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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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Link stability - based optimal routing path for efficient data communication in MANET Salim, Renisha Pulinchuvallil; Ramachandran, Rajesh
International Journal of Advances in Intelligent Informatics Vol 10, No 3 (2024): August 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v10i3.1558

Abstract

The paper delves into the complexities of Mobile Ad hoc Networks (MANETs), which consist of a diverse array of wireless nodes. In such networks, routing packets poses a significant challenge due to their dynamic nature. Despite the variety of techniques available for optimizing routing in MANETs, persistent issues like packet loss, routing overhead, and End-to-End Delay (EED) remain prevalent. In response to these challenges, the paper proposes a novel approach for efficient Data Communication (DC) by introducing a Link Stability (LS)-based optimal routing path. This approach leverages several advanced techniques, including Pearson Correlation Coefficient SWIFFT (PCC-SWIFFT), Galois-based Digital Signature Algorithm (G-DSA), and Entropy-based Gannet Optimization Algorithm (E-GOA). The proposed methodology involves a systematic process. Initially, the nodes in the MANET are initialized to establish the network infrastructure. Subsequently, the Canberra-based K Means (C-K Means) algorithm is employed to identify Neighboring Nodes (NNs), which are pivotal for creating communication links within the network. To ensure secure communication, secret keys (SK) are generated for both the Sender Node (SN) and the Receiver Node (RN) using Galois Theory. Following this, PCC-SWIFFT methodologies are utilized to generate hash codes, serving as unique identifiers for data packets or routing information. Signatures are created and verified at the SN and RN using the G-DSA. Verified nodes are subsequently added to the routing entry table, facilitating the establishment of multiple paths within the network. The Optimal Path (OP) is selected using the E-GOA, considering factors such as link stability and network congestion. Finally, Data Communication (DC) is initiated, continuously monitoring LS to ensure optimal routing performance. Comparative analysis with existing methodologies demonstrates the superior performance of the proposed model. In summary, the proposed approach offers a comprehensive solution to enhance routing efficiency in MANETs by addressing critical issues and leveraging advanced algorithms for key generation, signature verification, and path optimization
Enhanced personalized learning exercise question recommendation model based on knowledge tracing Pei, Pei; Raga Jr., Rodolfo C.; Abisado, Mideth
International Journal of Advances in Intelligent Informatics Vol 10, No 1 (2024): February 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v10i1.1136

Abstract

Personalized exercise question recommendation is a crucial aspect of smart education used to customize educational exercises and questions to individual students' distinct abilities and learning progress. Integrating cognitive diagnosis with deep learning has shown promising results in personalized exercise recommendations. However, the black-box nature of the deep learning model hinders their interpretability. This makes it challenging for educators and students to understand the reasons behind the model's predictions for the next problem, and this limits their opportunity to take an active role in improving the learning process. To address this limitation, this article presents a novel personalized exercise question recommendation model based on knowledge tracing. The approach incorporates graph convolutional neural networks to model the student's abilities, thus enhancing the interpretability of the model. By employing Bidirectional gate recurrent unit (Bi-GRU), the model effectively traces fluctuations in students' abilities over time and predicts their responses to exercise questions. Experimental results demonstrate the effectiveness of this model, achieving an accuracy of 90.8% and 92.6% on ASSISTment 2009 and ASSISTment 2017 datasets, containing 4218 and 1709 student records, respectively. Moreover, the experiment was also conducted to validate the model's exercise difficulty setting. Results indicate an acceptable level of effectiveness in generating appropriate difficulty-level recommendations for individual students. The proposed model contributes to advancing personalized exercise recommendations by offering valuable insights that can lead to more efficient and effective student learning experiences.
Weather classification using meta-based random forest fusion of transfer learning models Gdeeb, Rasha Talib
International Journal of Advances in Intelligent Informatics Vol 10, No 2 (2024): May 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v10i2.1264

