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Jurnal Natural
Published by Universitas Syiah Kuala
ISSN : 14118513     EISSN : 25414062     DOI : https://doi.org/10.24815/jn
Core Subject : Science, Agriculture,
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemistry Earth & Planetary Sciences Energy Immunology & microbiology Neuroscience Physics
Jurnal Natural (JN) aims to publish original research results and reviews on sciences and mathematics. Jurnal Natural (JN) encompasses a broad range of research topics in chemistry, pharmacy, biology, physics, mathematics, statistics, informatic and electronic.
Arjuna Subject : Matematika - Matematika (Lain-Lain)
Articles 9 Documents
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Search results for , issue "Volume 25 Number 3, October 2025" : 9 Documents clear
Electrofacies classification of a mixed carbonate-siliciclastic reservoir using machine learning techniques ADHARI, MUHAMMAD RIDHA; WIRANDHA, FREDDY SAPTA; YANIS, MUHAMMAD; KARDAWI, MUHAMMAD YUSUF
Jurnal Natural Volume 25 Number 3, October 2025
Publisher : Universitas Syiah Kuala

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Abstract

Many scientific fields, including the geosciences, have successfully employed machine learning to address numerous significant issues. Current studies show that the application of machine learning within the geosciences is still in its early stages, and there is a huge potential for this technique that need to be explored. This research focuses on the Late Permian Beekeeper Formation from the Perth Basin, Australia. It aims to improve our understanding of the application of machine learning to characterise subsurface rock formations. The objectives of this study are threefold: (1) to conduct cutting, crossplot, and modern machine learning analyses on a mixed carbonate-siliciclastic reservoir; (2) to compare the results from the aforementioned analyses and to interpret the electrofacies and lithofacies; and (3) to understand the degree of accuracy of the application of machine learning in the characterisation of the subsurface rock formations. Cutting, crossplotting, and modern machine learning analyses have been conducted to achieve the aim and objectives of this study. Seven electrofacies, associated with nine lithofacies, were identified within the studied data, and these were classified into carbonate-dominated facies group, siliciclastic-dominated facies group, and mixed carbonate-siliciclastic facies group. Results also show the presence of stratal and compositional mixing within the Beekeeper Formation. A combination of cutting, crossplot, and machine learning analyses can provide a better, more accurate, and more reliable interpretation of the facies of the Beekeeper Formation. This study is expected to advance our understanding of the application of machine learning in geosciences.
Design analysis of drainage system design on coal mining land BAHARUDDIN, ICHSAN INVANNI; HAR, Rusli; ASSHIDIQQI, MUHAMMAD HABIB; ANARTA, RUDY; Tanjung, Denny Akbar
Jurnal Natural Volume 25 Number 3, October 2025
Publisher : Universitas Syiah Kuala

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PT Bumi Bara Makmur Mandiri is a mining company engaged in coal mining, located in Hajran Village, Bathin XXIV District, Batang Hari Regency, Jambi Province. The company employs an open-pit mining method, which results in the formation of depressions that act as rainwater catchment areas, creating pools (sumps). To manage these sump areas effectively for mining activities, a mine dewatering drainage system is implemented. Over the past ten years, rainfall data indicates a catchment area of 246.02 hectares, with an estimated rainfall of 280.88 mm for a return period of ten years. The groundwater that accumulates in the pooling area has an inflow rate of 0.11 m/s, leading to a total water discharge entering PIT A of 39.12 m/second. Currently, there is one pump available with a power capacity of 10.83 kW to remove this water. Due to the significant water discharge, it is essential to design an economical open flow that can handle a flow rate of 2.96 m/s. Furthermore, a design for a holding pond is required, as the existing capacity is insufficient to accommodate the incoming water discharge, which hampers the sedimentation process. To address this, a redesigned settling pond with dimensions of 46 m x 36 m x 7 m and six compartments has been proposed. This solution will enhance the effectiveness of the mine drainage process
Microbiological and urinalysis assessment of UTIs in COVID-19 patients at Dr. Zainoel Abidin Hospital, Banda Aceh. MAHDANI, WILDA; ZIKRI, MUHAMMAD SHADIQUL; SAMINAN, SAMINAN
Jurnal Natural Volume 25 Number 3, October 2025
Publisher : Universitas Syiah Kuala

