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INDONESIA
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 353 Documents
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Automatic coloring of Ulos motif using large language model and non-dominated sorting differential evolution algorithm Arlinta Barus; Humasak Simanjuntak; Samuel Indra Gunawan Situmeang; Robert Aritonang; Nicholas Hutabarat; Grase Panjaitan
International Journal of Advances in Intelligent Informatics Vol 12, No 2 (2026): May 2026
Publisher : Universitas Ahmad Dahlan

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Abstract

Ulos is a traditional Batak textile with high cultural significance, yet artisans lack tools to modernize designs while preserving heritage. Previous research primarily uses single-objective methods that fail to accommodate user preferences. This study introduces the first multi-objective framework for traditional textile coloring, differentiating itself by using a Large Language Model (LLM) as a natural language interface for non-technical weavers. The LLM translates user design preferences into dynamic objective functions to guide a Non-dominated Sorting Differential Evolution (NSDE) algorithm. Performance is assessed using the epsilon (ϵ) indicator, aesthetic metrics (contrast and colorfulness), and user preference scores. The system achieved high convergence with an ϵ-indicator value of 2.1788×10^(-3) and user preference scores reaching 0.66. Additionally, a paired t-test (p=0.07) demonstrated the algorithm's robustness across parameter variations. This approach enables artisans to create culturally authentic, aesthetically optimized designs through intuitive interaction.
HAD-CDA: a hybrid approach for anomaly detection in streaming data with concept drift adaptation Sree Ram Murthy; Venkata Narayana
International Journal of Advances in Intelligent Informatics Vol 12, No 2 (2026): May 2026
Publisher : Universitas Ahmad Dahlan

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Abstract

The streaming anomaly detection is a difficult task since the data distribution is changing, and concept drift has a negative effect on the performance of the traditional detection methods. To solve the issue, this article suggests the HAD-CDA (Hybrid Anomaly Detection with Concept Drift Adaptation) which is a combined system that can detect local anomalies in the time series with the LSTM Autoencoders (LSTM-AE) and global anomalies in the distribution with Quant Tree-EWMA (QT-EWMA). The proposed framework accomplishes three objectives: (i) the use of the dynamic weighting mechanism, which automatically changes the contribution of each component (lambda between 0.2 and 0.8) according to their effectiveness, (ii) two concept drift detectors are proposed, i.e. the KolmogorovSmirnoff and PageHinkley tests to allow detecting concept drift, and (iii) the use of Elastic Weight Consolidation (EWC) to reduce catastrophic forgetting during update of the model. Experiments on four real world streaming datasets HTTP, SMTP, ForestCover and Shuttle indicate that HAD-CDA has AUCs of 0.95-0.97, a 8-9% improvement in state of the art methods and F1-scores of 0.81-0.95. The recall measure obtained by the LSTM-AE element is 0.8094 as compared to 0.9399, and the specificity of QT-EWMA is very high, 0.9399. The framework is highly adaptable to different types of drifts, regaining around 9092 performance levels before drift, and 1525 windows would be needed by a baseline method, with low processing latency of 12.430.9 ms per window. Having a per-sequence complexity of O(1), memory cost of O(n), low DIS (approximately 0.08), and stability indices of 0.02-0.04, the suggested HAD-CDA framework is an accurate, efficient, and robust solution to real-world streaming anomaly detection in changing data.
Enhancing multi-document summarization through topic–pattern-based sentence selection Shaufiah Shaufiah; Yuefeng Li; Richi Nayak; Yutong Wu
International Journal of Advances in Intelligent Informatics Vol 12, No 2 (2026): May 2026
Publisher : Universitas Ahmad Dahlan

