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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 353 Documents
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Multi-sensor weather data prediction-based artificial neural network algorithm Indra Riyanto; Erica Reva Akilah; Akhmad Musafa; Eka Purwa Laksana; Nazori Agani
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.v12i2.1841

Abstract

Accurate weather prediction is essential for supporting various human activities and mitigating the impacts of changing atmospheric conditions. Recent advances in artificial intelligence have enabled the development of data-driven forecasting models capable of capturing complex relationships among meteorological variables. This study proposes an Artificial Neural Network (ANN)-based weather prediction model using multi-sensor weather data, including temperature, humidity, precipitation, solar irradiance, and wind velocity. The proposed ANN architecture consists of an input layer, two hidden layers, and an output layer. Model performance was evaluated using three training–testing data splits (90/10, 80/20, and 70/30) with 100 and 150 training epochs. Prediction performance was assessed using accuracy and Root Mean Squared Error (RMSE). Experimental results demonstrate that the proposed model achieves the best performance with a 70/30 training–testing split and 150 training epochs, providing the highest prediction accuracy and the lowest RMSE among the evaluated configurations. These findings indicate that a relatively simple ANN architecture can effectively model multi-sensor weather data and provide reliable weather predictions.
Enhancing safety with IoT and machine learning: a novel smart safety net design Shokhan M Al-Barzinji; Zahraa H Ameen; Noor Abdul Khaleq Zghair; Abubakr S Issa; Samer Raad Azzawie; Ali Abdulateef Abdulbari
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.v12i2.1856

Abstract

The Internet of Things (IoT) represents a complex network of embedded devices that exchange data through heterogeneous communication technologies, making them increasingly vulnerable to sophisticated cyber attacks. This paper presents a hybrid Intrusion Detection System (HIDS) that integrates Extra Trees (ExtraTreesClassifier) for feature selection with four ensemble classifiers: XGBoost, CatBoost, AdaBoost, and Gradient Boosting. Our approach performs supervised feature selection exclusively on training data to prevent information leakage, applies class balancing for imbalanced datasets, and evaluates each hybrid model using comprehensive metrics including ROC-AUC, PR-AUC, false positive/negative rates, and Matthews Correlation Coefficient. We validate our methodology on three benchmark datasets with contrasting characteristics: UNSW-NB15 (real-world network traffic, 175K samples), IoTNet24 (laboratory-controlled traffic, 23K samples), and BoTNeTIoT-L01 (large-scale laboratory traffic, 2.4M samples). On UNSW-NB15, our best model (EXT-GB) achieves 87.73% accuracy, 0.90 F1-score, 0.98 ROC-AUC, and 98.58% recall with 1.42% false negative rate, representing realistic performance for production IDS. On laboratory datasets after addressing class imbalance, models achieve near-perfect performance (IoTNet24: 99.96%, BoTNeTIoT: 99.99%). The 12-percentage-point performance gap between real-world and laboratory data highlights a critical finding: controlled laboratory datasets significantly overestimate real-world IDS capability, underscoring the importance of evaluation on realistic traffic captures for assessing production deployment readiness.
Toward effective Text-to-MongoDB query translation Aicha Aggoune
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.v12i2.2360

Abstract

Translating natural language questions into MongoDB queries is critical for flexible data access in current NoSQL systems. However, semantic ambiguity in user questions and MongoDB's dynamic schema make this work difficult. This study presents QMQL (Question to Mongo Query Language), a hybrid approach meant to address these challenges. QMQL combines a Graph Attention Network (GAT) for refining schema elements with a Retrieval-Augmented Generation (RAG) mechanism that employs BERT embeddings to retrieve relevant schema and resolve semantic ambiguity. A T5-base model is used to generate a MongoDB query corresponding to the user’s question. An experimental evaluation on an extended dataset encompassing various real-world domains demonstrates the effectiveness of the proposed approach. QMQL achieves excellent performance with an EMA of 0.89, an EM of 0.91, and a BLEU score of 0.95, exceeding previous approaches, particularly for semantically ambiguous questions and sophisticated queries across flexible MongoDB schemas,
Enhanced tuberculosis diagnosis: microscopic automatic stitching of sputum samples utilizing the SURF feature detector Nadhya Gita Anggana; Indrarini Dyah Irawati; Suci Aulia; Lestari Lestari
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.v12i2.2325

