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International Journal of Advances in Intelligent Informatics
Published by Universitas Ahmad Dahlan
ISSN : 24426571     EISSN : 25483161     DOI : 10.26555
Core Subject : Science,
Computer Science & IT
International journal of advances in intelligent informatics (IJAIN) e-ISSN: 2442-6571 is a peer reviewed open-access journal published three times a year in English-language, provides scientists and engineers throughout the world for the exchange and dissemination of theoretical and practice-oriented papers dealing with advances in intelligent informatics. All the papers are refereed by two international reviewers, accepted papers will be available on line (free access), and no publication fee for authors.
Arjuna Subject : -
Articles 374 Documents
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Surgical-aware video masked autoencoders with phase-conditioned attention for laparoscopic action recognition Hakim Nasaoui; Hassan Silkan; Insaf Bellamine
International Journal of Advances in Intelligent Informatics Vol 12, No 2 (2026): May 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i2.2357

Abstract

Fine-grained surgical action recognition in laparoscopic videos remains a challenge, even with recent advances in deep learning. While current VideoMAE approaches reach 89.11% accuracy on cholecystectomy tasks, they face specific limitations. Random masking strategies often miss surgical instruments that occupy only 10% to 15% of frames. Furthermore, context-independent models struggle with visually similar actions across different phases, and symmetric two-stream architectures tend to waste computational resources. To solve this, we developed SA-VideoMAE, a surgical-aware video masked autoencoder specifically designed for laparoscopic action recognition. Our method utilizes surgical-aware adaptive masking that integrates YOLOv7x object detection to prioritize instrument patches. This increased instrument visibility from 10% to 60% during training, ensuring the model focuses on action-relevant regions rather than static backgrounds. We also utilized phase-conditioned hierarchical attention to inject learnable phase embeddings into the attention mechanisms, enabling the model to disambiguate visually similar actions based on surgical context. For efficiency, our asymmetric dual-stream architecture processes RGB using ViT-Base (86M parameters) and optical flow using ViT-Tiny (5.7M parameters), achieving a 47% parameter reduction compared to symmetric designs. Our training process then balanced reconstruction, classification, temporal consistency, and phase prediction through a novel multi-objective optimization strategy. Results from Cholec80's Calot's Triangle Dissection phase show 93.5% accuracy, representing a 4.4 percentage-point improvement over the verified baseline. Notably, challenging action recall improved from 51% to 74% while maintaining real-time inference at 62ms per clip. These findings demonstrate that encoding surgical domain knowledge into video architectures significantly enhances action recognition performance.
Enhancing clinical safety in automated breast ultrasound segmentation through a sensitivity-prioritized detection and statistical fail-safe mechanism Daffa Muhamad Azhar; Nanik Suciati
International Journal of Advances in Intelligent Informatics Vol 12, No 2 (2026): May 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i2.2408

Abstract

Accurate breast ultrasound (BUS) lesion segmentation is critical for early diagnosis but is challenged by image artifacts and the reliance of foundation models on manual prompting. Existing automated frameworks often lack robust fail-safe mechanisms, leading to missed diagnoses. To address this reliability gap, this study proposes a novel, fully automated hybrid segmentation framework that synergistically integrates three key components: (1) a recall-optimized YOLOv9 detector tailored to minimize clinical false negatives; (2) a MedSAM2 foundation model efficiently fine-tuned via Low-Rank Adaptation (LoRA) for ultrasound specifics; and (3) a statistical fallback mechanism that acts as a crucial safety net to recover spatial prompts during detection failures. Evaluated on the public BUSI dataset, the recall-dominant detection module achieved a Recall of 0.8238. Supported by this robust prompting and fallback strategy, the segmentation module achieved a Dice coefficient of 0.8818 and an IoU of 0.8113. By effectively integrating specialized detection with adaptive segmentation and a statistical fail-safe, the proposed pipeline offers a highly reliable automated approach for computer-aided screening systems.
Enhancing chest image classification using PCA and CNN model Thair A. Kadhim
International Journal of Advances in Intelligent Informatics Vol 12, No 2 (2026): May 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i2.2300