Abstract

Weather classification into multiple categories is an essential task for many applications, including farming, military, transport, airlines, navigation, agriculture, etc. A few pieces of research give attention to this field and the current state-of-art methods have limitations, including low accuracy and limited weather conditions. In this study, a new weather classification meta-based fusion of the transfer deep learning model is introduced. The study takes into account all possible weather conditions and utilizes the fusion technique to improve the performance. First, the weather images are pre-processed and a data augmentation process is performed. These images are fed into five transfer deep learning models (XceptionNet, VGG16, ResNet50V2, InceptionV3, and DenseNet201). Then, the meta-based random forest fusion, the meta-based bagging fusion, and the score-level fusion are applied. Finally, all individual and fusion models are evaluated. Experiments were conducted on the WEAPD dataset which includes 11 categories. Results prove that the best performance is related to the meta-based ransom forest fusion method with 96% accuracy. The current study is also compared with the current state-of-art methods, and the comparison proves the robustness and high performance of the current study especially the fact that the current study achieves the best performance on the WEAPD dataset compared to studies worked on the same dataset. The current study proves that meta-based RF fusion is a promising methodology to address the weather classification problem. This outcome can be used by future study to improve the weather classification fusion and ensemble methodologies.
Leveraging hybrid ANN–AHP to optimize cement industry average inventory levels Fradinata, Edy; Noor, Muhamad Mat; Kesuma, Zurnila Marli; Suthummanon, Sakesun; Asmadi, Didi
International Journal of Advances in Intelligent Informatics Vol 10, No 1 (2024): February 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v10i1.631

Abstract

In recent years, inventory has been critical due to the production cost and overstock risk related to the expiration date and the fluctuation price risk. This study's minimization of overstock and price fluctuation in the warehouse used a hybridized artificial neural network (ANN) and analytical hierarchy process (AHP) to produce an optimum model. The variables, such as average demand, reorder point, order quantity, factor service level, safety stock, and average inventory level, were used to obtain the optimal condition of the average inventory levels to maximize the profit. Then, the type of inventory system that guarantees the minimum risks in managing the inventory would be selected. The result shows that the data has a mean of 39.2 units, and the standard deviation (SD) was 12.9. This means that the order quantity is 20.2 units, the average inventory level is 57.3, and the average demand is 39. These conditions used the factor z, which is 97% service level. This study concludes that the optimum average inventory level is 91 units, the order quantity is 11 units with the maximum average profit is $1098, and the peak fluctuation condition maximum profit is $1463 when the average inventory level is 7.3, and the inventory policy system used to minimize the risk is the continuous review policy type. The study could be beneficial to reduce production costs and enhance overall profitability and operational efficiency in the sector by mitigating the risks associated with excessive inventory and price volatility while also minimizing the potential for expired inventory.
Academic expert finding using BERT pre-trained language model Mannix, Ilma Alpha; Yulianti, Evi
International Journal of Advances in Intelligent Informatics Vol 10, No 2 (2024): May 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v10i2.1497

Abstract

Academic expert finding has numerous advantages, such as: finding paper-reviewers, research collaboration, enhancing knowledge transfer, etc. Especially, for research collaboration, researchers tend to seek collaborators who share similar backgrounds or with the same native languages. Despite its importance, academic expert findings remain relatively unexplored within the context of Indonesian language. Recent studies have primarily relied on static word embedding techniques such as Word2Vec to match documents with relevant expertise areas. However, Word2Vec is unable to capture the varying meanings of words in different contexts. To address this research gap, this study employs Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art contextual embedding model. This paper aims to examine the effectiveness of BERT on the task of academic expert finding. The proposed model in this research consists of three variations of BERT, namely IndoBERT (Indonesian BERT), mBERT (Multilingual BERT), and SciBERT (Scientific BERT), which will be compared to a static embedding model using Word2Vec. Two approaches were employed to rank experts using the BERT variations: feature-based and fine-tuning. We found that the IndoBERT model outperforms the baseline by 6–9% when utilizing the feature-based approach and shows an improvement of 10–18% with the fine-tuning approach. Our results proved that the fine-tuning approach performs better than the feature-based approach, with an improvement of 1–5%.  It concludes by using IndoBERT, this research has shown an improved effectiveness in the academic expert finding within the context of Indonesian language.
Empirical study of 3D-HPE on HOI4D egocentric vision dataset based on deep learning Le, Van Hung
International Journal of Advances in Intelligent Informatics Vol 10, No 2 (2024): May 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v10i2.1360

