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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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A comparative analysis of classical and cooperative coevolutionary genetic algorithms for solving nurse scheduling problems Maizatul Farhana Mohamad Nazri; Zeratul Izzah Mohd Yusoh; Halizah Basiron; Azlina Daud
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.v12i3.2409

Abstract

The Nurse Scheduling Problem (NSP) is a complex workforce planning task that involves assigning nurses to shifts while satisfying operational feasibility, legal regulations and preference-based quality requirements. Classical Genetic Algorithms (GA) are widely applied to NSP but rely on monolithic optimisation structures that evaluate all constraints within a single population, which can lead to constraint interference and reduced stability as problem realism increases. This study investigates a Cooperative Co-Evolutionary approach for NSP (Coop-NSP), which decomposes optimisation into two interacting subpopulations corresponding to hard and soft constraints. Both subpopulations employ an identical nurse-by-day chromosome representation and evolve independently under specialised objectives, with cooperation introduced through contextual fitness evaluation. Experiments were conducted on a weekly NSP with a seven-day planning horizon and multiple nurse roles, evaluated for 15,000 generations across 30 independent runs under identical parameter settings. The Classical GA achieved a mean best penalty of 651.30 ± 37.90, with a minimum best penalty of 581.7, while Coop-NSP obtained a higher mean best penalty of 768.72 but achieved a lower minimum best penalty of 507.64. The Classical GA exhibited rapid convergence, whereas Coop-NSP demonstrated stepwise convergence with sustained population diversity. Although Coop-NSP incurred higher computational cost, its structured cooperative optimisation framework provides a stable and extensible foundation for future integrated healthcare scheduling research.
Attention-enhanced U-Net with VGG backbone for robust facial wrinkle segmentation under variable illumination and pose conditions Wahyu Fajar Setiawan; Nanik Suciati
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.v12i3.2394

Abstract

Facial wrinkle segmentation is critical for automated dermatological assessment, yet existing deep learning methods exhibit significant performance degradation under real-world illumination and pose variations, restricting practical clinical deployment where imaging conditions cannot be controlled. This study proposes a novel robustness-oriented segmentation framework that integrates three synergistic components: (1) attention-enhanced U-Net architectures with strategically frozen VGG16/VGG19 backbones enabling hierarchical feature transfer, (2) a dual augmentation strategy coupling geometric transformations for pose invariance with a four-level photometric enhancement pipeline for illumination robustness, and (3) a weighted mask fusion mechanism combining expert annotations with weak supervision labels. Three architectures (baseline Attention U-Net, VGG16, and VGG19 variants) are trained on 1,000 FFHQ-Wrinkle images and systematically evaluated across four augmentation strategies under nine challenging deployment conditions, including low light, high contrast, noise, head tilts, and perspective shifts. The proposed VGG19 Attention U-Net with combined augmentation achieves a Dice coefficient of 0.6533 and IoU of 0.4931, outperforming the best existing method (Striped WriNet) by +4.26% in Dice and +5.89% in IoU under identical re-implemented training conditions. The model retains 97.82% of its original performance across all nine perturbation conditions (robustness score: 0.6391), representing a 10.4% robustness improvement over the non-augmented baseline. These results demonstrate that the synergistic combination of attention mechanisms, transfer learning, and dual augmentation produces clinically viable robustness for facial wrinkle segmentation.
Soft voting technique with explainable artificial intelligence (XAI) for predicting depression, anxiety and stress Yefta Christian; Herman Herman; Muhammad Hafis; Kurnia Cantra
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.v12i3.2385

