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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.
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Articles 12 Documents
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Search results for , issue "Vol 11, No 3 (2025): August 2025" : 12 Documents clear
Optimized image-based grouping of e-commerce products using deep hierarchical clustering Pranoto, Yuliana Melita; Handayani, Anik Nur; Herwanto, Heru Wahyu; Kristian, Yosi
International Journal of Advances in Intelligent Informatics Vol 11, No 3 (2025): August 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i3.1979

Abstract

Managing large and constantly evolving product catalogs is a significant challenge for e-commerce platforms, especially when visually similar products cannot be reliably distinguished using text-based methods. This study proposes a product grouping method that combines a fine-tuned EfficientNetV2M model with an adaptive Agglomerative Clustering strategy. Unlike conventional CNN-based approaches, which have limited scalability and a fixed number of clusters, the proposed method dynamically adjusts similarity thresholds and automatically forms clusters for unseen product variations. By linking deep visual feature extraction with adaptive clustering, the method enhances flexibility in handling product diversity. Experiments on the Shopee product image dataset show that it achieves a high Normalized Mutual Information (NMI) score of 0.924, outperforming standard baselines. These results demonstrate the method’s effectiveness in automating catalog organization and offer a scalable solution for inventory management and personalized recommendations in e-commerce platforms.
Lightweight deep learning model with ResNet14 and spatial attention for anterior cruciate ligament diagnosis Herman, Herman; Kumar, Yogan Jaya; Wee, Sek Yong; Perhakaran, Vinod Kumar
International Journal of Advances in Intelligent Informatics Vol 11, No 3 (2025): August 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i3.2055

Abstract

The accuracy of diagnosing an Anterior Cruciate Ligament (ACL) tear depends on the radiologist’s or surgeon’s expertise, experience, and skills. In this study, we contribute to the development of an automated diagnostic model for anterior cruciate ligament (ACL) tears using a lightweight deep learning model, specifically ResNet-14, combined with a Spatial Attention mechanism to enhance diagnostic performance while conserving computational resources. The model processes knee MRI scans using a ResNet architecture, comprising a series of residual blocks and a spatial attention mechanism, to focus on the essential features in the imaging data. The methodology, which includes the training and evaluation process, was conducted using the Stanford dataset, comprising 1,370 knee MRI scans. Data augmentation techniques were also implemented to mitigate biases. The model’s assessment uses performance metrics, ROC-AUC, sensitivity, and specificity. The results show that the proposed model achieved an ROC-AUC score of 0.8696, a sensitivity of 79.81%, and a specificity of 79.82%. At 6.67 MB in size, with 1,684,517 parameters, the model is significantly more compact than existing models, such as MRNet. The findings demonstrate that embedding spatial attention into a lightweight deep learning framework augments the diagnostic accuracy for ACL tears while maintaining computational efficiency. Therefore, lightweight models have the potential to enhance diagnostic capability in medical imaging, allowing them to be deployed in resource-constrained clinical settings.
Biological constraint in digital data encoding: A DNA based approach for image representation Muttaqin, Muhammad Rafi; Herdiyeni, Yeni; Buono, Agus; Priandana, Karlisa; Siregar, Iskandar Zulkarnaen; Kusuma, Wisnu Ananta
International Journal of Advances in Intelligent Informatics Vol 11, No 3 (2025): August 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i3.1747

Abstract

Digital data encoding is crucial for communication and data storage, but conventional techniques, such as ASCII and binary coding, have drawbacks in terms of processing speed and storage capacity. A potential substitute with parallel processing and high-capacity storage is DNA-based data encoding. The goal of this research is to develop a digital data encoding technique based on DNA, while considering biological constraints such as homopolymer and GC-content. The process involves converting image pixel values into binary format, followed by encoding into DNA sequences, ensuring they meet biological constraints. The validity of the resulting DNA sequences is assessed through transcription and translation processes. Additionally, Multiple Sequence Alignment analysis is conducted to compare the similarities between the encoded DNA sequences. The results indicate that the DNA sequences from MNIST images share similar characteristics, reflected in the phylogenetic tree's close clustering. Multiple Sequence Alignment analysis shows that biological constraints successfully preserved the core visual features, allowing accurate clustering. However, this method also faces drawbacks, particularly in the reduction of visual information and sensitivity to changes in image intensity. Despite these challenges, DNA-based encoding shows potential for digital image representation. Further development, particularly the integration of deep learning, could lead to more efficient, secure, and sustainable data storage systems, especially for image data.
Geometry-aware light field angular super-resolution using multiple representations Mian, Tariq Saeed
International Journal of Advances in Intelligent Informatics Vol 11, No 3 (2025): August 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i3.1667