Abstract

3D hand pose estimation (3D-HPE) is one of the tasks performed on data obtained from egocentric vision camera (EVC) such as hand detection, segmentation, and gesture recognition applied in fields such as HCI, HRI, VR, AR, Healthcare, supporting for the visually impaired people, etc. In these applications, hand point cloud data obtained from EV is not very challenging due to being obscured by gaze direction and other objects. Our paper performs a comparative study on 3D right-hand pose estimation (3D-R-HPE) from the HOI4D dataset with four cameras used to collect and animate the dataset. This is a very challenging dataset and was published at CVPR 2022. We use CNNs (P2PR PointNet, Hand PointNet, V2V-PoseNet, and HandFoldingNet - HFNet) to fine-tune the 3D-HPE model based on the point cloud data (PCD) of hand. The resulting error of 3D-HPE is presented as follows: P2PR PointNet (average error (Erra) is 32.71mm), Hand PointNet (average error (Erra) is 35.12mm), V2V-PoseNet (average error (Erra) is 26.32mm), and HFNet (average error (Erra) is 20.49mm). HFNet is the latest CNN (in 2021) with the best results. This estimation error is small and can be applied and modeled to automatically detect, estimate, and recognize hand pose from the data obtained by EV. The average processing time is 5.4fps when done on the GPU of the HFNet, which is the fastest. Detailed quantitative and qualitative results were presented that are beneficial to various applications such as human-computer interaction, virtual and augmented reality, and healthcare, particularly in challenging scenarios involving occlusions and complex datasets.
Enhanced U-Net architecture with CNN backbone for accurate segmentation of skin lesions in dermoscopic images Aqthobirrobbany, Aqil; Al-Fahsi, Resha Dwika Hefni; Soesanti, Indah; Nugroho, Hanung Adi
International Journal of Advances in Intelligent Informatics Vol 10, No 3 (2024): August 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v10i3.1379

Abstract

Addressing the critical public health challenge of skin cancer, particularly melanoma and non-melanoma, this study focuses on enhancing early diagnosis through improved automatic segmentation of skin lesions in dermoscopic images. The researchers propose an optimized U-Net architecture that integrates advanced convolutional neural networks (CNNs) with backbone models such as ResNet50, VGG16, and MobileNetV2, specifically designed to handle the inherent variability and artifacts in dermoscopic imagery. The method's effectiveness was validated using the ISIC-2018 dataset, and our U-Net model incorporating the VGG16 backbone achieved notable improvements in segmentation accuracy, demonstrating an accuracy rate of 0.93. These results signify significant enhancements over existing methods, emphasizing the potential of the proposed approach in aiding precise skin cancer diagnosis and detection. This study makes a valuable contribution to dermatological imaging by presenting an advanced method that substantially boosts the accuracy of skin lesion segmentation, addressing a crucial need in public health.
Ensemble semi-supervised learning in facial expression recognition Purnawansyah, Purnawansyah; Adnan, Adam; Darwis, Herdianti; Wibawa, Aji Prasetya; Widyaningtyas, Triyanna; Haviluddin, Haviluddin
International Journal of Advances in Intelligent Informatics Vol 11, No 1 (2025): February 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i1.1880