Abstract

Deep learning (DL) has a significant impact on X-ray images for diagnosing and categorizing a range of lung disorders. The proliferation of extensive annotated image datasets has resulted in the emergence of convolutional neural networks (CNNs) as a useful instrument for the tasks of image recognition and categorization. Despite this abundance, the primary challenge in medical diagnosis continues to be the classification of these images. This research aims to enhance image classifiers by using the CNN model; both training and testing datasets underwent analysis using the suggested CNN system. An analysis and comparison are conducted on the impact of feature extraction using the Principal Components Analysis (PCA) technique. The study attains maximal classification efficiency by preparing images through dimensionality reduction before classification, concurrently enhancing the efficacy of CNNs in feature extraction. The optimal tuning strategies for enhancing the performance of the proposed CNN were found to include boosting the number of epochs, changing the optimizer, and decreasing the learning rate, as well as improving algorithm gains by using pre-trained weights. The suggested system outperforms previously utilized approaches like VGG or DenseNet, with more than 99.80 percent accuracy, precision, recall, and F1-score values. The suggested methodology demonstrates considerable promise for enhancing the efficacy and precision of lung disease diagnoses derived from chest X-ray images, thereby offering clinicians beneficial decision support and accelerating the implementation of treatment strategies. Furthermore, the developed model facilitates the identification of pulmonary ailments, encompassing critical conditions like COVID-19, thereby facilitating timely and efficacious patient care.
Channel-spatial dual-attention for plant disease detection: CBAM-ECA integrated CNN models with visual explainability Anton Anton; Supriadi Rustad; Guruh Fajar Shidik; Abdul Syukur
International Journal of Advances in Intelligent Informatics Vol 12, No 3 (2026): August 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i3.2377

Abstract

Early detection of plant leaf diseases is critical for minimizing crop losses and supporting precision agriculture. While Convolutional Neural Networks (CNNs) have demonstrated high accuracy in image-based diagnosis, conventional architectures may not optimally balance spatial localization and channel-wise feature refinement, particularly in multi-crop classification settings. This study proposes a redundancy-aware dual-attention architecture, termed ATSA-DenseNet, which integrates the spatial branch of the Convolutional Block Attention Module (CBAM-Spatial) with Efficient Channel Attention (ECA) within a DenseNet121 backbone. Unlike prior dual-attention frameworks that retain full CBAM and introduce channel-level redundancy, the proposed design isolates complementary spatial and channel mechanisms to improve representational efficiency without increasing computational complexity. The framework is evaluated on controlled multi-crop PlantVillage-derived datasets comprising tomato, potato, pepper, and maize. Across both 3-crop and 4-crop configuration, ATSA-DenseNet consistently outperforms baseline DenseNet121 and single-attention variants, achieving 99.94% accuracy and 0.9994 macro-F1 on the 4-crop setting while maintaining a lightweight footprint (6.96M parameters, 2.87G FLOPs). Grad-CAM visualizations indicate improved localization of disease-relevant regions compared to the baseline. While results are obtained under controlled imaging conditions, the findings demonstrate that redundancy-aware dual-attention enhances feature discrimination efficiency in multi-class agricultural classification tasks. Future work will extend validation to real-field datasets with natural variability.
A set-valued semantic approach for handling missing attribute values in incomplete information systems Asma' Mustafa; Rabiei Mamat; Iwan Tri Riyadi Yanto; Ahmad Shukri Mohd Noor
International Journal of Advances in Intelligent Informatics Vol 12, No 3 (2026): August 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i3.2453

Abstract

In order to handle incomplete information systems, numerous extended rough set theories have been introduced, primarily because the classical version of this theory is limited to complete information systems. Even though these approaches can deal with incompleteness, however they have an issue due to poor performance in terms of uncertainty measures. Thus, this paper aims to handle incomplete information while improving the performance of uncertainty measures based on a new semantic interpretation using set-valued terms. Firstly, the interpretation of missing attribute values is proposed based on the semantics of set-valued information systems. The transformation from an incomplete to a complete one leads to a new information system called the Possible Equivalent Value-Set Information System (PEVS). Based on this system, a rough set model constructed from the similarity relation of a set-valued information system is developed. Similarity precision, which measures the similarity between sets of attributes, is also defined. Secondly, δ-rough set model induced by similarity precision is discussed. Based on this model, δ-accuracy, δ-roughness, and δ-approximation accuracy are constructed to measure the uncertainty in PEVS. Experimental analysis is conducted on real-life datasets to compare the performance of the uncertainty measures between the proposed approach and other rough set approaches. The results show that the proposed approach is feasible in handling incomplete information while providing better uncertainty measures in terms of accuracy, roughness, and approximation accuracy.
Integrating machine learning and sustainable infrastructure: forecasting and improving sanitation coverage in rural Vietnam T. Thai-Phuong; N. Pham-Phuong; N. Luu-Huu; L. Nguyen-Son
International Journal of Advances in Intelligent Informatics Vol 12, No 3 (2026): August 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i3.2403