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Abstract

One of the secondary infections observed among COVID-19 patients is a Urinary Tract Infection (UTI). The presence of bacteria and fungi causes UTI, which can certainly occur after a urine culture. Urinalysis is one of the critical examinations to diagnose UTIs and assess functional disorders in the urinary tract. This study examines the characteristics of microbes isolated from urine specimens and urinalysis results in COVID-19 patients at Dr. Zainoel Abidin Hospital, Banda Aceh. This observational analytic study used secondary data from urine culture and urinalysis results of COVID-19 patients by implementing the total sampling technique. This study involved 110 urine culture data from confirmed COVID-19 patients. Comparative analysis of parameters between positive and negative urine culture groups with urinalysis results using categorical variables through the Chi-square test and Fisher's Exact test. The findings suggest that Escherichia coli, Enterococcus spp., and Candida spp. are the predominant uropathogens in COVID-19 patients, with urinalysis frequently indicating leukocytosis as a marker of urinary tract involvement. Urine culture is the gold standard for quantitatively diagnosing UTIs by determining bacterial density and identifying specific pathogens. Urinalysis, which checks for leukocytes in the urine, can support these results.
In silico characterization of adh1 gene encoding alcohol dehydrogenase 1 (ADH1) from non-conventional yeast, Wickerhamomyces and Pichia spp HARTONO, FAISAL DINIAMAL; MERYANDINI, ANJA; ASTUTI, RIKA INDRI
Jurnal Natural Volume 25 Number 3, October 2025
Publisher : Universitas Syiah Kuala

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Abstract

Wickerhamomyces anomalus and Pichia kudriavzevii have high potential to produce bioethanol under high stress condition, due to their stress-tolerant properties. To elucidate and develop an efficient and sustainable bioethanol production, characterization of ethanol fermentation reactions is highly substantial. Ethanol fermentation employs key enzyme ADH1 encoded by ADH1 gene, important for conversion of acetaldehyde to ethanol. However, structural studies about alcohol dehydrogenase1 from these genera of yeasts are limited. This study aimed to detect the alcohol dehydrogenase 1 gene from Pichia spp. Using computational-bioinformatics approaches. The adh1 gene was amplified by PCR, visualized by electrophoresis, and analysed for sequence homology by BlastN and BlastP. The enzyme structure was constructed by SWISS-MODEL and I-TASSER with validation by Ramachandran plot, QMEAN4, and Local Quality Estimate. The Similarity and homology analysis of ADH1 genes and their corresponding protein sequence of yeast isolates showed that the ADH1gene was successfully detected. Multiple sequence alignment (MSA) and phylogenetic tree revealed that W. anomalus BT1-BT6 has close evolutionary relationship with ADH1 from Saccharomyces cerevisiae sequence while P. kudriavzevii IP4 showed different pattern. The ADH 1 enzyme model, generated using the SWISS-MODEL web server, demonstrated the best stereochemical quality, with a Ramachandran plot value of 100% for W. anomalus BT1 and 99.3% for P. kudriavzevii IP4. Superimposition of 3D-predicted model of ADH1 from W. anomalus BT1 and P. kudriavzevii 1P4 showed an exact match with amino acid in Zn2+ binding sites, confirming the ADH1 metaloenzyme properties. These findings provide structural insights about ADH1 genes and protein properties which can be used further for the development of efficient and high productivity of bioethanol productions through genetic and protein engineering.
Synthesis and characterization of alumina-chitosan modified monolithic activated carbon biosorbent from oil palm empty fruit bunches for acid mine drainage remediation saisa, saisa; ELVITRIANA, ELVITRIANA; SARTIKA, ZULHAINI; ERDIWANSYAH, ERDIWANSYAH
Jurnal Natural Volume 25 Number 3, October 2025
Publisher : Universitas Syiah Kuala

