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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 330 Documents
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A dual-phase hybrid framework for real-time grayscale image denoising in structured noise Witefee, Diyar Mohammed; Al-kharaz, Ali Abdulmunim
International Journal of Advances in Intelligent Informatics Vol 11, No 4 (2025): November 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i4.2158

Abstract

Image denoising is a substantial section in the preprocessing stage, especially in medical images. This study proposed a hybrid denoising model for salt-and-pepper removal in grayscale images. The framework uses a U-Net convolutional neural network, modified to perform preliminary denoising, and the Alternating Direction Method (ADM) to refine the structure iteratively. A corrupted pixel location is first determined using an adaptive thresholding scheme. The model is trained with a composite loss function that combines pixel-wise reconstruction accuracy (MSE) and perceptual similarity, as measured by the Structural Similarity Index (SSIM). Tests conducted on benchmarks (e.g., Kodak24, Set14, DIV2K, and TID2013) show that the proposed method surpasses traditional filters and state-of-the-art deep learning models, e.g., FFDNet and DnCNN. The quantitative results are Peak Signal-to-Noise Ratio (PSNR) 32.45 dB, SSIM 0.92 against 30 percent salt-and-pepper noise, and the average speed of inference is 6.2 ms, showing improvements over baseline approaches in performance and appearance. The main innovation is combining a noise-aware adaptive detection step with a specially designed U-Net framework and ADM-sided refinement, achieving better edge preservation and robustness to noise at any level. The framework displays a high potential for use in medical imaging, document recovery, and real-time surveillance.
Multi-objective optimization algorithm for improving the efficiency of speeded up robust features of image stitching Sanprasit, Kittisak; Butsathip, Uraiwan; Chaiwong, Khomyuth
International Journal of Advances in Intelligent Informatics Vol 11, No 4 (2025): November 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i4.2084

Abstract

Image stitching to generate panoramic or composite images. This research proposes improved parameters for the fundamental matrix in the standard SURF method via multi-objective optimization. This paper compares three metaheuristic algorithms (MOWOA, MOGWO, MOGA) and evaluates their performance using the hypervolume indicator (HV). The optimal points were selected from non-dominated solutions using the MCDM and the weighted-sum method (WSM). There were two objective functions: 1) minimum of image subtraction and 2) minimum of histogram. The MOWOA is superior to the other. This approach significantly reduces stitching errors and improves performance by 24.48% over standard SURF. The proposed multi-objective optimization of fundamental matrix parameters significantly enhances SURF-based image stitching by reducing alignment and blending errors, resulting in smoother, more coherent panoramic or composite images. This is achieved by leveraging superior metaheuristic performance, particularly from MOWOA, which outperforms other algorithms. This approach increases stitching robustness and accuracy, making it highly valuable for real-world applications such as mapping, surveillance, and visual reconstruction.
Enhanced intrusion detection in smart grids using extended long short-term memory variants Baalia, Saida; Boughareb, Djalila; Kouahla, Zineddine; Seridi, Hamid
International Journal of Advances in Intelligent Informatics Vol 11, No 4 (2025): November 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i4.2169

Abstract

Smart grid systems, which integrate traditional energy infrastructure with modern communication technologies, face significant cybersecurity challenges due to their dynamic architecture and continuous data exchange. The diversity and interconnection of devices increase vulnerability to malicious intrusions, highlighting the need for advanced and scalable detection methods. This study aims to develop an intrusion detection system (IDS) for smart grids by leveraging recent advances in deep learning, specifically enhanced variants of Long Short-Term Memory (LSTM)—xLSTM, sLSTM, and mLSTM. These sequence modeling architectures were adapted and fine-tuned within our IDS framework to capture complex spatio-temporal patterns and handle heterogeneous, high-dimensional data effectively. A comprehensive evaluation on two benchmark datasets, NSL-KDD and DNP3, demonstrates the robustness of the proposed approach. On the NSL- KDD, xLSTM, sLSTM, and mLSTM achieved accuracies of 98.16%, 98.55%, and 98.54%. On the more modern, protocol-specific DNP3 dataset, which represents real-world SCADA-focused attacks, the models maintained their superior performance, achieving accuracies of 99.50%, 99.33%, and 99.42%, respectively. The high and consistent accuracy across both datasets demonstrates the models' dependability and adaptability for intrusion detection in smart grid infrastructures. The study's targeted enhancement of LSTM-based architectures contributes a novel and effective approach to protecting critical intelligent systems from emerging cyber threats.
Performance analysis on convergence of particle swarm optimization and incremental conductance MPPT method for NTR 5E3E PV module Burhanudin, Kharismi; Abas, Zuraida Abal; Asmai, Siti Azirah; Hidayat, Muhamad Nabil
International Journal of Advances in Intelligent Informatics Vol 11, No 4 (2025): November 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i4.2143