Abstract

In conventional tuberculosis diagnosis, 100-300 fields of view (FOVs) must be observed, which can lead to observer fatigue. For both tasks, an automatic stitching framework was developed that extends conventional feature-based transformations by incorporating affine-geometry-based feature matching and RANSAC-based homography refinement, thereby accounting for the unique low-texture morphology and irregular patterns of Mycobacterium tuberculosis in ZN-stained sputum smears. The system was tested on a set of 10 overlapping image pairs with a fixed overlap of 30%. Among the evaluated image pairs, the proposed optimized method achieved a 100% success rate. Objective zero-pixel metric-based quantitative analysis also validated higher transparency quality compared to other methods. The proposed SURF implementation reached a minimum number of 345.263 zero-pixels, outperforming standard SURF (964.247) and SIFT (1.069.687). This improved robustness to rotation and illumination variations made the optimized SURF-affine framework a preferred choice for automatic TB diagnosis systems.
Iron welding spot segmentation using Nested UNet (UNet++) enhanced with diverse convolutional modules Thomas Brian; Oskar Natan; Yohanes Yohanie Fridelin Panduman; Anggarjuna Puncak Pujiputra
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.v12i2.2250

Abstract

Welding inspection plays an essential role in manufacturing industries to ensure the integrity and quality of weld joints. However, the prevalent manual inspection procedures are inherently subjective, prone to bias, and result in inconsistent quality assessments. Therefore, there is a strong need for an automated, intelligent system capable of objectively detecting welding spots. To address this, we propose an advanced segmentation model based on deep learning and computer vision techniques, specifically utilizing a Nested UNet (UNet++) architecture enhanced by extensive architectural modifications and comprehensive hyperparameter tuning. To further optimize segmentation performance, we systematically compare various convolutional blocks integrated into the bottleneck of the network architecture. Our experimental evaluation demonstrates that employing a VGG convolutional block at the bottleneck of Nested UNet achieves the highest performance, reaching an Intersection over Union (IoU) score of 76.18% and a validation loss of 0.1713 on our collected dataset. This research advances automated welding inspection by demonstrating that an enhanced Nested UNet with an optimized VGG bottleneck significantly improves weld spot segmentation accuracy. The proposed framework provides a robust benchmark for future studies on intelligent industrial visual inspection and deep learning-based quality assurance.
AI-driven stress monitoring in melon crops via graph neural networks Son Ali Akbar; Jihad Rahmawan; Etika Dyah Puspitasari; Anton Yudhana; Novi Febrianti
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.v12i2.2237

Abstract

Melon cultivation is highly vulnerable to abiotic and biotic stress, and early detection remains difficult when monitoring relies on a single sensing modality. This study investigated a multimodal stress-classification framework that combined root-zone measurements and canopy reflectance descriptors for melon monitoring under greenhouse conditions. Soil pH, nitrogen, phosphorus, potassium, and temperature were acquired using an RS485 multi-parameter sensor, while canopy images were captured using a Raspberry Pi NoIR camera and converted into Normalized Difference Vegetation Index features. Each synchronized observation was represented as a graph with fixed variable nodes and correlation-based edges, enabling relation-aware learning through a Graph Convolutional Network. The proposed model was evaluated using cross-validation and compared against conventional machine learning and non-graph deep learning baselines. The graph-based model achieved the best overall classification performance, indicating that explicit modeling of soil-canopy dependencies improved discrimination between healthy and stressed plants. The results suggest that graph-structured multimodal fusion is a promising strategy for AI-assisted crop stress monitoring.
Automatic identification of herbal medicines using deep learning on leaf images Anita Ahmad Kasim; Lukman Nadjamudiin; Muhammad Bakri; Chairunnisa Ardiansyah Lamasitudju; Harry Tanni Pagiu; Puguh Budi Prakoso; Anindita Septiarini; Bima Prihasto
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.v12i2.2024

Abstract

Indonesia has a high diversity of medicinal plants that are widely used in traditional healthcare practices. Identification of medicinal plants is commonly based on leaf morphology; however, similarities in leaf shape, texture, and color often cause misidentification, particularly among non-experts. This limitation highlights the need for an automated and reliable identification approach. The primary objective of this study is to develop and evaluate a deep learning–based system for the automatic identification of medicinal plants using leaf images, with a specific focus on comparing the performance and efficiency of MobileNetV2 and ResNet50V2 architectures. The research design adopts an experimental approach using an internally collected dataset of medicinal plant leaf images representing multiple plant classes. The dataset is divided into training and testing sets to evaluate model generalization. The methodology involves image preprocessing steps, including resizing, normalization, and data augmentation, followed by the application of transfer learning using MobileNetV2 and ResNet50V2 as feature extractors. Both models are trained under the same experimental settings and evaluated using standard classification metrics, including accuracy, precision, recall, F1-score, and confusion matrix analysis. The main outcomes and results indicate that both deep learning models achieve high classification performance. MobileNetV2 achieves an accuracy of 98.77%, precision of 98.84%, recall of 98.77%, and F1-score of 98.77%, while ResNet50V2 achieves an accuracy of 97.53%, precision of 97.87%, recall of 97.53%, and F1-score of 97.58%. The results demonstrate that MobileNetV2 provides slightly superior performance with lower computational complexity. In conclusion, lightweight deep learning architectures such as MobileNetV2 are effective and efficient for medicinal plant leaf identification and are suitable for implementation in mobile or resource-constrained environments.
Enhancing anomaly lane detection accuracy using feature cross attention in U-Net architecture Zahir Zainuddin; Muhammad Abdillah Rahmat; Elly Warni; A. Ais Prayogi Alimuddin
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.v12i2.2327