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Abstract

The growing digital text content requires automated summarization systems as fundamental tools to enable users to access information at high speed. The automatic Multi-Document Summarization (MDS) requires systems to produce a summary which combines essential information from multiple documents. The extractive methods which use lexical signals and sentence-based rules only produce repetitive results because they cannot identify complex thematic relationships. This study developed an improved extractive MDS model which combines topic modeling with pattern-based semantic indicators and a method to choose diverse sentences. The model employs LDA to identify concealed thematic structures and retrieves typical word patterns which improve topic models and chooses topics through a greedy algorithm that reduces redundancy to achieve suitable salience and coverage. The proposed system achieves better results than classical baselines in experiments performed on DUC 2006 and DUC 2007 datasets by outperforming Lead and CLASSY04 and KL-SUM and LexRank and TextRank and PETMSUM. The system demonstrates superior performance to all baseline methods by achieving better results in ROUGE-1 and ROUGE-2 and ROUGE-SU4 evaluation metrics. The results show that extractive summarization tasks reach their best results when topic–pattern representations work together with diversity-aware scoring methods.
MobileNet-driven detection of bacterial and viral pneumonia with Grad-CAM heatmap insights Yuri Pamungkas; Adrian Jaleco Forca; Muhammad Nur Afnan Uda; Uda Hashim
International Journal of Advances in Intelligent Informatics Vol 12, No 2 (2026): May 2026
Publisher : Universitas Ahmad Dahlan

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Abstract

Pneumonia is a major cause of childhood illness and death, and chest X-rays remain the most accessible diagnostic tool. Differentiating bacterial from viral pneumonia, however, is difficult because of overlapping radiographic patterns. This study explores MobileNet architectures combined with Grad-CAM visualization to provide efficient and interpretable pneumonia classification. The main contribution of this research is to demonstrate that MobileNet combined with Grad-CAM not only produces accurate predictions but also highlights radiologically meaningful regions of the lungs, thereby improving transparency and trust in automated diagnosis. A dataset of 5,842 pediatric chest X-rays from Guangzhou Women and Children’s Medical Center was used, including bacterial, viral, and normal cases. MobileNet and MobileNetV2 were trained with stochastic gradient descent, categorical cross-entropy, 20 epochs, and batch size of 32, and validated through 10-fold cross-validation. Grad-CAM was applied to generate heatmaps for model interpretability. Results indicated that MobileNet outperformed MobileNetV2. MobileNet achieved 79.32% accuracy, 81.02% precision, 78.15% recall, 77.82% F1-score, and 89.49% specificity. Its AUC-ROC reached 94.64% (macro) and 90.52% (micro). MobileNetV2 obtained 76.44% accuracy, 74.45% F1-score, and 93.61% macro AUC-ROC. Grad-CAM confirmed that both models attended to pneumonia-related lung regions, with MobileNet producing sharper localized activations and MobileNetV2 showing broader patterns. In conclusion, MobileNet with Grad-CAM provides an accurate, efficient, and interpretable framework for pneumonia detection, making it suitable for deployment in resource-limited clinical settings.
IndoBERTSkill: pretrained domain-specific language model for recognition Indonesian skill Meilany Nonsi Tentua; Suprapto Suprapto; Afiahayati Afiahayati
International Journal of Advances in Intelligent Informatics Vol 12, No 2 (2026): May 2026
Publisher : Universitas Ahmad Dahlan

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Abstract

The pretrained language model in Indonesian is already available for natural language processing tasks. However, this pre-trained model has been trained on Indonesian text, which has a different structure from the job description. Due to this, the pre-trained language model effectiveness for skill recognition purposes. IndoBERTSkill is a novel pre trained domain-specific language model that recognizes Indonesian language skills. It is built on the Bidirectional Encoder Representations from Transformers (BERT) architecture. IndoBERTSkill was trained on an extensive collection of Indonesian language texts from the Indonesian Wikipedia, the English Wikipedia, and the Indonesian Job Description from the job portal. IndoBERTSkill's performance was evaluated through two main approaches: (1) language modeling via Masked Language Model (MLM) prediction, and (2) fine-tuning on a custom annotated dataset (NERSkill) for Named Entity Recognition (NER) tasks. The fine-tuning process involved training a classification layer on top of the IndoBERTSkill model using BIO tagging to identify hard skills, soft skills, and technology entities. Similarly, the skill recognition model derived from IndoBERTSkill exhibits the highest F1-Score among various pre-trained language models, precisely at 87%, thus demonstrating robustness and strong generalizability for skill entity recognition in Indonesian job descriptions. IndoBERTSkill provides valuable resources for developing Indonesian natural language processing applications that require skills introduction. This could increase the accuracy and efficiency of skills recognition across various domains, including job matching, education, and training.
Optimizing the compact convolution transformer for enhanced pneumonia detection Muhammad Munsarif; Norshuhani Zamin; Muhammad Sam’an
International Journal of Advances in Intelligent Informatics Vol 12, No 2 (2026): May 2026
Publisher : Universitas Ahmad Dahlan