Abstract

Disparities in sanitation coverage between urban and rural Vietnam remain a major barrier to sustainable development and public health, especially in rural areas with high poverty and limited clean water access. This study integrates machine learning and sustainable infrastructure to forecast sanitation coverage and address urban-rural disparities. Using data from 2018 to 2023, socio-economic factors (poverty rate, per capita income) and technical indicators (clean water access) were incorporated into Artificial Neural Network (ANN) and Support Vector Machine (SVM) models, evaluated via five-fold cross-validation. Results showed strong correlations (clean water-sanitation: r = 0.99; poverty-sanitation: r = -0.97), with ANN significantly outperforming SVM (validation R² = 0.999972, RMSE = 0.018912%, MAE = 0.1474%). Rural sanitation coverage increased slowly from 84.7% to 94.7%, while urban areas approached optimal levels. The findings demonstrate that machine learning effectively captures non-linear patterns in heterogeneous data for accurate forecasting. To tackle persistent rural gaps, urine-diverting sanitation systems are proposed as a sustainable solution that conserves water, reuses urine as nitrogen-rich fertilizer, and supports circular economy principles aligned with SDG 6. This approach provides practical insights for policymakers to bridge urban-rural inequalities, improve public health, and promote resilient infrastructure in developing contexts.
Development of double U-Net model using depthwise separable convolution and convolutional block attention module (CBAM) for polyp medical image segmentation Wiharto Wiharto; Maulana Daffa’ Athaullah Yahya; Umi Salamah
International Journal of Advances in Intelligent Informatics Vol 12, No 3 (2026): August 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i3.1783

Abstract

One of the most common types of cancer is colorectal cancer, which is commonly caused by colon polyps. Early detection via colonoscopy plays a crucial role in preventing the advancement of cancer, even though initial symptoms may not always be noticeable. In this context, computer-assisted methods for image segmentation in colonoscopy significantly aid in polyp detection, reduce the workload on healthcare professionals, and enhance diagnostic precision. A widely used approach in medical image segmentation is the U-Net model, known for its ability to merge information across multiple resolutions through its encoder-decoder structure. Nonetheless, several U-Net-derived architectures struggle to effectively capture deeper features and maintain computational efficiency. This research introduces a modified Double U-Net architecture that integrates MobileNetV2 as its encoder to alleviate computational complexity, while incorporating Depthwise Separable Convolutions (DWSC) and Convolutional Block Attention Modules (CBAM) in the skip connections to enhance both accuracy and efficiency. The proposed model demonstrated notable performance, achieving an F1-score of 95.28% and IoU of 90.84% on the CVC-Clinic DB dataset, and an F1-score of 89.08% with an IoU of 80.69% on the Kvasir-SEG dataset, with a parameter count of 1.96 million. This approach not only increases segmentation precision but also maintains computational efficiency, making it a viable option for detecting colorectal polyps.
Explainable machine learning with class balancing for predicting student academic performance Zamtinah Zamtinah; Rianto Rianto; Paulus Insap Santosa
International Journal of Advances in Intelligent Informatics Vol 12, No 3 (2026): August 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i3.2379