Abstract

Lane departure detection is a crucial task in advanced driver assistance systems (ADAS) and autonomous driving, aimed at reducing accidents caused by unintentional road deviation. This study proposes a modified U-Net architecture enhanced with Feature Cross Attention (FCA) to improve lane departure anomaly detection. The objective is to enhance spatial sensitivity and context awareness in segmentation, especially under challenging driving conditions such as occlusions, poor lighting, and distorted lane geometry. The materials used include the publicly available Comma 2k19 LD dataset, comprising 2,000 manually annotated frames extracted from highway driving scenarios. Each frame includes synchronized video and driving telemetry, offering diverse visual conditions. Preprocessing steps include resizing, normalization, and annotation conversion to binary masks. An anomaly is defined as a spatial deviation threshold between predicted and ground-truth lane boundaries. The proposed method incorporates FCA at the bottleneck and decoder levels of the U-Net architecture. Evaluation was performed using Intersection over Union (IoU), Pixel Accuracy, and threshold-based anomaly criteria. The model achieved 99.19% Pixel Accuracy and 98.47% IoU, outperforming the baseline U-Net (97.56% and 97.46%, respectively). Visual results showed improved detection of subtle lane shifts. A confusion matrix generated over 210 validation images demonstrated perfect classification of normal and anomalous cases. These results confirm that FCA integration enhances segmentation precision and anomaly sensitivity. The approach is suitable for real-time deployment in autonomous systems. Future research may focus on temporal integration, lightweight optimization for embedded devices, and the extension of the framework to multi-lane or urban traffic environments.
Blockchain-based 5G handoff authentication system using joint-graph-based delegated practical byzantine fault tolerance consensus approach Ravindra Janardan Lawande; Sudhir Bapurao Lande; Shailendrakumar Mahadeo Mukane
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.v12i2.2247

Abstract

The 5G mobile networks offer extra benefits in terms of high data rates, lower latency, and more coverage in comparison to 4 G networks. However, the 5G network offers new levels of data transfer and processing speeds, ensuring users do not disconnect as they move from one cell to another. By considering these issues, this paper proposes a new blockchain-based, scalable, and reliable 5G handoff authentication system. The proposed approach is intended to ensure authentication using Exponential Elliptic Curve-Assisted Encryption (EEE) between the user and the base station. After the successful authentication, the user stores the details in the source base station. In a blockchain-based 5G handoff authentication system, when a user device enters the range of a new base station, it sends a handoff request using a pseudorandom frequency-hopping sequence. The serving base station sends a handover command to the mobile device, containing details about the target base station and the next frequency in the hopping sequence. This request is disseminated through an improved gossip algorithm that minimizes communication overhead and accelerates node authentication in the blockchain for consensus and validation. Similarly, in blockchain networks, the gossip protocol ensures that every node in the network receives information about the request for handoff while ensuring that the messages sent are not redundant. Therefore, the joint graph-based Delegated Practical Byzantine Fault Tolerance (JG-DPBFT) consensus process is utilized to verify the handoff.
Comparison of SVM kernels in brain tumor image classification using GLCM feature extraction Kraugusteeliana Kraugusteeliana; I Gede Susrama Mas Diyasa; Hanif Nur Fadlilah; Yisti Vita Via; Anita Muliawati; Allan Ruhui Fatmah Sari; Erna Harfiani; Ni Made Ika Marini Mandenni; Deshinta Arrova Dewi
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.v12i2.2129

Abstract

The human brain plays a vital role in regulating bodily functions, and abnormal cell growth may lead to life-threatening brain tumors. Automated computer-aided diagnosis systems are therefore essential to support early detection from MRI images. This study investigates brain tumor classification using Gray Level Co-occurrence Matrix (GLCM) feature extraction combined with Support Vector Machine (SVM) classification. Unlike prior works that typically employ a single kernel configuration, this study conducts a systematic comparison of four SVM kernels: linear, polynomial, radial basis function (RBF), and sigmoid under a consistent preprocessing pipeline and structured hyperparameter tuning framework. GLCM features, including energy, contrast, correlation, and homogeneity, were extracted at multiple distances and angles. Kernel performance was evaluated using controlled hyperparameter search procedures to ensure fair comparison across models. Experimental results on a binary MRI dataset consisting of 2,800 images demonstrate that the RBF kernel achieved the highest accuracy of 96% with C = 100 and gamma = 10, outperforming polynomial (74%), linear (72%), and sigmoid (71%) kernels. The findings highlight the importance of systematic kernel evaluation and parameter sensitivity analysis in texture-based medical image classification. The proposed GLCM–SVM framework provides a computationally efficient and interpretable approach that may support preliminary decision-aid systems for brain tumor screening.

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