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All Journal International Journal of Applied Mathematics, Sciences, and Technology for National Defense
Al Habsy, Muhammad Yusuf
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Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Mathematics Physics

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Bibliometrix research of noise removal techniques in digital images for defense Al Husein, Fulkan Kafilah; Al Habsy, Muhammad Yusuf; Christi, Damaris Nugrahita; Hutagaol, Agnes Emanuela; Junoh, Ahmad Kadri bin
International Journal of Applied Mathematics, Sciences, and Technology for National Defense Vol. 3 No. 1 (2025): International Journal of Applied Mathematics, Sciences, and Technology for Nati
Publisher : FoundAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/app.sci.def.v3i1.463

Abstract

In modern defense applications, the accuracy and clarity of digital images are crucial, especially for tasks like surveillance, reconnaissance, and intelligence gathering. However, noise introduced during image acquisition or transmission significantly degrades image quality. This paper presents a comprehensive review of various noise removal techniques employed in digital image processing for defense systems. The review focuses on both linear and non-linear methods, including matrix decomposition, hybrid deep learning, Generative Adversarial Networks (GANs), and trimming filters. Emphasis is placed on the effectiveness of each technique in enhancing image quality while preserving critical details. The use of linear and non-linear methods such as deep learning-based approaches is shown to outperform traditional linear filters in handling complex noise patterns, particularly in scenarios requiring precise object detection and image restoration. The paper highlights a comprehensive overview of the researched literature and shows the latest trends and developments in the field. Finally, recommendations for future research and the development of more robust noise reduction methods are provided, aiming to improve operational effectiveness in defense applications.
Evaluating multiple time series models for consumer price index forecasting to support national defense decision-making Al Habsy, Muhammad Yusuf; Nur Rachmawati, Ro'fah; Jumadil Saputra
International Journal of Applied Mathematics, Sciences, and Technology for National Defense Vol. 3 No. 3 (2025): International Journal of Applied Mathematics, Sciences, and Technology for Nati
Publisher : FoundAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/app.sci.def.v3i3.865

Abstract

Price stability, as reflected in the Consumer Price Index (CPI), plays a crucial role in supporting economic resilience and national defense readiness. This study evaluates multiple time series forecasting models, including Error-Trend-Seasonal (ETS), Holt, Holt–Winter, SARIMA, SARIMAX with exogenous variables, and hybrid approaches combining Holt/Holt–Winter with SARIMA, to identify the most accurate method for predicting Indonesia’s CPI. Monthly data from 2017–2022 were analyzed using a training–testing split, and forecasting accuracy was assessed based on RMSE. The results show that the Holt–Winter model outperforms all other approaches, achieving the lowest RMSE value of 1.9159. Residual diagnostics confirm that the Holt–Winter model effectively captures trend and seasonal patterns, with errors behaving close to white noise. These findings highlight the superiority of Holt–Winter in providing reliable CPI forecasts, offering significant implications for economic policy formulation and strategic planning in the context of national resilience.
Co-Authors Al Husein, Fulkan Kafilah Christi, Damaris Nugrahita Hutagaol, Agnes Emanuela Jumadil Saputra Junoh, Ahmad Kadri bin Nur Rachmawati, Ro'fah