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Abstract

Pneumonia detection through medical imaging, especially using CT scans or X-rays, presents notable challenges due to the subtle and often unclear signs of the disease. This paper introduces a novel neural network model, the Compact Convolutional Transformer (CCT), designed to address these challenges by optimizing detection accuracy. The CCT model incorporates configuration dropout in its convolutional layers to enhance both robustness and precision.Experiments conducted on a dataset of 5,856 chest X-ray images from pediatric patients aged one to five years demonstrated the model's effectiveness, achieving a remarkable 97% accuracy, 97% recall, 98% precision, and an F1-score of 98%. When compared to state-of-the-art models like DarkNet-53 and VGG-19 + GradCAM, which achieved F1-scores of 97.3% and 95.61% respectively, the CCT model consistently matched or outperformed them, particularly when dealing with smaller and more complex datasets. Even models such as CNN + Bayesian Network, which used larger datasets, only reached an F1-score of 96.3%.These results underscore the superior efficiency and accuracy of the CCT model, highlighting its potential for broader applications in medical diagnostics and image analysis, especially in pneumonia detection.
Distance-based wrapper model for human activity recognition Rana Alauldeen Abdalrahman; Laith AL-Frady; Ruaa Ali Khamees
International Journal of Advances in Intelligent Informatics Vol 12, No 2 (2026): May 2026
Publisher : Universitas Ahmad Dahlan

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Abstract

In this paper, we developed an effective wrapper-based model to optimize the recognition of physical human daily life activities recorded by smartphone built-in sensors. The proposed model employs Sequential Forward Selection (SFS) method in combination with K-Nearest Neighbor (KNN) classifier based on a group of five commonly used distance measures: (Manhattan, Euclidean, Chebyshev, Canberra, and Correlation) each having a specific geometric neighborhood interpretation. The proposed SFS-KNN multi-distance approach enables each distance metric to guide the feature selection process. It examines how the resulting feature subsets, determined by each distance, impact the overall recognition performance. The goal is to identify the best feature subset that achieves the highest accuracy with the lowest dimensionality. . Our proposed distance-based wrapper model was validated on two publicly available WISDM and UCI-HAR datasets under 10-fold cross-validation. The experimental results obtained on both datasets showed that the performance of the proposed model is significantly affected by the distance measure used due to generating different feature subsets for each distance. For the WISDM dataset, our model achieves an overall accuracy of 93.39% based on Euclidean distance, with a reduction ratio of 71.42%. It also offers a substantial reduction of 95.9% in the feature dimensions of the UCI-HAR dataset using Correlation distance, with a recognition rate of 99.33%. These outcomes confirm the superiority of our wrapper model over other feature selection-based approaches proposed for the same datasets.
Machine learning model for classifying the severity level of cyber security attacks Imam Riadi; Sri Winiarti; Herman Yuliansyah; Muhammad ‘Arif Bin Mohamad
International Journal of Advances in Intelligent Informatics Vol 12, No 2 (2026): May 2026
Publisher : Universitas Ahmad Dahlan

