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All Journal Brilliance: Research of Artificial Intelligence Jurnal Matematika Integratif
Inayaturohmat, Fatuh
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Mathematics Mechanical Engineering Transportation Other

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Optimal Control of Monkeypox Transmission Model with the Effect of Hospitalization Inayaturohmat, Fatuh; Pramesti, Retta Farah; Pratama, Gilar Budi; Cahyani, Nita; Hanifah, Aisyah
Jurnal Matematika Integratif Vol 21, No 1: April 2025
Publisher : Department of Matematics, Universitas Padjadjaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/jmi.v21.n1.62791.113-122

Abstract

Monkeypox, also known as mpox, is a zoonotic illness caused by the Monkeypox Virus (MPV), which belongs to the Orthopoxvirus genus within the Poxviridae family. According to a WHO report as of September 2023, the virus has spread to numerous non-endemic countries, showing a significant number of cases. The United States reported the highest count, with 4,259 cases. In contrast, Indonesia has reported relatively fewer cases compared to other Southeast Asian nations. Nonetheless, the risk of transmission, particularly through close personal contact, remains a public health concern. This study examined the transmission of monkeypox among human populations using the spread model proposed by previous research. The novelty of this research is the enhancement of the model by introducing hospitalization parameters as a control mechanism, aiming to determine the optimal hospitalization level to minimize the disease's spread. The method used for optimal control is minimum pontryagin principle. The model also consider the asymptomatic and symptomatic infected individuals. There is a transition from asymptomatic to symptomatic individuals. Numerical simulation results show that implementing this control leads to a more rapid decline in the number of symptomatic infected individuals compared to scenarios without control measures.
Profit Prediction for Skincare Resellers Using the Exponential Smoothing Method Cahyani, Nita; Irsyada, Rahmat; Firman, Azharil; Inayaturohmat, Fatuh; Pramesti, Retta Farah
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.6585

Abstract

This research elucidates the application of the exponential smoothing method in forecasting profit figures for Lutfia MS Glow Skincare. This method was chosen due to its capability to adapt data using the alpha value, along with continual refinement based on exponentially smoothed historical averages. With an explanatory purpose, the study collected profit data from 2020 to 2022 at Lutfia MS Glow Skincare. The single exponential smoothing technique was employed to develop a profit prediction system, enabling the identification of sales trends and evaluation through metrics like Mean Absolute Error (MAE) and Mean Squared Error (MSE). The approach offers simplicity in implementation while providing relatively accurate results, especially for short-term forecasting. This makes it particularly useful in retail and skincare business contexts, where sales figures can be volatile due to seasonal demands or market fluctuations. By utilizing exponential smoothing, the research helps reduce forecasting errors and provides actionable insights for business planning. The result of the analysis showed a reasonably low error margin with a Mean Absolute Percentage Error (MAPE) of 3.65%, indicating high prediction accuracy. The research outcomes furnish skincare resellers and decision-makers with practical guidance in planning inventory, managing supply chains, and executing marketing strategies, ultimately supporting better data-driven decisions in a competitive industry.
Co-Authors Firman, Azharil Hanifah, Aisyah Irsyada, Rahmat Nita Cahyani Pramesti, Retta Farah Pratama, Gilar Budi