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All Journal Journal of Degraded and Mining Lands Management Jurnal Teknik Informatika (JUTIF)
Thuy, Tran Thi Thanh
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT

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Development of an indigenous bacterial consortium for enhanced oil degradation in saline-contaminated soils Tuyen, Do Thi; Thuy, Tran Thi Thanh; Thanh, Nguyen Thi Kim; Cuong, Nguyen Viet; Loi, Nguyen Thi Thanh; Tien, Phi Quyet; Cuong, Ngo Cao
Journal of Degraded and Mining Lands Management Vol. 12 No. 4 (2025)
Publisher : Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15243/jdmlm.2025.124.7923

Abstract

This study developed the indigenous CR3.M3 bacterial consortium to enhance oil degradation in saline-contaminated soils. Seven hydrocarbon-degrading strains-closely related to Pseudomonas, Bacillus, and Niveispirillum species (92-99% 16S rRNA sequence similarity)-were isolated from polluted coastal soils using mineral salt media supplemented with crude oil and diesel. While phylogenetic analysis suggests close relationships to known oil-degrading species, formal taxonomic classification requires further genomic validation. The consortium degraded 70% of hydrocarbons within 13 days under saline conditions (?3% NaCl). Field trials in non-sterilized soils (3,542 mg/kg TPH) achieved 65.42% oil removal alongside microbial density increases from 6.26 to 8.11 Log??(CFU/g), confirming ecological compatibility. Its performance in both sterilized and native soils highlights adaptability for coastal bioremediation. Future research should optimize strain ratios, resolve taxonomic identities through whole-genome sequencing, and assess long-term ecological impacts to advance this sustainable remediation strategy.
Incremental CNN-k-NN Hybrid Facial Recognition for Helmeted Facial Recognition in IoT-Enabled Smart Parking: A Case Study at Universitas Mataram Widiartha, Ida Bagus Ketut; Husodo, Ario Yudo; Thuy, Tran Thi Thanh; Murpratiwi, Santi Ika
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5447

Abstract

Helmeted rider identification challenges traditional facial recognition, especially in Indonesian campuses like UNRAM, where motorbike use is prevalent and theft risks are high. This study develops a hybrid CNN-k-NN system for secure parking access. The dataset contains 2,800 augmented images (Haar Cascade crop, 224x224 grayscale), with features extracted via VGG16/ResNet and classified using k-NN (k=1, Euclidean/Cosine). The system achieves 95.62% accuracy, with precision, recall, and F1 scores of 0.96. Incremental retraining reduces processing time to under 1 second, compared to 30 minutes for full retraining. The use of cosine similarity improves accuracy slightly over Euclidean distance. This solution enhances IoT-based smart campuses by enabling efficient, real-time identification and reducing theft by improving access control. It is adaptable to low-resource environments, supporting scalable deployments in smart parking and campus security systems.
Co-Authors Ario Yudo Husodo, Ario Yudo Cuong, Ngo Cao Cuong, Nguyen Viet I B K Widiartha Loi, Nguyen Thi Thanh Murpratiwi, Santi Ika Thanh, Nguyen Thi Kim Tien, Phi Quyet Tuyen, Do Thi