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Abstract

Cyberattacks are becoming increasingly sophisticated, necessitating defense mechanisms that go beyond simple detection to include severity assessment for prioritizing mitigation. This study proposes a comprehensive machine learning framework to classify cyberattack severity levels (Low, Medium, High) using a modern, high-dimensional dataset. Addressing the critical challenge of class imbalance, the research integrates the Synthetic Minority Oversampling Technique (SMOTE) with a rigorous feature selection process involving SelectKBest. Four algorithms Naive Bayes, K-Nearest Neighbor (KNN), Random Forest (RF), and Support Vector Machine (SVM) were evaluated using 10-fold cross-validation. The results demonstrate that the SVM model with an RBF kernel achieves superior performance with an accuracy of 97.30% and a False Negative Rate (FNR) of only 3.1% for high-severity threats. This research contributes a robust, data-driven approach to severity classification that effectively handles feature non-linearity and class imbalance, offering actionable insights for real-time security operations.
Enhancing minority class recognition in cattle monitoring: A robustness analysis of lightweight decision-level fusion Putri Nayla Sabri; Nisrina Nurhafizhah; Amrul Faruq; Achmad Fauzan Hery Soegiharto; Muhammad Ilham Perdana
International Journal of Advances in Intelligent Informatics Vol 12, No 2 (2026): May 2026
Publisher : Universitas Ahmad Dahlan

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Abstract

Accelerometer-based monitoring has become an important approach in precision livestock farming, but achieving both high sensitivity for rare, health-relevant behaviors and computational efficiency remains challenging. Deep learning methods can address class imbalance but are often unsuitable for edge deployment due to their computational cost. This study evaluates the robustness of a lightweight decision-level fusion framework for imbalanced cattle behavior classification using tri-axial accelerometer data. To ensure rigorous evaluation, the Synthetic Minority Over-sampling Technique (SMOTE) was applied only in the training feature space to prevent data leakage. Because the dataset is strongly imbalanced, with Salt Licking (SLT) as the minority yet health-relevant class, model performance was assessed using Macro-F1 for global robustness and RecallSLT_{SLT}SLT for rare-event sensitivity. Individual tree-based models achieved strong results, with the best single model, XGBoost, obtaining 93.70% accuracy and 0.9421 Macro-F1. The proposed soft-voting fusion of Extra Trees, XGBoost, and CatBoost further improved performance to 94.21% accuracy and 0.9447 Macro-F1, with a statistically significant gain over the best single model (Wilcoxon signed-rank test, (p=0.0326). The framework also maintained strong minority-class recognition, with SLT achieving precision = 0.9571, recall = 0.9853, and PR-AUC = 0.9990. These results show that lightweight decision-level fusion can improve robustness and rare-event sensitivity without temporal deep learning, making it suitable for resource-constrained edge monitoring in livestock systems.
Improvement of CNN model using integration of multimodal MRI sequences and multilevel fusion to enhance performance for brain tumor classification Ade Umar Ramadhan; Shofwatul Uyun
International Journal of Advances in Intelligent Informatics Vol 12, No 2 (2026): May 2026
Publisher : Universitas Ahmad Dahlan

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Abstract

The prevalence of brain tumors has been increasing annually, and headaches, a common initial symptom, represent the most common manifestation. However, there is a paucity of research on effective methods of assessing brain tumors. This study proposes a novel approach by introducing various modality fusion techniques based on their fusion levels, which are then categorized into four groups: single-modal, data-level fusion, feature-level fusion, and multilevel fusion. A total of 51 combinations are designed to evaluate the efficacy of these fusion techniques and modality configurations. The experiments used a BraTS2021, which comprises four magnetic resonance imaging (MRI) sequences (flair, t1, t1ce and t2). Initially, the image was pre-processed, encompassing data selection, conversion, and normalization. Subsequently, it was input into a 13-layer CNN architecture for feature extraction. Classification was facilitated by a soft voting method in ensemble learning, incorporating support vector machine (SVM), k-nearest neighbor (KNN), logistic regression, random forest, and decision tree algorithms. The predictive efficacy of the model was rigorously assessed through a comprehensive suite of metrics, prominently featuring accuracy, AUCROC, AUCPR, Cohen's Kappa, and MCC. The results indicate that multilevel fusion exhibits optimal performance, with an average accuracy of 95.84%, followed by feature-level fusion and data-level fusion, at 95.12% and 94.77%, respectively. The optimal fusion technique was identified as the combination with the FF configuration (1,2),3,4), producing an accuracy of 96.62%. The best-model combination proposed exhibited an accuracy difference of nearly 6% from the baseline model, underscoring the efficacy of the proposed approach. These empirical results establish a robust baseline for future investigations into sophisticated fusion architectures across hierarchical integration levels.

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