cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Hengki Tamando Sihotang
Contact Email
hengkitamando26@gmail.com
Phone
+6281381251442
Journal Mail Official
hengkitamando26@gmail.com
Editorial Address
Romeby Lestari Housing Complex Blok C, No C14 Deliserdang, North Sumatra, Indonesia
Location
Unknown,
Unknown
INDONESIA
International Journal of Basic and Applied Science
Published by Institute of Computer Science
ISSN : 23018038     EISSN : 27763013     DOI : https://doi.org/10.35335/ijobas
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Physics
International Journal of Basic and Applied Science provides an advanced forum on all aspects of applied natural sciences. It publishes reviews, research papers, and communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. Electronic files and software regarding the full details of the calculation or experimental procedure, if unable to be published in a normal way, can be deposited as supplementary electronic material.
Arjuna Subject : Umum - Umum
Articles 3 Documents
 1 
Search results for , issue "vol. 15 no. 2 (2026): basic and applied science" : 3 Documents clear
Visual Classification of Spirulina Microalgae Maturity Stages Based on Color Using Convolutional Neural Networks Dicky Hermawan; Hasanul Fahmi Zuhri
International Journal of Basic and Applied Science Vol. 15 No. 2 (2026): Basic and Applied Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v15i2.875

Abstract

Manual assessment of microalgae maturity is subjective and does not scale well. This study evaluates whether a convolutional neural network can classify Spirulina microalgae into five visual maturity stages (dead, unripe, under-ripe, medium-ripe, ripe) using color alone. From 5,005 images collected over three months at a single facility, byte-exact duplicates accounted for 41.9% of the dataset; after removal, 2,909 unique images remained. To prevent near-duplicate images from the same session leaking across folds, evaluation used a group-stratified 5-fold cross-validation scheme, with groups defined via perceptual-hash clustering. An ablation across four preprocessing configurations found that combining CLAHE with augmentation, the original proposed method, was numerically weakest (94.74% ± 2.15% accuracy versus 97.43% ± 0.89% with no preprocessing), differing significantly from CLAHE alone (p = 0.021) though not from the plain baseline at this sample size (p = 0.085). A separate comparison, matching fine-tuning depth (last 30 layers) and preprocessing across a custom CNN, MobileNetV2, and ResNet50, found ResNet50 highest at 99.52% ± 0.18% accuracy, significantly ahead of the custom CNN (97.06% ± 1.39%; p = 0.024), while MobileNetV2 showed severe fold-to-fold instability. The custom CNN's advantage lay in efficiency: 54.8 times fewer parameters and roughly 3.5 times faster inference than ResNet50, for only a small accuracy trade-off. A brief, unlabeled field test on six images outside the lab setting showed frequent disagreement among the three models, suggesting limited transfer from controlled to field conditions. Grad-CAM maps are presented as an indication of model attention, not evidence of underlying chlorophyll degradation biochemistry
Spatio-Temporal Disinformation Detection Using GNN–LSTM and Cross-Attention for South China Sea Information Analysis Adam Mardamsyah; Marsetio Marsetio; Bambang Irwanto; Robby Mochammad Taufik
International Journal of Basic and Applied Science Vol. 15 No. 2 (2026): Basic and Applied Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v15i2.915

Abstract

The rapid propagation of disinformation through social media presents a significant challenge because information diffusion is characterized by complex interactions among users and continuously evolving temporal patterns. Conventional deep-learning and transformer-based approaches primarily focus on textual or sequential representations and may not fully capture the structural and temporal characteristics of information propagation. This study proposes a spatio-temporal framework for disinformation detection that integrates Graph Neural Network (GNN)-based spatial representation, Long Short-Term Memory (LSTM)-based temporal modeling, and an adaptive cross-attention mechanism for spatial–temporal fusion. The GNN is employed to model relational dependencies within social-media interaction networks, while LSTM captures the temporal evolution of graph-based representations. Cross-attention is subsequently introduced to adaptively model the interaction between spatial and temporal representations rather than relying solely on fixed feature concatenation. The proposed framework is evaluated using a social-media dataset associated with the South China Sea and compared with CNN, LSTM, GNN, BERT, and GAT-BERT baselines. Experimental results show that the proposed model achieves 95.84% accuracy, 95.30% precision, 95.60% recall, and 95.45% F1-score, outperforming the strongest baseline, GAT-BERT, by 2.63 percentage points in accuracy and 2.55 percentage points in F1-score. The ablation analysis further demonstrates that integrating LSTM with GNN improves accuracy by 5.32 percentage points, while the subsequent incorporation of cross-attention provides an additional improvement of 3.74 percentage points. These findings indicate that adaptive spatial–temporal fusion contributes beyond the independent use of graph and sequential encoders and provides a potential decision-support approach for analyzing dynamic disinformation propagation in geopolitical social-media environments.
Slice-Group-Level Brain Tumor Classification Using CRNN-GRU Mohammed Z.M. Almadani; Maslita Abd Aziz; T. Zalizam T. Muda
International Journal of Basic and Applied Science Vol. 15 No. 2 (2026): Basic and Applied Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v15i2.944