Abstract

Accurate prediction of student academic performance is crucial for implementing effective early intervention strategies in higher education. However, existing models often struggle with class imbalance and lack interpretability, limiting their practical adoption. This methodological proof-of-concept develops a robust predictiveframework by benchmarking multiple machine learning algorithms, specifically Logistic Regression, Decision Tree, and the standard Random Forest, against a proposed Random Forest classifier with class balancing. Utilizing a public simulation dataset of 2,392 student records, the proposed model incorporates algorithmic penalties for misclassifications of the minority class to address data skewness. Experimental results demonstrate that this balanced approach significantly outperforms the linear and standard tree-based baselines, achieving an Accuracy of 92.3% and an F1 Macro score of 0.872. The results confirm the model's superior capability in identifying at-risk students without compromising overall precision. Furthermore, explainability analysis using SHAP (Shapley Additive exPlanations) identifies Grade Point Average (GPA) as the dominant predictor, while highlighting Absences and Weekly Study Time as critical behavioral leading indicators. These findings demonstrate that integrating predictive accuracy with explainable AI supports proactive and evidence-based academic governance. Future research is recommended to validate this framework using longitudinal data streams within Learning Management Systems, enabling real-time monitoring.
A three-dimensional STFT representation for EEG alcoholism classification using 3D convolutional networks Ayu Sekar Safitri; Achmad Rizal; Inung Wijayanto
International Journal of Advances in Intelligent Informatics Vol 12, No 3 (2026): August 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i3.2319

Abstract

Alcohol consumption can alter electrical activity in the brain, and these changes can be captured through electroencephalogram recordings. Automatically recognizing such patterns may help support early identification and reduce the reliance on subjective screening methods. In this work, a three-dimensional Short-Time Fourier Transform (3D STFT) representation is developed to encode temporal, spectral, and spatial characteristics contained in multichannel EEG data. Each electrode receives the STFT in order to produce a unique time-frequency map, which are then combined into a 3D tensor that preserves how the signal evolves across channels. Unlike 2D spectrogram, the 3D representation preserves spatial interactions across electrodes within the time-frequency domain. This multidimensional structure allows the model to interpret the EEG not as separate slices but as a unified volume. The 3D tensor is subsequently used as a 3D CNN's input for classifying EEG trials into alcoholic and non-alcoholic categories. Evaluation is performed using publicly available UCI Alcoholic EEG dataset. The proposed approach yields strong performance, producing accuracy, precision, and recall reaching 98.96%, 98.76%, and 99.17%, respectively, alongside an F1-score of 98.96 and AUC of 99.50%.. These results indicate that combining temporal, spectral, and spatial information within a single representation allows the network to extract deeper and more informative neural patterns compared to conventional 1D features or 2D spectrogram-based inputs. While STFT’s fixed window length limits its ability to represent rapid or highly irregular non-stationary changes, the overall results show that the 3D representation provides a comprehensive view of EEG dynamics and enables effective classification of alcoholic versus non-alcoholic subjects.
An enhanced MobileNetV2-191H architecture for accurate crescent moon detection using semantic segmentation Muhammad Habib Yuhandri; Yuhandri Yuhandri; Sumijan Sumijan
International Journal of Advances in Intelligent Informatics Vol 12, No 3 (2026): August 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i3.2488

Abstract

This study proposes an enhanced lightweight semantic segmentation framework, called MobileNetV2-191H, for accurate crescent moon detection in astronomical observation images. Crescent moon detection is a challenging task because the crescent moon typically appears as a very thin and low-contrast object that is highly affected by atmospheric interference, cloud cover, image noise, and illumination variation near the horizon. The dataset used in this study was obtained from crescent moon observation videos provided by the Indonesian Agency for Meteorology, Climatology, and Geophysics (BMKG), resulting in 6,283 extracted image frames that were divided into training and testing datasets. The proposed MobileNetV2-191H framework was developed by adapting the original MobileNetV2 architecture through the integration of additional segmentation-oriented layers, including transposed convolution, feature refinement, and pixel classification modules, to improve pixel-level segmentation capability for thin crescent moon objects. Experimental evaluation was conducted using semantic segmentation metrics, including precision, recall, F1-score, and Intersection over Union (IoU). The proposed model achieved 84.81% precision, 85.32% recall, 84.07% F1-score, and 72.64% IoU, outperforming several baseline segmentation models such as U-Net, DeepLabV3, SegNet, and EfficientNet. These results demonstrate that the proposed lightweight framework effectively improves crescent moon segmentation performance while maintaining computational efficiency for real-time astronomical observation applications.

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