Abstract

Particle swarm optimization (PSO), a technique in Artificial Intelligence, is one of the MPPT methods used to optimize the output of a Photovoltaic (PV) system. The PSO is well known for its convergence in Maximum Power Point Tracking (MPPT). However, no comprehensive study has been conducted on the performance of the PSO and incremental-conductance (INC) MPPT combination for the NTR 5E3E PV module. This study aims to provide a detailed performance analysis of the convergence of PSO and INC combination compared to PSO MPPT during maximum power (MP) tracking on NTR 5E3E PV module. This research work studies the relationships among PV parameters and other parameters affected during the implementation of PSO-INC MPPT. The study found that, in terms of efficient power and time consumption during the Maximum Power (MP) tracking process, the PSO-INC MPPT combination provides the highest average peak power at the shortest time compared to standalone PSO. The efficiency of PSO-INC Average Power is near 98.9% to 99.93%, compared to PSO MPPT, which is between 95.7% and 99.3%. The PSO and INC MPPT were tested on a boost converter without altering the specific electrical component characteristics to ensure accurate output during testing. Furthermore, a boost converter is sufficient to meet the overall requirements for the research work and simulation testing. The characteristics of the PSO and INC MPPT are observed using MATLAB/Simulink. This research assesses the robustness of the PSO-INC combination, advancing hybrid MPPT technology by demonstrating its performance.
A cascaded classification approach using transfer learning and feature engineering for improved breast cancer classification Ferkous, Chokri; Fadel, Ouissal; Kefali, Abderrahmane; Merouani, Hayet-Farida
International Journal of Advances in Intelligent Informatics Vol 12, No 1 (2026): February 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i1.1670

Abstract

The primary objective of this study is to design a cascaded classification framework that integrates deep-learning representations with handcrafted and clinical features to enhance the reliability and accuracy of breast cancer detection in mammographic screening. A multi-source mammography dataset comprising four databases was used to ensure diversity and reduce bias. The proposed system operates in two stages. In the first stage, transfer learning models (VGG16, ResNet50, and EfficientNet_B0) were evaluated using ROC-AUC, PR-AUC, calibration curves, and bootstrap confidence intervals. EfficientNet_B0, which achieved the best balance between discrimination and calibration, was selected as the feature extractor. In the second stage, the malignancy probability was combined with Haralick texture features, patient age, and breast density, and classified using SVM, Random Forest, MLP, Decision Tree, and Logistic Regression. Model robustness was verified through multi-run experiments (five random seeds) and subgroup analyses by age and density. Among the CNN models, EfficientNet_B0 yielded the best performance (accuracy = 0.9438, ROC-AUC = 0.944, PR-AUC = 0.960). In the second stage, although Random Forest achieved the highest accuracy (0.9556 ± 0.002), SVM obtained the highest mean ROC-AUC (0.980 ± 0.001) with stable accuracy (0.9539 ± 0.001) and the most significant p-values, indicating superior robustness and generalization. The proposed cascaded framework effectively combines deep, handcrafted, and clinical features to improve mammogram classification performance. The SVM-based model demonstrates strong calibration, stability, and subgroup consistency, highlighting its potential for deployment in computer-aided mammography screening systems that assist radiologists in early breast cancer detection.
Augmented haar cascade classifier for real-time ball detection in humanoid robots under dynamic environments Setyawan, Gembong Edhi; Widasari, Edita Rosana; Prasetio, Barlian Henryranu; Umar, Yasa Palaguna; Adipratama, Ivan Rafli
International Journal of Advances in Intelligent Informatics Vol 12, No 1 (2026): February 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i1.2146