Abstract

Interpreting brain MRI for tumor diagnosis is time-consuming and varies between raters, and single-slice decisions can be fragile. This paper compares two classification pipelines on the Brain Tumor Classification (MRI) Dataset from Kaggle (glioma, meningioma, pituitary, no-tumor) of T1-contrast-enhanced images. The first is a per-slice CNN. The second is a per-group CRNN-GRU that reads an ordered sequence of ten slices with a bidirectional GRU and inter-slice attention. Because the dataset provides no patient identifiers, the analysis unit is a surrogate slice group, and the splits are group-disjoint by construction; all models are compared at the group-level. The per-slice CNN reached 80.0% slice-level accuracy. Aggregated to the group-level, the same CNN reached 92.68% by majority voting and 95.12% by mean-probability pooling. The CRNN-GRU reached 90.2% in a single pass, and 87.8% with test-time augmentation. No pairwise difference is statistically significant (exact McNemar, all p >= 0.25). The results do not show that sequential modelling and inter-slice attention improve on simple probability aggregation on this dataset, and the simplest configuration was the most accurate. A content-level audit found a small number of images duplicated across partitions, so all figures should be read as upper bounds. These results describe internal performance on one dataset under group-disjoint evaluation with a content-level leakage audit and do not establish clinical generalization.

Page 1 of 1 | Total Record : 3

 1 

Filter by Year

2026 2026


Reset
Filter By Issues
All Issue Vol. 15 No. 2 (2026): Basic and Applied Science Vol. 15 No. 1 (2026): Basic and Applied Science Vol. 14 No. 4 (2026): March: Computer Science Vol. 14 No. 3 (2025): Optimization and Artificial Intelligence Vol. 14 No. 2 (2025): Optimization and Computer Science Vol. 14 No. 1 (2025): Computer Science, Engineering, Basic and Applied mathematics Science Vol. 13 No. 4 (2025): Computer Science, Engineering, Basic and Applied mathematics Science Vol. 13 No. 3 (2024): Dec: Optimization and Artificial Intelligence Vol. 13 No. 2 (2024): Sep: Basic and Applied Science Vol. 13 No. 1 (2024): June: Basic and Aplied Science Vol. 12 No. 4 (2024): March: Basic and Applied Science Vol. 12 No. 3 (2023): December: Basic and Applied Science Vol. 12 No. 2 (2023): Sep: Basic and Applied Science Vol. 12 No. 1 (2023): June: Basic and Applied Science Vol. 11 No. 4 (2023): March: Basic and Applied Science Vol. 11 No. 3 (2022): December: Basic and Applied Science Vol. 11 No. 2 (2022): Sep: Basic and Applied Science Vol. 11 No. 1 (2022): June: Basic and Applied Science Vol. 10 No. 4 (2022): March: Basic and Applied Science Vol. 10 No. 3 (2021): December: Basic and Applied Science Vol. 10 No. 2 (2021): September: Basic and Applied Science Vol. 10 No. 1 (2021): June: Basic and Applied Science Vol. 9 No. 4 (2021): March: Basic and Applied Science Vol. 9 No. 3 (2020): December: Basic and Applied Science Vol. 9 No. 2 (2020): September: Basic and Applied Science Vol. 9 No. 1 (2020): June: Basic and Applied Science Vol. 8 No. 4 (2020): March: Basic and Applied Science Vol. 8 No. 3 (2019): December: Basic and Applied Science Vol. 8 No. 2 (2019): September: Basic and Applied Science More Issue