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This study reports the synthesis and characterization of a monolithic activated carbon adsorbent modified with alumina and chitosan (Al-Chit/OAC), derived from oil palm empty fruit bunches (OPEFB). The adsorbent was fabricated through pyrolysis, followed by alumina incorporation and chitosan impregnation. FTIR analysis confirmed the presence of functional groups including OH stretching (3640cm), CH stretching (2920cm), CN/CO stretching (10551031cm), and AlO vibrations (693, 522, 495cm), indicating successful surface modification. TGA revealed two major stages of thermal degradation, with a total mass loss of 17.4% and a final residue of 17.55%, reflecting the presence of thermally stable inorganic components. SEM imaging showed a heterogeneous and porous surface with agglomerated particles and interparticle voids, suggesting enhanced surface accessibility. Even though we didn't test how well it absorbs substances, the physical and chemical properties of the composite show it could be very useful for cleaning up acid mine drainage (AMD) in the future. Further studies are recommended to validate its adsorption performance.
A station-scale modeling framework for heavy rainfall classification in tropical weather using representative machine learning approaches MULSANDI, ADI; MIFTAHUDDIN, MIFTAHUDDIN
Jurnal Natural Volume 25 Number 3, October 2025
Publisher : Universitas Syiah Kuala

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Abstract

Extreme daily rainfall in rapidly urbanizing tropical cities frequently overwhelms drainage and disrupts critical services, yet station-scale forecasting remains limited by convective variability and sparse observations. This motivates lightweight, interpretable machine-learning tools that operate on routine station data. We propose and evaluate a station-scale framework to classify heavy-rainfall days (50 mm) in a humid tropical setting. Using 1,796 daily observations from the Soekarno-Hatta Meteorological Station (20182022), we engineered lag-informed predictors (e.g., previous-day rainfall, 3-day sums/means) and compared three representative classifiers, Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF). Class imbalance was addressed with class-weighted training, and models were assessed on a held-out test set using precision, recall, F1, and Receiver Operating Characteristic Area Under the Curve (ROC-AUC). LR achieved the highest recall (0.429), indicating moderate sensitivity to rare heavy-rainfall events, whereas RF yielded the best probabilistic discrimination (AUC = 0.619) but failed to flag positives at the default threshold; SVM displayed near-random behavior. Feature analyses highlighted humidity, temperature, and recent rainfall accumulation as the most influential predictors, consistent with tropical convective processes. Despite severe class imbalance, simple, station-based classifiers can extract actionable signals for rare-event screening in data-limited tropical regions. Operational value is likely to improve through probability calibration and threshold tuning, ensemble integration, and spatial generalization to multi-station settings.
Simultaneous inference for empirical best predictors in generalized linear mixed models: A poverty study in West Java SAHAMONY, NUR FITRIYANI; SADIK, KUSMAN; KURNIA, ANANG
Jurnal Natural Volume 25 Number 3, October 2025
Publisher : Universitas Syiah Kuala