Abstract

This study proposes an Augmented Haar Cascade Classifier (AHCC) to enhance real-time ball detection for humanoid robots operating in dynamic environments. The method integrates Convex Hull mapping, HSV-based segmentation, and Hough Circle validation to overcome challenges such as fluctuating illumination, complex backgrounds, and partial occlusions. Experiments were conducted entirely on a CPU-only Intel NUC platform running ROS without GPU acceleration, using a dataset containing variations in lighting, orientation, scale, and background clutter. Compared with baseline models (standard Haar Cascade Classifier (HCC) and YOLOv5) the proposed AHCC achieved 97% accuracy, 83% recall, 97% precision, and an 89% F1-score, while requiring only 0.00849 s per frame with 8.97% memory usage. Although YOLOv5 reached 99% accuracy, it demanded higher computational resources (0.0344 s per frame, 22.3% memory usage), limiting its practicality for embedded robotic systems. The AHCC therefore offers an optimal balance between detection reliability and computational efficiency, outperforming traditional HCC and providing a lightweight alternative to GPU-dependent detectors such as Tiny-YOLO and MobileNet-SSD.
TUD-BISINDO: A new dataset and its recognition system using YOLO Raihan, Muhammad; Lestari, Aulia Ayu Dyah; Aulia, Suci; Hariyani, Yuli Sun; Maharani, Devira Anggi
International Journal of Advances in Intelligent Informatics Vol 12, No 1 (2026): February 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i1.2329

Abstract

This study addresses the urgent need for digital inclusivity by developing a high-precision, real-time recognition system for Bahasa Isyarat Indonesia (BISINDO). The main new idea in this study is the creation of the Telkom University Database (TUD)-BISINDO, which is a strong and varied collection of data designed to fix the problems of current sign language databases, like not having enough different environments and camera angles. The TUD-BISINDO was created using 1,040 original images and added 780 more images to fix problems like differences in lighting, angles, and hand features that were often found in earlier datasets. The YOLOv8l model, improved with the AdamW optimizer and a flexible learning rate, performed exceptionally well with a mAP50 of 99.30% mAP50-95 of 85.40%, 99.80% precision, and 99.70% recall. These results demonstrate that the model significantly outperforms the previous YOLOv5 baseline across all primary metrics. The model has outstanding precision in recognizing real-time finger movements. However, complicated gestures, including the G and Z letters, require additional improvement. This research enhances sign language recognition technology, encouraging inclusion and improving accessibility for real-time communication. Future studies should focus on diversifying the dataset and maximizing performance in challenging conditions.
Comparative analysis of xception and svm for brain tumor classification on mri images Huda, Nurul; Ku-Mahamud, Ku Ruhana
International Journal of Advances in Intelligent Informatics Vol 12, No 1 (2026): February 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i1.2016

