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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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Semi-supervised learning for sentiment classification with ensemble multi-classifier approach Agus Sasmito Aribowo; Halizah Basiron; Noor Fazilla Abd Yusof
International Journal of Advances in Intelligent Informatics Vol 8, No 3 (2022): November 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v8i3.929

Abstract

Supervised sentiment analysis ideally uses a fully labeled data set for modeling. However, this ideal condition requires a struggle in the label annotation process. Semi-supervised learning (SSL) has emerged as a promising method to avoid time-consuming and expensive data labeling without reducing model performance. However, the research on SSL is still limited and its performance needs to be improved. Thus, this study aims to create a new SSL-Model for sentiment analysis. The Ensemble Classifier SSL model for sentiment classification is introduced. The research went through pre-processing, vectorization, and feature extraction using TF-IDF and n-grams. Support Vector Machine (SVM) or Random Forest for tokenization was used to separate unigram, bigram, and trigram in model generation. Then, the outputs of these models were combined using stacking ensemble approach. Accuracy and F1-score were used for the evaluation. IMDB datasets and US Airlines were used to test the new SSL models. The conclusion is that the sentiment annotation accuracy is highly dependent on the suitability of the dataset with the machine learning algorithm. In IMDB dataset, which consists of two classes, it is better to use SVM. In the US Airlines consisting of three classes, SVM is better at improving the model performance against the baseline, but RF is better at achieving the baseline performance even though it fails to maintain the model performance.
Boosting and bagging classification for computer science journal Nastiti Susetyo Fanany Putri; Aji Prasetya Wibawa; Harits Ar Rasyid; Andrew Nafalski; Ummi Rabaah Hasyim
International Journal of Advances in Intelligent Informatics Vol 9, No 1 (2023): March 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i1.985

Abstract

In recent years, data processing has become an issue across all disciplines. Good data processing can provide decision-making recommendations. Data processing is covered in academic data processing publications, including those in computer science. This topic has grown over the past three years, demonstrating that data processing is expanding and diversifying, and there is a great deal of interest in this area of study. Within the journal, groupings (quartiles) indicate the journal's influence on other similar studies. SCImago provides this category. There are four quartiles, with the highest quartile being 1 and the lowest being 4. There are, however, numerous differences in class quartiles, with different quartile values for the same journal in different disciplines. Therefore, a method of categorization is provided to solve this issue. Classification is a machine-learning technique that groups data based on the supplied label class. Ensemble Boosting and Bagging with Decision Tree (DT) and Gaussian Nave Bayes (GNB) were utilized in this study. Several modifications were made to the ensemble algorithm's depth and estimator settings to examine the influence of adding values on the resultant precision. In the DT algorithm, both variables are altered, whereas, in the GNB algorithm, just the estimator's value is modified. Based on the average value of the accuracy results, it is known that the best algorithm for computer science datasets is GNB Bagging, with values of 68.96%, 70.99%, and 69.05%. Second-place XGBDT has 67.75% accuracy, 67.69% precision, and 67.83 recall. The DT Bagging method placed third with 67.31 percent recall, 68.13 percent precision, and 67.30 percent accuracy. The fourth sequence is the XGBoost GNB approach, which has an accuracy of 67.07%, a precision of 68.85%, and a recall of 67.18%. The Adaboost DT technique ranks in the fifth position with an accuracy of 63.65%, a precision of 64.21 %, and a recall of 63.63 %. Adaboost GNB is the least efficient algorithm for this dataset since it only achieves 43.19 % accuracy, 48.14 % precision, and 43.2% recall. The results are still quite far from the ideal. Hence the proposed method for journal quartile inequality issues is not advised.
An efficient activity recognition for homecare robots from multi-modal communication dataset Mohamad Yani; Yamada Nao; Chyan Zheng Siow; Kubota Naoyuki
International Journal of Advances in Intelligent Informatics Vol 9, No 1 (2023): March 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i1.903

Abstract

Human environments are designed and managed by humans for humans. Thus, adding robots to interact with humans and perform specific tasks appropriately is an essential topic in robotics research. In recent decades, object recognition, human skeletal, and face recognition frameworks have been implemented to support the tasks of robots. However, recognition of activities and interactions between humans and surrounding objects is an ongoing and more challenging problem. Therefore, this study proposed a graph neural network (GNN) approach to directly recognize human activity at home using vision and speech teaching data. Focus was given to the problem of classifying three activities, namely, eating, working, and reading, where these activities were conducted in the same environment. From the experiments, observations, and analyses, this proved to be quite a challenging problem to solve using only traditional convolutional neural networks (CNN) and video datasets. In the proposed method, an activity classification was learned from a 3D detected object corresponding to the human position. Next, human utterances were used to label the activity from the collected human and object 3D positions. The experiment, involving data collection and learning, was demonstrated by using human-robot communication. It was shown that the proposed method had the shortest training time of 100.346 seconds with 6000 positions from the dataset and was able to recognize the three activities more accurately than the deep layer aggregation (DLA) and X3D networks with video datasets.
Extending adamic adar for cold-start problem in link prediction based on network metrics Herman Yuliansyah; Zulaiha Ali Othman; Azuraliza Abu Bakar
International Journal of Advances in Intelligent Informatics Vol 8, No 3 (2022): November 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v8i3.882