Abstract

Mental health severity assessment is often hindered by limited access to professional services and the time required for clinical evaluation. This study proposes an interpretable soft voting method to classify the severity levels of depression, anxiety, and stress using DASS-42 questionnaire data. The proposed framework integrates Logistic Regression, Random Forest, Support Vector Machine, and Extreme Gradient Boosting, and is evaluated on 35,445 anonymized responses from a public psychometric dataset. Model performance was assessed using accuracy, precision, recall, F1-score, macro-averaged and weighted F1-score and the precision-recall curve under stratified cross-validation for class imbalance between the normal and mild classes. Explainable Artificial Intelligence using SHAP was employed to interpret model decisions. The soft voting achieved strong predictive performance, with accuracy values of 0.98 for depression, 0.99 for anxiety, and 0.97 for stress, outperforming or matching individual base models. SHAP analysis identified clinically consistent features contributing to model predictions; due to computational constraints, SHAP analysis was not applied to the SVM model. Despite strong performance, the use of secondary self-reported data and class imbalance, particularly the underrepresentation of normal and mild cases, were limitations. The proposed model demonstrates the potential of interpretable soft voting as a decision-support tool for mental health severity stratification in resource-constrained settings.
Optimizing priority scheduling in hadoop for resource utilization using quantum particle swarm optimization technique Bhavana Potli; Shashikumar Dandinashivara Revanna
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.v12i3.2302

Abstract

Efficient resource scheduling in Hadoop remains a challenging problem due to the presence of diverse workloads and varying priority requirements in cluster environments. Traditional YARN schedulers such as FIFO, Fair, and Capacity often struggle to simultaneously balance data locality, responsiveness, fairness, and priority handling, which can lead to increased waiting times and inefficient resource utilization. To address these limitations, this study proposes a Quantum-Inspired Priority Scheduler (QIPS) that integrates a Quantum-behaved Particle Swarm Optimization (QPSO) mechanism within the YARN Resource Manager to enhance job–node assignment decisions. The proposed scheduler considers multiple performance criteria, including latency, resource utilization, data locality, and priority awareness, enabling more adaptive and balanced scheduling under dynamic workload conditions. A hybrid implementation combining Java and Python is developed, where YARN handles job execution while the QPSO module performs optimization. Experimental evaluation on a multi-node Hadoop 3.3.4 cluster using synthetic workloads shows that QIPS effectively reduces deadline penalties and improves data locality, while maintaining competitive performance across other scheduling metrics. These findings indicate that quantum-inspired optimization offers a promising direction for achieving efficient and balanced resource scheduling in distributed systems.
A comprehensive review of CNN and transformer based visual feature extraction for automatic video captioning Hemel Sharker Akash; Joseph Emerson Raja; Md. Jakir Hossein
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.v12i3.2280

Abstract

Automatic Video Captions are increasingly important to use in applications in the accessibility, search, education, and content moderation areas. Generating accurate and context-aware captions is difficult for lengthy and complex videos. Recent surveys focus more on language models than on the vision side. This means that the contribution of visual feature extraction towards the performance of the model is not studied properly. Here we attempt to fill this gap by conducting reviews of video captioning literature from the vision viewpoint, comparing CNNs-based and transformer-based feature extraction, and detecting trends, pros and cons. Approximately 100 papers published between 2017 - 2025 were reviewed and analyzed based on base vision, dataset and evaluation metrics. The review classified the selected papers into CNN-based pipelines and transformer-based techniques. Moreover, it compared their rank measured by application popularity, dataset usage, and performance scores across popular datasets/benchmarks. The review shows that CNNs were dominated before 2021, but transformer-based approaches now lead due to their ability to capture long-range temporal dependencies and multimodal interactions. However, CNNs continue to perform well for short videos. As for Dataset usage, patterns show that ActivityNet Captions and YouCook2 are suited for long-form reasoning because of timely manner captioning, while MSR-VTT and MSVD are effective for short clips (one sentence caption). In terms of Special semantic quality metrics, CIDEr and METEOR are more semantically appropriate compared to n-gram quality metrics like BLEU. This review emphasizes transformer-based architecture, particularly multimodal models, which will be the future of video captioning. However, challenges remain in handling lengthy videos, adapting across domains and languages, and ensuring efficient deployment (small, optimized model). Addressing these gaps will add value by making video captioning systems more reliable, interpretable, and broadly usable in real-world contexts.
CatBoost-based context-aware purchase-decision prediction on tabular customer journey sequences Phung Kim Thai; Ngan Thi Bich Lam; Trinh My Le; Duy Le Trinh
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.v12i3.2412

