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All Journal HAYATI Journal of Biosciences JURNAL SUMBER DAYA AIR
Sanjaya, Stephen
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Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Earth & Planetary Sciences Engineering

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Phenotypic and Genotypic Antimicrobial Resistance Profile of Salmonella spp. Isolated from Kampung Chicken Carcasses Nurjanah, Siti; Rahayu, Winiati P.; Sanjaya, Stephen
HAYATI Journal of Biosciences Vol. 31 No. 2 (2024): March 2024
Publisher : Bogor Agricultural University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.4308/hjb.31.2.229-240

Abstract

Kampung chicken meats have been widely consumed in Indonesia as well as broiler chicken. However, the extensive rearing allowed multidrug-resistant (MDR) bacteria exposure to kampung chicken, including through horizontal gene transfer. This study aimed to observe the correlation between the phenotypic and genotypic antimicrobial resistance profile of Salmonella spp. isolated from kampung chicken carcasses. Phenotypic antimicrobial resistance was evaluated by Kirby-Bauer's disk diffusion method, while the detection of drug resistance genes in seventeen isolates of Salmonella was carried out by PCR. All (17/17) isolates were susceptible to ampicillin and chloramphenicol. Most isolates of Salmonella were resistant to erythromycin (82%; 14/17), while the decreased susceptibility (intermediate category) most occurred in oxytetracycline (82%; 14/17). Salmonella Typhimurium showed a resistance pattern to more antimicrobial groups than S. Newport and S. Weltevreden. Several antimicrobial resistance genes (blaTEM, tetG, cmlA, gyrA) were present in all (17/17) isolates of Salmonella spp. Resistance to antimicrobial agents and the presence of resistance genes were not always related. This study could provide beneficial information regarding the transmission of antimicrobial resistance among Salmonella spp. from kampung chickens.
Evaluasi Data Hujan Berbasis Satelit untuk Menentukan Debit Aliran Masuk Waduk Selorejo Menggunakan Model HBV-96 Hidayat, Ivana Nathalia; Yudianto, Doddi; Sanjaya, Stephen
JURNAL SUMBER DAYA AIR Vol 21, No 1 (2025)
Publisher : Direktorat Bina Teknik Sumber Daya Air, Kementerian Pekerjaan Umum dan Perumahan Rakyat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32679/jsda.v21i1.907

Abstract

Effective reservoir management can be supported by applying rainfall-runoff hydrological models. However, one of the main challenges of such models lies in the availability of reliable rainfall data. Satellite-based rainfall data offer a viable alternative to address this issue. This study aims to evaluate the reliability of satellite-based rainfall data for hydrological applications, specifically for simulating reservoir inflow using the HBV-96 model in the Selorejo Reservoir. The rainfall data used in this study include satellite-based datasets from TRMM, GPM, and RCM, tested in both raw and corrected forms. The HBV-96 model parameters were calibrated using observed rainfall data from 1998 to 2008, achieving a correlation coefficient and Nash-Sutcliffe Efficiency (NSE) of 0.86 and 0.72, respectively, for simulated streamflow. The model's performance was subsequently verified using observed rainfall data from 2009 to 2016, yielding consistent results with a correlation coefficient and NSE of 0.832 and 0.71, respectively. These calibrated parameters were then applied to the satellite rainfall datasets. The findings reveal that, in general, corrected TRMM satellite rainfall data using regression equations were not suitable for hydrological modelling. However, TRMM data corrected using duration curves significantly reduced deviations by up to 50% compared to raw data and provided better-simulated streamflow results, aligning more closely with observed streamflow. Conversely, RCM rainfall data, whether raw or corrected, performed poorly in the HBV model, with negative NSE values. Meanwhile, the bias-corrected GPM satellite rainfall data demonstrated the best performance in the HBV model, with a maximum deviation of only 5.81%.
Co-Authors Doddi Yudianto Hidayat, Ivana Nathalia Siti Nurjanah Winiati P. Rahayu