Abstract

Light Field Angular Super-Resolution (LFASR) is a critical task that enables applications such as depth estimation, refocusing, and 3D scene reconstruction. Acquiring LFASR from Plenoptic cameras has an inherent trade-off between the angular and spatial resolution due to sensor limitations. To address this challenge, many learning-based LFASR methods have been proposed; however, the reconstruction problem of LF with a wide baseline remains a significant challenge. In this study, we proposed an end-to-end learning-based geometry-aware network using multiple representations. A multi-scale residual network with varying receptive fields is employed to effectively extract spatial and angular features, enabling angular resolution enhancement without compromising spatial fidelity. Extensive experiments demonstrate that the proposed method effectively recovers fine details with high angular resolution while preserving the intricate parallax structure of the light field. Quantitative and qualitative evaluations on both synthetic and real-world datasets further confirm that the proposed approach outperforms existing state-of-the-art methods. This research improves the angular resolution of the light field without reducing spatial sharpness, supporting applications such as depth estimation and 3D reconstruction. The method is able to preserve parallax details and structure with better results than current methods.
Optimizing LPG distribution: A hybrid particle swarm optimization and genetic algorithm for efficient vehicle routing and cost minimization Indrianti, Nur; Leuveano, Raden Achmad Chairdino; Abdul-Rashid, Salwa Hanim; Kuncoro, Andreas Mahendro; Liestyana, Yuli
International Journal of Advances in Intelligent Informatics Vol 11, No 3 (2025): August 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i3.1837

Abstract

This paper aims to develop an optimized solution for the Vehicle Routing Problem (VRP), tailored explicitly for Liquid Petroleum Gas (LPG) distribution, with a focus on minimizing transportation costs and enhancing delivery reliability. The critical role of LPG as an essential public infrastructure commodity, widely utilized for cooking and heating, makes its efficient and reliable distribution a significant logistical challenge due to the strict adherence to delivery time windows, heterogeneous fleets, multi-trip scenarios, and intricate loading and unloading requirements. To address these complexities, this study proposes a novel hybrid Particle Swarm Optimization and Genetic Algorithm (HPSOGA) that uniquely integrates multi-trip routing, time windows, and heterogeneous vehicle fleet management into a single optimization framework. The dual-phase optimization strategy leverages the exploratory capability of PSO and the solution-refining power of GA, resulting in high-quality, feasible solutions. Validation against real-world data involving VRP instances with 88 and 40 stations demonstrates the model’s practical impact, achieving reductions of up to 4.56% in transportation costs compared to existing operational routes. This research makes a significant contribution to interdisciplinary domains, including logistics optimization, sustainability, and energy distribution, by offering a robust and scalable model that comprehensively addresses complex, real-world VRP constraints.
GAN-Enhanced multimodal fusion and ensemble learning for imbalanced chest X-Ray classification Snani, Aissa; Khadir, Mohammed Tarek; Pranolo, Andri; Abdalla, Modawy Adam Ali
International Journal of Advances in Intelligent Informatics Vol 11, No 3 (2025): August 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i3.2092

