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The Journal of Experimental Life Sciences (JELS)
Published by Universitas Brawijaya
ISSN : 20872852     EISSN : 23381655     DOI : 10.21776/ub.jels
Core Subject : Health, Science,
Biochemistry, Genetics & Molecular Biology Immunology & microbiology Medicine & Pharmacology Neuroscience
The Journal of Experimental Life Science (JELS) is a scientific journal published by Postgraduate School, University of Brawijaya as distribution media of Indonesian researcher’s results in life science to the wider community. JELS is published in every four months. JELS published scientific papers in review, short report, and articles in Life Sciences especially biology, biotechnology, nanobiology, molecular biology, botany, microbiology, genetics, neuroscience, pharmacology, toxicology, and Applied Life Science including fermentation technology, food science, immunotherapy, proteomics and other fields related to life matter. JELS is a scientific journal that published compatible qualified articles to the academic standard, scientific and all articles reviewed by the expert in their field. The Journal of Experimental Life Science (JELS) have a vision to become qualified reference media to publish the best and original research results and become the foundation of science development through invention and innovation on cellular, molecular, nanobiology, and simulation work related to life matter rapidly to the community. The Journal of Experimental Life Science (JELS) has objectives to published qualified articles on research’s results of Indonesian researchers in life science scope. JELS encompasses articles which discuss basic principles on natural phenomenon with cellular, molecular, and nanobiology approach.
Arjuna Subject : Biokimia, Genetika dan Biologi Molekuler - Biokimia, Genetika dan Biologi Molekuler (Lain-Lain)
Articles 313 Documents
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Hybrid VGG16–LSTM Classification of Microscopic Bacterial Images for Environmental Microbiology Screening Yanti, Indah; Marjono, Marjono; Antariksa, Antariksa; Kurniawan, Andi; Anam, Syaiful; Bukhori, Hilmi Aziz
The Journal of Experimental Life Science Vol. 16 No. 2 (2026)
Publisher : Graduate School, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jels.2026.016.02.02

Abstract

While conventional culture-based, biochemical, and molecular identification methods remain fundamental in microbiology, computational image analysis can serve as an exploratory, supplementary tool for studying bacterial morphology. This proof-of-concept study evaluates a hybrid VGG16-LSTM model for microscopic bacterial image classification. This study utilized a small subset of the DIBaS dataset consisting of six bacterial classes: Acinetobacter baumannii, Escherichia coli, Lactobacillus plantarum, Micrococcus spp., Propionibacterium acnes, and Pseudomonas aeruginosa. The total dataset size is highly constrained at 124 images, with a correspondingly small number of images per class ranging from 20 to 23. The dataset was divided into training, validation, and testing subsets in a 70:20:10 ratio. All microscopic images were resized to 224 × 224 pixels, normalized, and dynamically augmented during training to improve data variability under these limited-sample conditions. A pre-trained VGG16 network was employed to extract spatial image features, and the final convolutional feature map was reshaped into a spatial sequence and processed using an LSTM layer for further feature learning. Three optimization algorithms, namely Adam, RMSprop, and stochastic gradient descent, were compared. Among them, the RMSprop-optimized model exhibited the highest metrics after 50 epochs, achieving 92.86% accuracy, 95.24% precision, 92.86% recall, and a 92.38% F1-score; however, these performance indicators must be interpreted with caution as they are based on a severely limited test set (approximately 12 images). Some misclassifications occurred between P. aeruginosa and E. coli, which may be attributed to their shared gram-negative rod-shaped morphology. This finding highlights the inherent biological challenge of distinguishing visually similar bacterial species from microscopic image analysis alone, underscoring that such models remain strictly experimental and are not suited for clinical-grade diagnosis or field-ready environmental screening.
Multiple Imputation of White Blood Cell Measurements for Early Emergency Assessment Using Tree-Based MICEforest Prasetya, Renaldi Primaswara; Ashar, Muhammad; Zaeni, Ilham Ari Elbaith
The Journal of Experimental Life Science Vol. 16 No. 2 (2026)
Publisher : Graduate School, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jels.2026.016.02.03

Abstract

Early clinical assessment in Emergency Departments (EDs) frequently relies on laboratory indicators that are often unavailable at the time of patient arrival. The White Blood Cell (WBC) count is a key marker for infection, inflammation, and acute physiological stress, yet missing or delayed WBC measurements are common in emergency care workflows. Conventional imputation methods typically replace missing values with single-point estimates, implicitly assuming full reliability and ignoring the uncertainty inherent in the imputation process. This paper investigates the use of multiple imputation strategies to address missing WBC measurements in emergency care data. A multivariate, tree-based imputation approach is employed to generate multiple plausible WBC values for each missing observation, capturing the inherent variability of laboratory uncertainty. Instead of focusing solely on point estimates, this study emphasizes the role of imputation variability as an indicator of confidence in reconstructed WBC values. Experiments are conducted on a synthetic emergency care dataset designed to mimic early ED scenarios, using three multiple imputations (M = 3) to compare single imputation and tree-based multiple imputation in terms of variability and uncertainty representation. Through analytical comparison and illustrative experiments on emergency care data with realistic missingness patterns, the proposed approach demonstrates that multiple imputation provides more informative and robust representations of missing WBC measurements compared to traditional single-imputation techniques. The results highlight how uncertainty-aware WBC reconstruction can better support early emergency assessment, particularly in high-risk and time-sensitive clinical scenarios. By focusing on WBC as a representative and clinically critical laboratory variable, this work underscores the importance of treating missing laboratory data as uncertain rather than deterministic. The proposed perspective offers practical insights for improving the reliability of data-driven decision support systems in emergency medicine and lays the groundwork for future integration with predictive modeling frameworks.
Optimizing Bacterial Cellulose from Coffee Husk Waste for Enhanced Vegan Leather Production Wahyudi, Aleyda Nur Halizah; Ardyati, Tri; Srihardyastutie, Arie; Habibi, Ananta Naufal; Kato, Masashi
The Journal of Experimental Life Science Vol. 16 No. 2 (2026)
Publisher : Graduate School, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jels.2026.016.02.04

Abstract

Coffee production in Indonesia continues to increase each year, necessitating sustainable waste management to reduce the accumulation of coffee husk waste in the environment. This waste still contains natural carbon sources that can potentially be utilized as a fermentation medium for bacterial cellulose (BC) production, which is promising for vegan leather applications. This study aims to determine the most effective combination of molasses and ammonium sulfate (ZA) to enhance BC production and optimize the BC pretreatment process for vegan leather application. In this study, chemical composition analysis was also carried out using Fourier transform infrared (FTIR) spectroscopy analysis on samples of coffee fruit peel following the thermal delignification process to determine the optimal conditions for the process. BC production was carried out over 14 days with treatment variations of 5%, 10%, and 15% molasses and 0.3%, 0.5%, and 1% ZA. The pretreatment involved pressing and coating with a coconut oil–beeswax mixture, followed by characterization of BC through physicochemical properties, weight, and thickness. The effect of varying concentrations of molasses and ZA in the coffee husk extract medium had an effect on the weight and thickness of the cellulose membrane produced, with optimal results in treatment P3 (10% molasses and 0.3% ZA) (weight 509 g; thickness 9.69 mm). The pH value was measured before and after fermentation and showed a decrease caused by the activity of acetic acid bacteria.

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