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All Journal Jurnal Teknik Pengairan: Journal of Water Resources Engineering Jurnal Penelitian Pendidikan IPA (JPPIPA)
Ery Suhartanto
Brawijaya University
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Education Engineering Materials Science & Nanotechnology Physics

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Analisis Sebaran Jaringan Penakar Hujan Dengan Metode Stepwise, Kriging & WMO Di DAS Serang Jawa Tengah Oksa Ega Hermawan; Lily Montarcih Limantara; Ery Suhartanto
Jurnal Teknik Pengairan: Journal of Water Resources Engineering Vol. 11 No. 2 (2020)
Publisher : Fakultas Teknik, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.pengairan.2020.011.02.07

Abstract

DAS Serang merupakan salah satu Daerah Aliran Sungai yang berada di Jawa Tengah dan memiliki luas sekitar 3648 km2. Hasil analisis sebaran pos hujan pada DAS Serang menggunakan metode Stepwise dan Kriging menunjukkan sebaran sudah bagus, hal ini ditunjukkan pada metode Stepwise dengan nilai R (koefisien antar variabel) mendekati angka 1 yang berarti hubungan antar stasiun kuat dan hasil RMSE pada analisis Kriging yang kecil. Namun jika ditinjau dari standar WMO (World Meteorological Oganization) luas daerah pengaruh pada tiap-tiap stasiun tidak memenuhi standar. Akhirnya dilakukan analisis Rekomendasi I (tujuh stasiun) dan Rekomendasi II (enam stasiun) dengan upaya menghilangkan stasiun-stasiun dengan luas daerah pengaruh terkecil agar memenuhi standar WMO. Namun penghilangan stasiun tidak dianjurkan dikarenakan stasiun hujan merupakan aset negara yang berharga, maka dilakukan upaya Rekomendasi III (sembilan stasiun) dengan menggeser stasiun-stasiun yang sudah ada tanpa ada penghilangan stasiun sehingga luas daerah pengaruh antar stasiun eksisting memenuhi standar WMO.
An Integrated Performance Index for Decentralized Water Supply Systems: A Case Study of SiPAS in the Brantas River Basin Ussy Andawayanti; Runi Asmaranto; M. Amar Sajali; Ery Suhartanto; Mustafa Mukti Hidayat; Rizki Tri Utami
Jurnal Penelitian Pendidikan IPA Vol 11 No 8 (2025): August
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i8.12137

Abstract

To develop and validate an Integrated Performance Index Model (IPIM) for evaluating Simple Water Supply Systems (SiPAS) in the Brantas River Basin, Indonesia. We surveyed, audited, and interviewed stakeholders at 31 SiPAS sites. Latent constructs were tested using SEM–PLS, and dimension weights were optimized with the Generalized Reduced Gradient method. The model explained system performance well (R² = 0.95) and showed high predictive reliability. The technical dimension exerted the strongest influence (72.10%), followed by managerial (26.70%) and social (15.10%) factors. The index differentiated low and high performing sites and was consistent with field audit findings. A companion mobile application enabled real time reporting and feedback to strengthen community participation. IPIM provides a concise, scalable framework for assessing decentralized water supply, prioritizing technical improvements while supporting managerial and social strengthening, and can inform investment and governance decisions for rural water services.
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.
Remote Sensing for Sustainable Development: Multi-Temporal Landsat Analysis of Land-Use Change and Urbanization in the Rejoso Watershed (2005–2024) Alyavara Mayang Ferynandari; Ery Suhartanto; Linda Prasetyorini
Jurnal Penelitian Pendidikan IPA Vol 12 No 1 (2026)
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v12i1.13639

Abstract

Rapid urbanization and shifting agricultural practices are reshaping watershed sustainability in Indonesia, yet their spatial and hydrological implications in the Rejoso Watershed (East Java) remain insufficiently quantified. This study evaluates land-use/land-cover (LULC) dynamics over 2005–2024 using multi-temporal Landsat imagery from five observation years (2005, 2011, 2015, 2020, and 2024). A hybrid classification (ISODATA clustering combined with visual interpretation) was validated using 250 ground points and confusion matrix metrics (overall accuracy and Kappa). Vegetation declined from 54.72% (197.11 km²) in 2005 to its minimum in 2020 at 38.06% (137.09 km²), then recovered to 41.28% (148.70 km²) in 2024. Agricultural land expanded from 32.14% (115.77 km²) to 52.28% (188.32 km²) in 2020 before contracting to 46.96% (169.14 km²) in 2024, indicating a notable post-2020 trend reversal with vegetation regrowth and reduced cropland extent. Built-up areas increased steadily (4.14% to 7.54%), while open land fluctuated and water bodies remained <1% with a slight decline. The 2020 map achieved the highest accuracy (95.83%; κ=0.96). These findings highlight upstream LULC reconfiguration and continued downstream urbanization, supporting integrated watershed management, upland rehabilitation, and stricter spatial planning.
Co-Authors Alyavara Mayang Ferynandari Ana Maria Jadfan Sidqi Fidari Lily Montarcih Limantara Linda Prasetyorini M. Amar Sajali Mustafa Mukti Hidayat Nafisah Zahrani Oksa Ega Hermawan Rizki Tri Utami Runi Asmaranto Ussy Andawayanti Ussy Andawayanti