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All Journal International Journal of Marine Engineering Innovation and Research International Journal of Applied Mathematics, Sciences, and Technology for National Defense
Maria Oktafiana Dedu
Universitas AKPRIND Indonesia
Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Transportation

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Microplastic Contamination in Yogyakarta's Rivers: Spatial Analysis and Factor Assessment to Identify Key Pollutants Eka Sulistyaningsih; Rokhana Dwi Bekti; Kris Suryowati; Erma Susanti; Gupita Cahyaning Mutiara; Maria Oktafiana Dedu
International Journal of Marine Engineering Innovation and Research Vol. 10 No. 1 (2025)
Publisher : Department of Marine Engineering, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25481479.v10i1.4743

Abstract

River water quality monitoring aims to determine the state of river water quality and to ensure its safety for human health and the sustainability of its use. Some important parameters that are often used to measure river water quality include chemical oxygen demand (COD), biological oxygen demand (BOD), total suspended solids (TSS), pH, Temperature, and microplastic content. This study uses multiple linear regression to determine which factors contribute significantly to river water quality. Samples were collected from the Winongo, Gadjah Wong, Bulus, Oyo, Belik, Tambakbayan, Opak, and Kuning rivers in Daerah Istimewa Yogyakarta (DIY) and distributed in 20 points. The results of the correlation matrix show the relationships between the variables in the data. The DO variable has the most substantial relationship with microplastics, suggesting that water quality, measured by oxygen levels, may be related to microplastic pollution. The relationship between pH and Temperature is also moderate. However, other relationships tend to be weak, suggesting that other factors may be more influential in determining these variables' relationships. The multiple linear regression model shows that an increase in pH, a decrease in Temperature, an increase in DO, and a decrease in TSS will increase the amount of microplastics. Furthermore, through spatial analysis and geographically Weighted Regression (GWR) modelling, DO significantly affects 12 observation points and does not affect eight. The spatial approach shows that the causes of river water pollution are different in each location. Therefore, each site's treatment is also different according to its characteristics.
Kernel selection in support vector regression for forecasting international tourist arrivals: Implications for economic resilience and national defense Kris Suryowati; Maria Oktafiana Dedu; Rokhana Dwi Bekti; Junaidi
International Journal of Applied Mathematics, Sciences, and Technology for National Defense Vol. 4 No. 2 (2026): 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.v4i2.1496

Abstract

Background: International tourist arrivals are an important component of Indonesia's tourism economy and are influenced by temporal patterns and macroeconomic conditions. Accurate forecasting is therefore important to support tourism planning and economic resilience. Aims: This study aims to develop and evaluate a Support Vector Regression (SVR) model with different kernel functions for forecasting international tourist arrivals in Indonesia and to compare its performance with the ARIMAX model. Method: Monthly tourist arrival data from January 2017 to November 2024 were analyzed using SVR with linear, polynomial, radial basis function, and sigmoid kernels. Data were standardized using StandardScaler, while Grid Search was used to optimize the model parameters. Inflation and the BI Rate were incorporated as explanatory variables. The best-performing SVR model was compared with ARIMAX(1,1,1) using MSE, RMSE, MAD, and MAPE. Result: The SVR with a second-degree Polynomial Kernel achieved the best performance, with an MSE of 3,401,140,779.52, RMSE of 58,319.30, MAD of 44,565.19, and MAPE of 12.94%. The model outperformed ARIMAX(1,1,1), which obtained a MAPE of 16.23%. The results indicate that the polynomial kernel can better capture nonlinear patterns in tourist arrival dynamics. Conclusion: The SVR Polynomial model provides a useful approach for forecasting international tourist arrivals in Indonesia. Its forecasting capability can support tourism planning, resource allocation, and early identification of changes in tourism demand, contributing to more adaptive and resilient tourism management. 
Co-Authors Eka Sulistyaningsih Erma Susanti Gupita Cahyaning Mutiara Junaidi Junaidi Kris Suryowati Rokhana Dwi Bekti