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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 13 Documents
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Search results for , issue "Vol 9, No 2 (2023): July 2023" : 13 Documents clear
Hand–object interaction recognition based on visual attention using multiscopic cyber-physical-social system Adnan Rachmat Anom Besari; Azhar Aulia Saputra; Wei Hong Chin; Kurnianingsih Kurnianingsih; Naoyuki Kubota
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.v9i2.901

Abstract

Computer vision-based cyber-physical-social systems (CPSS) are predicted to be the future of independent hand rehabilitation. However, there is a link between hand function and cognition in the elderly that this technology has not adequately supported. To investigate this issue, this paper proposes a multiscopic CPSS framework by developing hand–object interaction (HOI) based on visual attention. First, we use egocentric vision to extract features from hand posture at the microscopic level. With 94.87% testing accuracy, we use three layers of graph neural network (GNN) based on hand skeletal features to categorize 16 grasp postures. Second, we use a mesoscopic active perception ability to validate the HOI with eye tracking in the task-specific reach-to-grasp cycle. With 90.75% testing accuracy, the distance between the fingertips and the center of an object is used as input to a multi-layer gated recurrent unit based on recurrent neural network architecture. Third, we incorporate visual attention into the cognitive ability for classifying multiple objects at the macroscopic level. In two scenarios with four activities, we use GNN with three convolutional layers to categorize some objects. The outcome demonstrates that the system can successfully separate objects based on related activities. Further research and development are expected to support the CPSS application in independent rehabilitation.
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.
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.
Multi-step CNN forecasting for COVID-19 multivariate time-series Haviluddin Haviluddin; Rayner Alfred
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.v9i2.1080

Abstract

The new coronavirus (COVID-19) has spread to over 200 countries, with over 36 million confirmed cases as of October 10, 2020. As a result, numerous machine learning models capable of forecasting the epidemic worldwide have been produced. This paper reviews and summarizes the most relevant machine learning forecasting models for COVID-19. The dataset is derived from the world health organization (WHO) COVID-19 dashboard, and it contains official daily counts of COVID-19 cases, fatalities, and vaccination use reported by countries, territories, and regions. We propose various convolutional neural network (CNN) based models such as CNN, single exponential smoothing CNN (S-CNN), moving average CNN (MA-CNN), smoothed moving average CNN (SMA-CNN), and moving average smoothed CNN (MAS-CNN). Here, MAPE and MSE are used to assess the suggested models. MAPE is frequently used to compare accuracy across time series with different scales. MSE, the model must strive for a total forecast equal to the entire demand. That is, optimizing MSE seeks to create a forecast that is right on average and so unbiased. The final result shows that SMA-CNN outperformed its baselines in both MAPE and MSE. The main contribution of this novel forecasting approach is a more accurate result as a base of the strategy of preventing COVID-19 spreads.
Image contrast enhancement for preserving entropy and image visual features Bilal Bataineh
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.v9i2.907

Abstract

Histogram equalization is essential for low-contrast enhancement in image processing. Several methods have been proposed; however, one of the most critical problems encountered by existing methods is their ability to preserve information in the enhanced image as the original. This research proposes an image enhancement method based on a histogram equalization approach that preserves the entropy and fine details similar to those of the original image. This is achieved through proposed probability density functions (PDFs) that preserve the small gray values of the usual PDF. The method consists of several steps. First, occurrences and clipped histograms are extracted according to the proposed thresholding. Then, they are equalized and used by a proposed transferring function to calculate the new pixel values in the enhanced image. The proposed method is compared with widely used methods such as Clahe, CS, HE, and GTSHE. Experiments using benchmark datasets and entropy, contrast, PSNR, and SSIM measurements are conducted to evaluate the performance. The results show that the proposed method is the only one that preserves the entropy of the enhanced image of the original image. In addition, it is efficient and reliable in enhancing image quality. This method preserves fine details and improves image quality, supporting computer vision and pattern recognition fields.
Automatic note generator for Javanese gamelan music accompaniment using deep learning Arik Kurniawati; Eko Mulyanto Yuniarno; Yoyon Kusnendar Suprapto; Aditya Nur Ikhsan Soewidiatmaka
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.v9i2.1031

