus algebra matrix equation. The results also showed that the production of cracker crackers can be done periodically, taking about 590 minutes per unit, or about 10 hours in total to complete one production unit. If it is adjusted to the factory work schedule that runs from 05.00 WIB to 15.00 WIB, then 1 production unit can be completed in 1 day, while 10 units require 4 days. Keywords: : Linear System, Algebra Max-Plus, Production System
Comparison of Non-linear Autoregressive Neural Network (NARNN) and Holt–Winters Methods for Antam Gold PricePrediction Akbar, Raihan; Saputra, Rika Ardiansyah; Najib, Mohamad Khoirun; Khatizah, Elis; Nurdiari, Sri
Desimal: Jurnal Matematika Vol. 9 No. 1 (2026): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/djm.v9i1.30260

Abstract

The high volatility and nonlinear dynamics of Antam gold prices present significant challenges for accurate time series forecasting, particularly within emerging financial markets. This study aims to develop and evaluate a comparative forecasting framework by examining the performance of the Nonlinear Autoregressive Neural Network (NARNN) and the Holt–Winters exponential smoothing method. A quantitative approach was applied using daily gold price data from January 4, 2010, to January 4, 2025. Data preprocessing included linear interpolation for missing values, Box–Cox transformation for variance stabilization, and time series decomposition to identify structural patterns. The dataset was partitioned into training and testing sets using an 80:20 ratio. Model performance was assessed using the Mean Absolute Percentage Error (MAPE). The results demonstrate that the NARNN model significantly outperforms the Holt–Winters approach, achieving a MAPE of 0.44%, compared to 11.43% and 11.90% for the additive and multiplicative variants, respectively. These findings highlight the limitations of classical linear smoothing methods in capturing abrupt structural changes and confirm the superiority of nonlinear neural network models in modeling complex financial time series. This study provides a robust empirical contribution by establishing a comparative modeling framework that enhances forecasting accuracy in volatile commodity markets.
Co-Authors Akbar, Raihan Khatizah, Elis Marcellinus Andy Rudhito Najib, Mohamad Khoirun Nurdiari, Sri Savina, Dessy Agustin