Abstract

Chest X-ray (CXR) classification tasks often suffer from severe class imbalance, resulting in biased predictions and suboptimal diagnostic performance. To address this challenge, we propose an integrated framework that combines high-fidelity data augmentation using Generative Adversarial Networks (GANs), ensemble learning via hard and soft voting, and multimodal feature fusion. The method begins by partitioning the majority class into multiple subsets, which are individually balanced through GAN-generated synthetic images. Deep learning models, specifically DenseNet201 and EfficientNetV2B3, are trained separately on each balanced subset. These models are then combined using ensemble voting to improve robustness. Additionally, features extracted from the most performant models are fused and used to train traditional classifiers such as Logistic Regression, Multilayer Perceptron, CatBoost, and XGBoost. Evaluations on a publicly available CXR dataset demonstrate consistent improvements across key metrics, including accuracy, precision, recall, F1-score, AUROC, AUPRC, MCC, and G-mean. This framework shows superior performance in multiclass scenarios.
Optimization hybrid weighted switching filtering (OHWSF) using SVD and SVD++ for addressing data sparsity Muhammad, Malim; Gunardi, Gunardi; Danardono, Danardono; Rosadi, Dedi
International Journal of Advances in Intelligent Informatics Vol 11, No 3 (2025): August 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i3.1796

Abstract

Recommender systems are crucial for filtering vast amounts of digital content and providing personalized recommendations; however, their effectiveness is often hindered by data sparsity, where limited user-item interactions lead to reduced prediction accuracy. This study introduces a novel hybrid model, Optimization Hybrid Weighted Switching Filtering (OHWSF), to overcome this challenge by integrating two complementary strategies: Hybrid Weighted Filtering (HWF), which linearly combines predictions from SVD and SVD++ using a weighting parameter (α), and Hybrid Switching Filtering (HSF), which dynamically selects predictions based on a threshold rating (θ). The OHWSF framework introduces a tunable optimization mechanism governed by the parameter σ₁ to adaptively balance weighting and switching decisions based on actual rating deviations. Unlike existing static or manually tuned hybrid methods, the proposed model combines dynamic switching with weight optimization to minimize prediction error effectively. Extensive experiments on four benchmark datasets (ML-100K, ML-1M, Amazon Cell Phones Reviews, and GoodBooks-10K) demonstrate that OHWSF consistently outperforms traditional collaborative filtering (UBCF, IBCF), matrix factorization techniques (SVD, SVD++), and standalone hybrid models across all evaluation metrics (MAE, MSE, RMSE). The model achieves optimal performance within the range of α = 0.6–0.9 and θ = 1.0–1.5, demonstrating robustness across varying sparsity levels. Notably, OHWSF achieves up to 742.16% MAE improvement over the UBCF model, with significantly reduced training time compared to SVD++. These findings confirm that OHWSF significantly improves prediction accuracy, scalability, and adaptability in sparse data environments. This research contributes a flexible, interpretable, and efficient hybrid recommendation framework suitable for real-world applications.
Edge optimized multimodal cross fusion model with statistical validation for multi crop disease detection Njoroge, Thomas Kinyanjui Njoroge; Mugoye, Kelvin Sindu; Kibuku, Rachael
International Journal of Advances in Intelligent Informatics Vol 11, No 3 (2025): August 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i3.2051

Abstract

Accurate and timely crop disease detection is crucial for mitigating agricultural losses and ensuring food security, particularly in resource-limited settings. Traditional diagnostic methods are inefficient and prone to errors, while existing deep learning models, such as ResNet50 and Inception V3, struggle with generalizability and computational efficiency. This study proposes a Dynamic Edge-Optimized Multimodal Fusion (DEMF) model, integrating EfficientNetV2 and MobileNetV2 to enhance feature learning and scalability. The model was trained on a 76-class dataset comprising PlantVillage and locally collected images of crop diseases, ensuring robustness across diverse conditions. Feature fusion via concatenation, combined with compound scaling and transfer learning, enabled the model to capture fine-grained patterns of disease. Extensive experiments, including ablation studies and comparative evaluations against DenseNet-121, DenseNet-50, AlexNet, and ResNet-152, validated the model’s superiority. The proposed model achieved 99.2% accuracy, a Kappa of 0.9919, and an AUC of 0.9999, outperforming benchmarks. Statistical validation confirmed significant improvements (p<0.05) and stability. To enhance accessibility, an AI-powered mobile application was deployed on the Google Play Store, enabling real-time disease detection and actionable recommendations. To enhance accessibility, an AI-powered mobile application was deployed on the Google Play Store, enabling real-time disease detection and actionable recommendations. This research advances transfer learning, feature fusion, and statistical validation for robust, scalable crop disease detection in low-resource environments.
A two-layered collaborative approach for network intrusion detection system using blended shallow learning gaussian naïve bayes and support vector machine models Pardeshi, Nilesh Ghanshyam; Patil, Dipak Vitthalrao
International Journal of Advances in Intelligent Informatics Vol 11, No 3 (2025): August 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i3.2035