Abstract

Javanese gamelan is a traditional form of music from Indonesia with a variety of styles and patterns. One of these patterns is the harmony music of the Bonang Barung and Bonang Penerus instruments. When playing gamelan, the resulting patterns can vary based on the music’s rhythm or dynamics, which can be challenging for novice players unfamiliar with the gamelan rules and notation system, which only provides melodic notes. Unlike in modern music, where harmony notes are often the same for all instruments, harmony music in Javanese gamelan is vital in establishing the character of a song. With technological advancements, musical composition can be generated automatically without human participation, which has become a trend in music generation research. This study proposes a method to generate musical accompaniment notes for harmony music using a bidirectional long-term memory (BiLSTM) network and compares it with recurrent neural network (RNN) and long-term memory (LSTM) models that use numerical notation to represent musical data, making it easier to learn the variations of harmony music in Javanese gamelan. This method replaces the gamelan composer in completing the notation for all the instruments in a song. To evaluate the generated harmonic music, note distance, dynamic time warping (DTW), and cross-correlation techniques were used to measure the distance between the system-generated results and the gamelan composer's creations. In addition, audio features were extracted and used to visualize the audio. The experimental results show that all models produced better accuracy results when using all features of the song, reaching a value of around 90%, compared to using only 2 features (rhythm and note of melody), which reached 65-70%. Furthermore, the BiLSTM model produced musical harmonies that were more similar to the original music (+93%) than those generated by the LSTM (+92%) and RNN (+90%). This study can be applied to performing Javanese gamelan music.
Pneumonia Detection on X-Ray Imaging using Softmax Output in Multilevel Meta Ensemble Algorithm of Deep Convolutional Neural Network Transfer Learning Models Simeon Yuda Prasetyo; Ghinaa Zain Nabiilah; Zahra Nabila Izdihar; Sani Muhamad Isa
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.v9i2.884

Abstract

Pneumonia is the leading cause of death from a single infection worldwide in children. A proven clinical method for diagnosing pneumonia is through a chest X-ray. However, the resulting X-ray images often need clarification, resulting in subjective judgments. In addition, the process of diagnosis requires a longer time. One technique can be applied by applying advanced deep learning, namely, Transfer Learning with Deep Convolutional Neural Network (Deep CNN) and modified Multilevel Meta Ensemble Learning using Softmax. The purpose of this research was to improve the accuracy of the pneumonia classification model. This study proposes a classification model with a meta-ensemble approach using five classification algorithms: Xception, Resnet 15V2, InceptionV3, VGG16, and VGG19. The ensemble stage used two different concepts, where the first level ensemble combined the output of the Xception, ResNet15V2, and InceptionV3 algorithms. Then the output from the first ensemble level is reused for the following learning process, combined with the output from other algorithms, namely VGG16 and VGG19. This process is called ensemble level two. The classification algorithm used at this stage is the same as the previous stage, using KNN as a classification model. Based on experiments, the model proposed in this study has better accuracy than the others, with a test accuracy value of 98.272%. The benefit of this research could help doctors as a recommendation tool to make more accurate and timely diagnoses, thus speeding up the treatment process and reducing the risk of complications.
Understanding requirements dependency in requirements prioritization: a systematic literature review Fiftin Noviyanto; Rozilawati Razali; Mohd Zakree Ahmad Nazree
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.v9i2.1082