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Abstract

Accurate poverty mapping at the district and municipal levels remains challenging due to small sample sizes in household surveys, which often result in unstable direct estimates. To address this issue, this study employs microdata from the 2023 National Socioeconomic Survey (SUSENAS) to estimate household-level poverty proportions across 27 districts and municipalities in West Java Province using a binomial Generalized Linear Mixed Model (GLMM) combined with the Empirical Best Predictor (EBP) and Simultaneous Confidence Intervals (SCI). The GLMM framework captures household characteristics and random area effects to account for spatial heterogeneity. Three SCI approachesBonferroni correction, Bootstrap-t, and the Simes procedurewere implemented to evaluate EBP uncertainty while controlling the family-wise error rate. Results reveal substantial disparities, with Tasikmalaya (21.7%), Bandung Barat (15.5%), and Cianjur (12.8%) consistently above the provincial average of (6.8%), while urban areas such as Cimahi, Bekasi, and Depok report poverty rates below 2%. All methods achieved full empirical coverage (ECP = 100%), although interval widths differed: Bonferroni produced the widest intervals (AIW = 44.99), Bootstrap-t yielded the narrowest and most efficient (AIW = 29.16), and Simes provided intermediate but highly consistent results (AIW = 33.24). These findings underscore the methodological importance of integrating GLMM, EBP, and SCI for small area estimation while offering practical insights for evidence-based policy development and poverty reduction strategies in Indonesia.
Effect of seed priming on the germination of upland rice (Oryza sativa L.) sown in acidic soil AGUSTIANSYAH, AGUSTIANSYAH; TIMOTIWU, PAUL BENYAMIN; ADHINUGRAHA, QUDUS SABHA
Jurnal Natural Volume 25 Number 3, October 2025
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Upland rice is a type of rice that can be grown in Ultisol. The obstacles to cultivating plants in Ultisol are low pH and high aluminum content which hinders seed germination. Seed priming is a technology that can overcome problems with Ultisol. The aim of this research was to determine the effect of priming on the germination of upland rice seeds in acid soil conditions. The experiment conducted on non-factorial Completely Randomized Design (CRD) consisting of 8 treatments with 3 replications. The data were analyzed for variance and followed by Honest Significant Differences (HSD) at 5% using the Statistic R Program. Seed priming treatments tested were (1) Untreatment; (2) Hydropriming; (3) priming GA3 25 ppm; (4) priming GA3 50 ppm; (5) priming PEG 6000 10%; (6) priming PEG 6000 20%; (7) 0.5% KNO3 priming, and (8) 1% KNO3 priming. Rice seed are soaked in priming for 24 hours. The upland rice seeds of the Inpago 13 Fortiz variety were planted in Ultisol soil media with a pH of 4.45, Al content of 0.44%, and Fe 1.37%. The results showed that the priming treatment increased the germination and themost effective treatment was priming GA3 50 ppm, each value of showed germination (92.38%), germination speed (19.71% day-1), vigor index (83.81%), and time of appearance of plumule (2.96 day).
Biosynthesis of silver nanoparticles with antioxidant activity using Mesona palustris Bl. leaf water extract Retnaningtyas, Yuni -; Aida, Siti Nor; Kristiningrum, Nia
Jurnal Natural Volume 25 Number 3, October 2025
Publisher : Universitas Syiah Kuala

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Mesona palustris Bl. has considerable potential. Still, it has yet to be matched by its utilization in nanotechnology, especially nanoparticles (1-100 nm), which have superior physicochemical properties and activities. This laboratory study aims to determine the potential of of Mesona palustris Bl. leaf water extract as a bioreductant and capping agent in the biosynthesis of silver nanoparticles (AgNPs), as well as to evaluate the antioxidant activity of the synthesized AgNPs using the DPPH assay. The biosynthesis method produced optimum AgNPs with a ratio of 1% Mesona palustris Bl. leaf water extract, AgNO3, and dispersing medium 0.75:7:5 (v/v) synthesized for 60 minutes at 55C. The maximum wavelength of AgNPs produced was 433 nm with an absorbance of 0.677. FT-IR spectrophotometric characterization showed that phenolic compounds in the Mesona palustris Bl. leaf water extract is thought to act as a bioreductors and capping agent in the synthesis of AgNPs. SEM results showed that the AgNPs were spherical. Meanwhile, the PSA test results showed the average size of AgNPs was 81 nm, and the polydispersity index was 0.323 (moderately polydispersed). The IC50 value of AgNPs synthesized with Mesona palustris Bl. leaf water extract against DPPH free radicals was 71.501 1.347 g/mL, which is included in the potent antioxidant and better than Mesona palustris Bl. leaf water extract.

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