Abstract

Brain tumor classification from magnetic resonance imaging (MRI) plays a critical role in supporting radiologists during diagnosis and treatment planning. However, many existing automated approaches employ limited preprocessing, single-stage transfer learning, or evaluation on a single dataset, which restricts robustness and clinical applicability. This study proposes an enhanced transfer-learning framework based on the Xception architecture for multiclass brain tumor classification and compares its performance with baseline models under identical experimental conditions. The framework integrates a comprehensive preprocessing pipeline consisting of normalization, adaptive noise filtering, contrast enhancement, and targeted data augmentation, together with a structured two-phase fine-tuning strategy. A total of 6,537 MRI images were used, employing five-fold cross-validation, independent testing, and validation on an additional benchmark dataset. The proposed model achieved a mean cross-validation accuracy of 0.8994 ± 0.089 and 99.06% accuracy, precision, and recall on the independent test set, demonstrating strong stability and generalization ability. Evaluation on the CE-MRI Figshare dataset further confirmed robustness, yielding 98.45% accuracy, 98% precision, and 98% recall. In contrast, when re-evaluated in the same experimental setting, baseline models performed considerably worse: the SVM classifier achieved 21.41% accuracy, and ResNet50 reached 75.27%, both substantially lower than Xception. Although higher accuracies for these models have been reported in prior studies under different conditions, the present findings highlight their limited generalization under unified evaluation. Overall, the proposed Xception-based framework provides a reliable and generalizable solution for automated brain tumor classification, with strong potential to support clinical workflows such as triage prioritization and second-opinion assistance.
Fine-tuned hyperparameter optimization for phishing website detection: insights into efficiency and performance Wahyudi, Rizki; Barkah, Azhari Shouni; Selamat, Siti Rahayu; Subarkah, Pungkas
International Journal of Advances in Intelligent Informatics Vol 12, No 1 (2026): February 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i1.1920

Abstract

The escalation of digital threats has made phishing-site identification a critical aspect of online protection. This study investigates how systematic hyperparameter adjustment through grid search influences both predictive precision and computational efficiency in phishing detection. Nine supervised classifiers from different algorithmic families were analyzed: tree-based models (DT, RF, GB, XGBoost), margin and distance-based learners (SVM, k-NN), probabilistic and neural approaches (NB, MLP), and a linear baseline using logistic regression (LR). Although machine learning (ML) approaches have demonstrated strong predictive capability, their reliability largely depends on precise parameter calibration. Through systematic exploration of parameter combinations, the grid-search approach identifies optimal settings for each model. Using the Kaggle phishing-URL dataset, tuned models achieved noticeable accuracy gains. DT, RF, and k-NN reached 99.1% accuracy with training times of 0.10 s, 1.55 s, and 0.01 s, respectively. MLP yielded 99.0% accuracy but required 2758 s, while SVM and LR achieved 97.8% and 92.9%. NB did the worst (62.7%). The results indicate that careful hyperparameter optimization enhances predictive ability, whereas model complexity heavily impacts runtime. This study’s novelty lies in a balanced assessment of accuracy and efficiency trade-offs, offering guidelines for selecting computationally efficient algorithms in practical phishing-detection systems.
Synergistic preprocessing approaches for improved time series analysis Pranolo, Andri; Pujiyanta, Ardi; Supriyanto, Supriyanto
International Journal of Advances in Intelligent Informatics Vol 12, No 1 (2026): February 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i1.2321

Abstract

This paper systematically evaluates the performance of an LSTM baseline model, along with four smoothing augmentation methods (Kalman, Laplace, Moving Average, Savitzky-Golay), under different normalization strategies (Min-Max and Z-Score) for multivariate time-series forecasting. Experiments were conducted on six publicly available datasets (electricity consumption, energy consumption, sensor data, household energy, Indian electricity, and Brazilian temperature), and model performance was comprehensively compared using three metrics: MAPE, RMSE, and R². Results indicate that Laplace smoothing achieved the best performance across five datasets, effectively reducing errors while maintaining high fit quality, demonstrating its advantage in handling highly volatile and noisy time-series data. However, in some instances, Laplace smoothing, along with MA and SG methods, may produce an “over-smoothing” effect, causing forecasts to lose sensitivity to spike fluctuations. The choice of normalization strategy is equally critical: Min-Max is more suitable for data with stable distributions, while Z-Score demonstrates greater advantages for data with large numerical ranges and significant volatility. Notably, in temperature datasets with small sample sizes and high volatility, complex smoothing methods actually degraded performance, making the baseline LSTM + Z-Score the optimal choice. However, the LSTM-Laplace model with Min-Max normalization achieves the best performance among the models. Overall, the study concludes that improving prediction performance relies not only on model architecture but also on optimizing data scale, distribution characteristics, and preprocessing strategies.

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