Abstract

The majority of network intrusion detection systems use a signature matching technique. To detect abnormalities and unfamiliar attacks using machine learning methods is a more reliable approach. However, due to significant variations in attack trends, applying a single classifier is impractical for the effective detection of all types and forms of attacks, particularly rare attacks such as User2Root (U2R) and Remote2Local (R2L). Consequently, a hybrid strategy is expected to provide more promising performance. The proposed Two-Layered Collaborative Approach (TLCA) particularly addresses the problem as mentioned earlier. Principal Component Analysis optimizes variables to handle the variation resulting from every kind of attack. The proposed method investigates several types of attacks and discovered that the behaviors of U2R and R2L attacks are similar to those of regular users’ characteristics. To identify DoS and Probe attacks, TLCA uses a Shallow Learning classifier, such as Gaussian Naïve Bayes, as Layer 1. Similarly, the Support Vector Machine at Layer 2 discriminates between U2R and R2L and typical occurrences. We have divided KDDTrain+ into Set 1 and Set 2 by observing that it involves two 2-dimensional PCA analyses. Cross-sectional Correlated Feature Selection (CCFS) is employed to choose key attributes. PCA and KPCA are applied to datasets to reduce dimensionality. The results obtained using the proposed method on the NSL-KDD dataset are compared with those of available benchmark methods. According to the experimental data, TLCA outperforms all single machine learning classifiers and surpasses many current cutting-edge IDS approaches. The proposed method achieves detection rates of 92.4%, 92.3%, 95.6%, and 100% for DoS, Probe, R2L, and U2R, respectively. The proposed TLCA also demonstrates a better ability to identify unusual attacks. It also yields improved detection rate results for known attacks, at 94.1%, and for unknown attacks, at 91.1%, when using the KDDTest+ dataset for testing.
Machine learning-based B2C software project success prediction model in Indonesia Setiawan, Rudi; Rahman, Titik Khawa Abdul
International Journal of Advances in Intelligent Informatics Vol 11, No 3 (2025): August 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i3.2123

Abstract

The success of a software project is a crucial factor in the information technology industry, but it is often difficult to predict due to its complexity and high dynamics. This research aims to develop a model for predicting the success of software projects, particularly B2C e-business software in Indonesia, utilizing a machine learning approach. This study involved 28 variables that affect the success of software projects obtained from previous research. The dataset was compiled from the historical records of software projects from various software development companies in Indonesia. The predictive model was developed using Support Vector Machine and Artificial Neural Network algorithms, with hyperparameter tuning performed via Grid Search. The modelling process includes the pre-processing stage of data, which involves synthetic data generation due to inadequate data collection, as well as the application of several dataset mining techniques (SMOTE, ADASYN, SMOTE Tomek Links, and ADASYN Tomek Links). Additionally, model training and performance evaluation are conducted using a confusion matrix. The search for important features using the Shapley Additive Explanations method is also conducted to develop an automated recommendation system based on key factors that require improvement. The results showed that the SVM model with Grid Search tuning of hyperparameters in the SMOTE Tomek Links data test yielded the best performance, with an accuracy of 87.8%, demonstrating the significant potential of machine learning in identifying project success factors from the early stages. This study contributes to the development of decision-support tools for B2C project managers in Indonesia by providing accurate early predictions and interpretable recommendations.

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