Abstract

Requirement prioritization (RP) is a crucial task in managing requirements as it determines the order of implementation and, thus, the delivery of a software system. Improper RP may cause software project failures due to over budget and schedule as well as a low-quality product. Several factors influence RP. One of which is requirements dependency. Handling inappropriate handling of requirements dependencies can lead to software development failures. If a requirement that serves as a prerequisite for other requirements is given low priority, it affects the overall project completion time. Despite its importance, little is known about requirements dependency in RP, particularly its impacts, types, and techniques. This study, therefore, aims to understand the phenomenon by analyzing the existing literature. It addresses three objectives, namely, to investigate the impacts of requirements dependency on RP, to identify different types of requirements dependency, and to discover the techniques used for requirements dependency problems in RP. To fulfill the objectives, this study adopts the Systematic Literature Review (SLR) method. Applying the SLR protocol, this study selected forty primary articles, which comprise 58% journal papers, 32% conference proceedings, and 10% book sections. The results of data synthesis indicate that requirements dependency has significant impacts on RP, and there are a number of requirements dependency types as well as techniques for addressing requirements dependency problems in RP. This research discovered various techniques employed, including the use of Graphs for RD visualization, Machine Learning for handling large-scale RP, decision making for multi-criteria handling, and optimization techniques utilizing evolutionary algorithms. The study also reveals that the existing techniques have encountered serious limitations in terms of scalability, time consumption, interdependencies of requirements, and limited types of requirement dependencies.
Multidisciplinary classification for Indonesian scientific articles abstract using pre-trained BERT model Antonius Angga Kurniawan; Sarifuddin Madenda; Setia Wirawan; Ruddy J. Suhatril
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.v9i2.1051

Abstract

Scientific articles now have multidisciplinary content. These make it difficult for researchers to find out relevant information. Some submissions are irrelevant to the journal's discipline. Categorizing articles and assessing their relevance can aid researchers and journals. Existing research still focuses on single-category predictive outcomes. Therefore, this research takes a new approach by applying a multidisciplinary classification for Indonesian scientific article abstracts using a pre-trained BERT model, showing the relevance between each category in an abstract. The dataset used was 9,000 abstracts with 9 disciplinary categories. On the dataset, text preprocessing is performed. The classification model was built by combining the pre-trained BERT model with Artificial Neural Network. Fine-tuning the hyperparameters is done to determine the most optimal hyperparameter combination for the model. The hyperparameters consist of batch size, learning rate, number of epochs, and data ratio. The best hyperparameter combination is a learning rate of 1e-5, batch size 32, epochs 3, and data ratio 9:1, with a validation accuracy value of 90.8%. The confusion matrix results of the model are compared with the confusion matrix results by experts. In this case, the highest accuracy result obtained by the model is 99.56%. A software prototype used the most accurate model to classify new data, displaying the top two prediction probabilities and the dominant category. This research produces a model that can be used to solve Indonesian text classification-related problems.
A hybrid model for aspect-based sentiment analysis on customer feedback: research on the mobile commerce sector in Vietnam Thanh Trung Ho; Hien Minh Bui; Phung Kim Thai
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.v9i2.976

Abstract

Feedback and comments on mobile commerce applications are extremely useful and valuable information sources that reflect the quality of products or services to determine whether data is positive or negative and help businesses monitor brand and product sentiment in customers’ feedback and understand customers’ needs. However, the increasing number of comments makes it increasingly difficult to understand customers using manual methods. To solve this problem, this study builds a hybrid research model based on aspect mining and comment classification for aspect-based sentiment analysis (ABSA) to deeply comprehend the customer and their experiences. Based on previous classification results, we first construct a dictionary of positive and negative words in the e-commerce field. Then, the POS tagging technique is applied for word classification in Vietnamese to extract aspects of model commerce related to positive or negative words. The model is implemented with machine and deep learning methods on a corpus comprising more than 1,000,000 customer opinions collected from Vietnam's four largest mobile commerce applications. Experimental results show that the Bi-LSTM method has the highest accuracy with 92.01%; it is selected for the proposed model to analyze the viewpoint of words on real data. The findings are that the proposed hybrid model can be applied to monitor online customer experience in real time, enable administrators to make timely and accurate decisions, and improve the quality of products and services to take a competitive advantage.

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