Abstract

The cold-start problem is a condition for a new node to join a network with no available information or an isolated node. Most studies use topological network information with the Triadic Closure principles to predict links in future networks. However, the method based on the Triadic Closure principles cannot predict the future link due to no common neighbors between the predicted node pairs. Adamic Adar is one of the methods based on the Triadic Closure principles. This paper proposes three methods for extending Adamic Adar based on network metrics. The main objective is to utilize the network metrics to attract the isolated node or new node to make new relationships in the future network. The proposed method is called the extended Adamic Adar index based on Degree Centrality (DCAA), Closeness Centrality (CloCAA), and Clustering Coefficient (CluCAA). Experiments were conducted by sampling 10% of the dataset as testing data. The proposed method is examined using the four real-world networks by comparing the AUC score. Finally, the experiment results show that the DCAA and CloCAA can predict up to 99% of node pairs with a cold-start problem. DCAA and CloCAA outperform the benchmark, with an AUC score of up to 0,960. This finding shows that the extended Adamic Adar index can overcome prediction failures on node pairs with cold-start problems. In addition, prediction performance is also improved compared to the original Adamic Adar. The experiment results are promising for future research due to successfully improving the prediction performance and overcoming the cold-start problem.
Multi-granularity active learning based on the three-way decision Wu Xiaogang; Thitipong Thitipong
International Journal of Advances in Intelligent Informatics Vol 9, No 2 (2023): July 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i2.1036

Abstract

The reliance on data and the high cost of data labelling are the main problems facing deep learning today. Active learning aims to make the best model with as few training samples as possible. Previous query strategies for active learning have mainly used the uncertainty and diversity criteria, and have not considered the data distribution's multi-granularity. To extract more valid information from the samples, we use three-way decisions to select uncertain samples and propose a multi-granularity active learning method (MGAL). The model divides the unlabeled samples into three parts: positive, negative and boundary region. Through active iterative training samples, the decision delay of the boundary domain can reduce the decision cost. We validated the model on five UCI datasets and the CIFAR10 dataset. The experimental results show that the cost of three-way decisions is lower than that of two-way decisions. The multi-granularity active learning achieves good classification results, which validates the model. In this case study, the reader can learn about the ideas and methods of the three-way decision theory applied to deep learning.
Lightweight pyramid residual features with attention for person re-identification Reza Fuad Rachmadi; I Ketut Eddy Purnama; Supeno Mardi Susiki Nugroho
International Journal of Advances in Intelligent Informatics Vol 9, No 1 (2023): March 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i1.702

Abstract

Person re-identification is one of the problems in the computer vision field that aims to retrieve similar human images in some image collections (or galleries). It is very useful for people searching or tracking in a closed environment (like a mall or building). One of the highlighted things on person re-identification problems is that the model is usually designed only for performance instead of performance and computing power consideration, which is applicable for devices with limited computing power. In this paper, we proposed a lightweight residual network with pyramid attention for person re-identification problems. The lightweight residual network adopted from the residual network (ResNet) model used for CIFAR dataset experiments consists of not more than two million parameters. An additional pyramid features extraction network and attention module are added to the network to improve the classifier's performance. We use CPFE (Context-aware Pyramid Features Extraction) network that utilizes atrous convolution with different dilation rates to extract the pyramid features. In addition, two different attention networks are used for the classifier: channel-wise and spatial-based attention networks. The proposed classifier is tested using widely use Market-1501 and DukeMTMC-reID person re-identification datasets. Experiments on Market-1501 and DukeMTMC-reID datasets show that our proposed classifier can perform well and outperform the classifier without CPFE and attention networks. Further investigation and ablation study shows that our proposed classifier has higher information density compared with other person re-identification methods.
Aspect-based sentiment analysis for hotel reviews using an improved model of long short-term memory Rahmat Jayanto; Retno Kusumaningrum; Adi Wibowo
International Journal of Advances in Intelligent Informatics Vol 8, No 3 (2022): November 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v8i3.691