Abstract

Building on the premise that real-time marketing decisions require models that are both accurate and resource-efficient, our study proposes a context-aware, tabular pipeline using CatBoost with ordered target encoding and greedy feature combinations to learn from customer journey sequences. In feature engineering, we derive period-level aggregates such as touch counts and mean dwell times across daily, weekly or monthly intervals, compute channel-usage entropy to measure diversity, and quantify recency relative to the most recent interaction, capturing intensity and diversity of user behavior. These sequences are further enriched with context signals such as dwell time, device type, temporal gaps and channel entropy, along with demographic attributes, to provide a rich representation of customer behavior. We apply the method to the Netherlands Travel dataset (May 2015–Oct 2016), trimming each journey to the first ten and last twenty touchpoints, engineering row-level and period-level aggregates, and collapsing them to the purchase level. CatBoost, a gradient boosting algorithm, leverages ordered target encoding and greedy feature combinations to capture nonlinear interactions while remaining interpretable and CPU-friendly. Stratified five-fold cross-validation yields an AUC of 0.9479, and tuning the F0.5 = 0.869 score produces a threshold of 0.723 with precision 0.897, recall 0.773 and accuracy 0.895. These results demonstrate that structured tabular representations of customer journeys, combined with tree-based learning, can achieve strong predictive performance while maintaining interpretability and computational efficiency, offering a practical modeling framework for decision-support systems in travel marketing.
Feature-enhanced residual signals with parallel hybrid network and novel loss for noninvasive blood glucose detection Mochammad Fatchur Rahman; Karlisa Priandana; Husin Alatas; Renan Prasta Jenie
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.v12i3.1970

Abstract

Diabetes remains a major global health challenge. According to the International Diabetes Federation (IDF), approximately 10.5% of the world’s population is affected, with nearly half unaware of their condition. Therefore, accurate and accessible blood glucose level (BGL) monitoring remains critical. This paper presents a noninvasive BGL estimation approach using in vivo residual infrared (IR) signals collected from patients’ fingertips, which is inherently challenging due to weak glucose-related signatures and interference from other physiological factors. To address this, we propose a tailored feature engineering strategy to uncover hidden relationships between residual signals and BGL. The proposed method improves BGL range discrimination, as indicated by increased Spearman correlations and consistent gains in Kendall and Pearson correlations, demonstrating its effectiveness. Furthermore, we propose a task-specific hybrid neural network architecture, consisting of parallel 1D convolutional and dense blocks to capture both local spectral–temporal patterns and global nonlinear relationships. A distribution-aware loss function is also introduced to reduce prediction bias toward the dataset mean, improving sensitivity across the full BGL range. For validation, the proposed model is compared with random forest (RF) and boosting-based methods, showing superior performance with a 14.11% mean absolute percentage error (MAPE). Clinical reliability is evaluated using Clarke error grid (CEG) analysis, where 84.7% of predictions fall within the accurate zone and 15.3% within the clinically acceptable zone. These results demonstrate the potential of the proposed approach for practical, low-cost, and portable noninvasive BGL monitoring.
A two-factor linguistic time series model with time-dependent semantic relationship groups Phong Dinh Pham; Linh Van Mai
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.v12i3.2423

