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All Journal Jurnal Penelitian Pendidikan IPA (JPPIPA)
Ussy Andawayanti
Brawijaya University
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Education Materials Science & Nanotechnology Physics

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Assessment of Bias-Correction Methods for CHIRPS Satellite Rainfall Estimates in the Petung Watershed, Indonesia Nafisah Zahrani; Ery Suhartanto; Ussy Andawayanti
Jurnal Penelitian Pendidikan IPA Vol 11 No 12 (2025): December
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i12.13250

Abstract

Satellite-based rainfall products such as CHIRPS are essential in data-scarce tropical regions, but they require bias correction to improve reliability. This study compares five correction techniques—Linear Regression, Linear Scaling, a static Correction Factor, a Genetic Algorithm (GA)-optimized Correction Factor, and a Python-based Temporal Analysis—against gauge observations in the Petung Watershed, East Java, Indonesia. The GA method optimized nonlinear correction coefficient by minimizing RMSE through iterative selection and mutation processes. The Temporal Analysis applied monthly dynamic scaling using Python scripts to account for seasonal rainfall variability. Model performance was assessed using the Nash–Sutcliffe Efficiency (NSE), Pearson correlation (R), and the RMSE–Standard Deviation Ratio (RSR). Linear Scaling achieved the best results (R = 0.857, NSE = 0.724, RSR = 0.547), followed by Linear Regression. The GA-based approach showed marginal improvement over the static factor (NSE = 0.658 versus 0.639). Temporal Analysis improved correlation (R = 0.813) but showed poor performance overall (RSR = 1.425), indicating residual errors exceeding natural data variability. While statistical methods performed best in this case, the poor results of the complex methods reflect implementation limitations—rather than inherent inferiority. This study also highlights the importance of including RSR alongside conventional metrics to expose residual structures often masked by high correlation.
Reliable Assessment of Long-Term Land Use Change to Support Sustainable Watershed Management Using Multi-Sensor Landsat Imagery Nafisah Zahrani; Ery Suhartanto; Ussy Andawayanti
Jurnal Penelitian Pendidikan IPA Vol 12 No 2 (2026)
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v12i2.14076

Abstract

Land use change analysis is widely applied in watershed studies, yet its reliability is often limited when multi-temporal classification accuracy is not systematically evaluated. This study aims to assess long-term land use change while emphasizing the accuracy and reliability of change detection in the Petung Watershed, East Java, Indonesia. Multi-temporal Landsat imagery from Landsat 7, 8, and 9 (2005–2024) was analyzed using a supervised classification approach implemented in ArcGIS. Classification accuracy was evaluated using a confusion matrix and the Kappa coefficient to ensure temporal consistency and methodological reliability. The results indicate a clear shift in land use patterns, characterized by a continuous decline in vegetation cover and a substantial expansion of agricultural and built-up areas. Classification reliability is supported by Kappa coefficient values ranging from 0.701 to 0.829, corresponding to substantial to almost perfect agreement, with the highest accuracy achieved in the later observation years. These findings demonstrate that the proposed supervised, GIS-based framework provides a reliable and replicable approach for accurately measuring long-term land use change at the watershed scale. While the methodology can be applied to other regions, the observed land use dynamics are specific to the Petung Watershed and reflect local environmental and anthropogenic conditions.
Co-Authors Ery Suhartanto Nafisah Zahrani