Abstract

Advances in information technology have given rise to online hotel reservation options. The user review feature is an important factor during the online booking of hotels. Generally, most online hotel booking service providers provide review and rating features for assessing hotels. However, not all service providers provide rating features or recap reviews for every aspect of the hotel services offered. Therefore, we propose a method to summarise reviews based on multiple aspects, including food, room, service, and location. This method uses long short-term memory (LSTM), together with hidden layers and automation of the optimal number of hidden neurons. The F1-measure value of 75.28% for the best model was based on the fact that (i) the size of the first hidden layer is 1,200 neurons with the tanh activation function, and (ii) the size of the second hidden layer is 600 neurons with the ReLU activation function. The proposed model outperforms the baseline model (also known as standard LSTM) by 10.16%. It is anticipated that the model developed through this study can be accessed by users of online hotel booking services to acquire a review recap on more specific aspects of services offered by hotels
Tumor-Net: convolutional neural network modeling for classifying brain tumors from MRI images Abu Kowshir Bitto; Md. Hasan Imam Bijoy; Sabina Yesmin; Imran Mahmud; Md. Jueal Mia; Khalid Been Badruzzaman Biplob
International Journal of Advances in Intelligent Informatics Vol 9, No 2 (2023): July 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i2.872

Abstract

Abnormal brain tissue or cell growth is known as a brain tumor. One of the body's most intricate organs is the brain, where billions of cells work together. As a head tumor grows, the brain suffers damage due to its increasingly dense core. Magnetic resonance imaging, or MRI, is a type of medical imaging that enables radiologists to view the inside of body structures without the need for surgery. The image-based medical diagnosis expert system is crucial for a brain tumor patient. In this study, we combined two Magnetic Resonance Imaging (MRI)-based image datasets from Figshare and Kaggle to identify brain tumor MRI using a variety of convolutional neural network designs. To achieve competitive performance, we employ several data preprocessing techniques, such as resizing and enhancing contrast. The image augmentation techniques (E.g., rotated, width shifted, height shifted, shear shifted, and horizontally flipped) are used to increase data size, and five pre-trained models employed, including VGG-16, VGG-19, ResNet-50, Xception, and Inception-V3. The model with the highest accuracy, ResNet-50, performs at 96.76 percent. The model with the highest precision overall is Inception V3, with a precision score of 98.83 percent. ResNet-50 performs at 96.96% for F1-Score. The prominent accuracy of the implemented model, i.e., ResNet-50, compared with several earlier studies to validate the consequence of this introspection. The outcome of this study can be used in the medical diagnosis of brain tumors with an MRI-based expert system.
Improving convolutional neural network based on hyperparameter optimization using variable length genetic algorithm for english digit handwritten recognition Muhammad Munsarif; Edi Noersasongko; Pulung Nurtantio Andono; Mochammad Arief Soeleman
International Journal of Advances in Intelligent Informatics Vol 9, No 1 (2023): March 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i1.881

Abstract

Convolutional Neural Networks (CNNs) perform well compared to other deep learning models in image recognition, especially in handwritten alphabetic numeral datasets. CNN's challenging task is to find an architecture with the right hyperparameters. Usually, this activity is done by trial and error. A genetic algorithm (GA) has been widely used for automatic hyperparameter optimization. However, the original GA with fixed chromosome length allows for suboptimal solution results because CNN has a variable number of hyperparameters depending on the depth of the model. Previous work proposed variable chromosome lengths to overcome the drawbacks of native GA. This paper proposes a variable length GA by adding global hyperparameters, namely optimizer and learning speed, to systematically and automatically tune CNN hyperparameters to improve performance. We optimize seven hyperparameters, such as the learning rate. Optimizer, kernel, filter, activation function, number of layers and pooling. The experimental results show that a population of 25 produces the best fitness value and average fitness. In addition, the comparison results show that the proposed model is superior to the basic model based on accuracy. The experimental results show that the proposed model is about 99.18% higher than the baseline model.
Deep reinforcement learning autoencoder with RA-GAN and GAN Hoang-Sy Nguyen; Cong-Danh Huynh
International Journal of Advances in Intelligent Informatics Vol 8, No 3 (2022): November 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v8i3.896

Abstract

Deep learning utilization to optimize block-structured communication systems has attracted tremendous attention from researchers. Nevertheless, owing to the extensive data transmission between the transmitter and the receiver, communication, in this case, is hard to establish and maintain effectively. As a solution for this, we first investigate typical end-to-end learning for a communication system, Generative Adversarial Network (GAN). Then, two problems associated with GAN-based systems, the gradient vanishing and overfitting, are reviewed. Subsequently, a residual aided GAN (RA-GAN) is proposed as means to overcome these problems. In the proposed learning scheme, the residual learning and the regularization method are used to mitigate the gradient vanishing and over-fitting problems. In the proposed learning scheme, the residual learning and the regularization method are used to mitigate the gradient vanishing and over-fitting problems. Finally, the numerical results performed in MATLAB for simulation and Codelabs for training have proven that the RA-GAN scheme has near-optimal performance and outperforms the conventional GAN scheme. Throughout this case study, readers can understand the issues that would occur when deep learning is applied to a communication system and possible approaches to address them.

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