Abstract

Fuzzy time series-based forecasting models have been extensively examined because of their flexibility and ability to handle uncertainty. In those forecasting models, the universe of discourse is apportioned into subintervals and human experts intuitively assign fuzzy sets to them. Because fuzzy sets are always associated with linguistic words, the concept of linguistic time series (LTS) is introduced, which a numeric time series is transformed into a linguistic one by a mathematical formalism. Then, the obtained LTS specifies semantic logical relationships that are utilized to generate semantic logical relationship groups (SLRG) for establishing forecasting models. In this paper, the concept of time-dependent multi-factor SLRG is proposed and it is used to establish time-dependent multi-factor first-order and high-order LTS models. The experimental studies executed on the time series datasets of the TAIFEX index of Taiwan and daily average temperature in 1996 in Taipei, Taiwan, show that our proposed forecasting model outperforms the benchmarked models.
Adaptive hybrid ensemble-based DDoS detection using reinforcement learning-guided optimization and deep learning Maha Ismail Raheem; Shouket Abdulrahman Ahmed; Enas Faek Aziz; Saad Ali Assi; Sinan Qahtan Salih; Ahmed Dheyaa Radhi; Hilal Adnan Fadhil; Taha Almulaisi
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.v12i3.2109

Abstract

Distributed Denial-of-Service (DDoS) attacks remain among the most disruptive network threats, and detectors that generalize across attack families with low false-alarm rates are still an open problem. Propose an adaptive hybrid ensemble that unifies two gradient-boosting learners (Random Forest and Gradient Boosting) with three deep neural base learners (DNN, CNN-1D, and LSTM) under a weighted soft-voting rule whose weights are produced by a Reinforcement Learning (RL) policy. The RL agent treats the ensemble-weight simplex as its action space, observes a state vector built from validation-set diagnostic statistics, and is trained by REINFORCE-with-baseline to maximize a reward equal to validation F1 minus a small calibration penalty. The framework is formalized as a Markov decision process with one stochastic step per training episode, which decouples ensemble-weight learning from the (non-differentiable) outer F1 objective. On a 10,000-sample, 25-feature, five-class benchmark with 7% label noise, the proposed system reaches weighted F1 = 0.846, accuracy = 84.7%, MCC = 0.781, AUC = 0.952, and ECE = 0.039. Friedman and Nemenyi post-hoc tests over 50 CV folds confirm the RL-guided ensemble is significantly better than every individual base learner and uniform voting at α = 0.05 (Cohen's d = 0.96). An ablation isolates the RL policy and gradient boosting as the main drivers; a label-noise robustness study shows graceful degradation up to 20%; a head-to-head comparison against the Bonobo Optimizer (BO), GA, PSO, GWO, and WOA shows the best F1/wallclock trade-off.
Validation of a large language models-based socio-political sentiment analysis system with a retrieval-augmented generation approach Sidharta Sidharta; Hady Pranoto; Frederik Gasa; Nur Kholis; Ardvin Kester Ong
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.v12i3.2389

Abstract

Socio-political discourse on digital platforms generates large volumes of unstructured textual data, posing challenges for reliable sentiment analysis and contextual interpretation. Conventional sentiment analysis approaches often lack contextual grounding, while direct use of Large Language Models (LLMs) may introduce hallucinations, bias, and excessive generalization. To address these issues, this study proposes an LLM-based sentiment analysis system that integrates multi-source data acquisition, zero-shot sentiment classification, Retrieval-Augmented Generation (RAG), and generative reasoning for contextual interpretation. Unlike prior studies that mainly focus on improving model accuracy, this research emphasizes system-level validation of the entire analytical pipeline. The evaluation includes sentiment calibration using a Golden Dataset, data ingestion performance analysis, retrieval quality assessment based on semantic distance, and generative evaluation using an LLM-as-a-Judge framework. Experimental results indicate that the system provides stable data acquisition and produces coherent analytical reports. However, retrieval noise and neutrality bias introduced by LLM alignment mechanisms still affect the groundedness of generated outputs. These findings highlight the importance of retrieval quality and demonstrate the potential of LLMs as reasoning and evaluation components for analyzing complex socio-political discourse.

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