Abstract

Facial Expression Recognition (FER) plays a crucial role in human-computer interaction, yet improving its accuracy remains a significant challenge. This study aims to enhance the robustness and effectiveness of FER systems by integrating multiple machine learning techniques within a semi-supervised learning framework. The primary objective is to develop a more effective ensemble model that combines Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), Support Vector Classifier (SVC), and Random Forest classifiers, utilizing both labeled and unlabeled data. The research implements data augmentation and feature extraction techniques, utilizing advanced architectures such as VGG19, ResNet50, and InceptionV3 to improve the quality and representation of facial expression data. Evaluations were conducted across three dataset scenarios: original, feature-extracted, and augmented, using various label-to-unlabeled ratios. The results indicate that the ensemble model achieved a notable accuracy improvement of 87% on the augmented dataset compared to individual classifiers and other ensemble methods, demonstrating superior performance in handling occlusions and diverse data conditions. However, several limitations exist. The study’s reliance on the JAFFE dataset may restrict its generalizability, as it may not cover the full range of facial expressions encountered in real-world scenarios. Additionally, the effect of label-to-unlabeled ratios on the model's performance requires further exploration. Computational efficiency and training time were also not evaluated, which are critical considerations for practical implementation. For future research, it is recommended to employ cross-validation methods for more robust performance evaluation, explore additional data augmentation techniques, optimize ensemble configurations, and address the computational efficiency of the model to better advance FER technologies.
Traffic light optimization (TLO) using reinforcement learning for automated transport systems Hassan, Mohammad Mehedi; Karungaru, Stephen; Terada, Kenji
International Journal of Advances in Intelligent Informatics Vol 11, No 1 (2025): February 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i1.1655

Abstract

Current traffic light systems follow predefined timing sequences, causing the light to turn green even when no cars are waiting, while the side road with waiting vehicles may still face a red light. Reinforcement learning can help by training an intelligent model to analyze real-time traffic conditions and dynamically adjust signal lights based on actual demand and necessity. If the traffic light becomes intelligent and autonomous then it can significantly reduce the time wasted everyday commuting due to previously determined traffic light timing sequences. In our previous work, we used fuzzy logic to control the traffic light where the time was fixed but in this paper, the waiting time becomes a variable that changes depending on other road variables like vehicles, pedestrians, and times. Moreover, we trained an agent in this work using reinforcement learning to optimize the traffic flow in junctions with traffic lights. The trained agent worked using the greedy method to improve traffic flow to maximize the rewards by changing the signals appropriately. We have two states and there are only two actions to take for the agent. The results of the training of the model are promising.  In normal situations, the average waiting time was 9.16 seconds. After applying our fuzzy rules, the average waiting time was reduced to 0.26 seconds, and after applying reinforcement learning, it was 0.12 seconds in a simulator. The average waiting time was reduced by 97~98%. These models have the potential to improve real-world traffic efficiency by approximately 67~68%.
Advanced deep learning techniques for sentiment analysis: combining Bi-LSTM, CNN, and attention layers Mirdan, Asmaa Sami; Buyrukoglu, Selim; Baker, Mohammed Rashad
International Journal of Advances in Intelligent Informatics Vol 11, No 1 (2025): February 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i1.1848

Abstract

Online platforms enhance customer engagement and provide businesses with valuable data for predictive analysis, critical for strategic sales forecasting and customer relationship management. This study explores in depth the potential of sentiment analysis (SA) to enhance sales forecasting and customer retention for small and large businesses. We collected a large dataset of product review tweets, representing a rich consumer sentiment source. We developed an artificial neural network based on a dataset of product review tweets that captures both positive and negative sentiments. The core of our model is Bi-LSTM (Bidirectional Long Short-Term Memory) architecture, enhanced by an attention mechanism to capture relationships between words and emphasize key terms. Then, a one-dimensional convolutional neural network with 64 filters of size 3x3 is applied, followed by Average_Max_Pooling to reduce the feature map. Finally, two dense layers classify the sentiment as positive or negative. This research provides significant benefits and contributions to sentiment analysis by accurately identifying consumer sentiment in product review tweets. The proposed model that integrated Bi-LSTM with attention mechanism and CNN detects negative sentiment with a precision of 0.97, recall of 0.98, and F1-score of 0.98, allowing companies to address customer concerns, improving satisfaction and brand loyalty proactively. In addition, the proposed model presents a better sentiment classification on average for both positive and negative sentiments, and accuracy (96%) compared to the other baselines. It ensures high-quality input data by reducing noise and inconsistencies in product review tweets. Moreover, the dataset collected in this study serves as a valuable benchmark for future research in sentiment analysis and predictive analytics.

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