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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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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.
IDSX-Attention: Intrusion detection system (IDS) based hybrid MADE-SDAE and LSTM-Attention mechanism Hanafi Hanafi; Andri Pranolo; Yingchi Mao; Taqwa Hariguna; Leonel Hernandez; Nanang Fitriana Kurniawan
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.v9i1.942

Abstract

An Intrusion Detection System (IDS) is essential for automatically monitoring cyber-attack activity. Adopting machine learning to develop automatic cyber attack detection has become an important research topic in the last decade. Deep learning is a popular machine learning algorithm recently applied in IDS applications. The adoption of complex layer algorithms in the term of deep learning has been applied in the last five years to increase IDS detection effectiveness. Unfortunately, most deep learning models generate a large number of false negatives, leading to dominant mistake detection that can affect the performance of IDS applications. This paper aims to integrate a statistical model to remove outliers in pre-processing, SDAE, responsible for reducing data dimensionality, and LSTM-Attention, responsible for producing attack classification tasks. The model was implemented into the NSL-KDD dataset and evaluated using Accuracy, F1, Recall, and Confusion metrics measures. The results showed that the proposed IDSX-Attention outperformed the baseline model, SDAE, LSTM, PCA-LSTM, and Mutual Information (MI)-LSTM, achieving more than a 2% improvement on average. This study demonstrates the potential of the proposed IDSX-Attention, particularly as a deep learning approach, in enhancing the effectiveness of IDS and addressing the challenges in cyber threat detection. It highlights the importance of integrating statistical models, deep learning, and dimensionality reduction mechanisms to improve IDS detection. Further research can explore the integration of other deep learning algorithms and datasets to validate the proposed model's effectiveness and improve the performance of IDS.
Exploration of hybrid deep learning algorithms for covid-19 mrna vaccine degradation prediction system Soon Hwai Ing; Azian Azamimi Abdullah; Mohd Yusoff Mashor; Zeti-Azura Mohamed-Hussein; Zeehaida Mohamed; Wei Chern Ang
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.v8i3.950

Abstract

Coronavirus causes a global pandemic that has adversely affected public health, the economy, including every life aspect. To manage the spread, innumerable measurements are gathered. Administering vaccines is considered to be among the precautionary steps under the blueprint. Among all vaccines, the messenger ribonucleic acid (mRNA) vaccines provide notable effectiveness with minimal side effects. However, it is easily degraded and limits its application. Therefore, considering the cruciality of predicting the degradation rate of the mRNA vaccine, this prediction study is proposed. In addition, this study compared the hybridizing sequence of the hybrid model to identify its influence on prediction performance. Five models are created for exploration and prediction on the COVID-19 mRNA vaccine dataset provided by Stanford University and made accessible on the Kaggle community platform employing the two deep learning algorithms, Long Short-Term Memory (LSTM) as well as Gated Recurrent Unit (GRU). The Mean Columnwise Root Mean Square Error (MCRMSE) performance metric was utilized to assess each model’s performance. Results demonstrated that both GRU and LSTM are befitting for predicting the degradation rate of COVID-19 mRNA vaccines. Moreover, performance improvement could be achieved by performing the hybridization approach. Among Hybrid_1, Hybrid_2, and Hybrid_3, when trained with Set_1 augmented data, Hybrid_3 with the lowest training error (0.1257) and validation error (0.1324) surpassed the other two models; the same for model training with Set_2 augmented data, scoring 0.0164 and 0.0175 MCRMSE for training error and validation error, respectively. The variance in results obtained by hybrid models from experimenting claimed hybridizing sequence of algorithms in hybrid modeling should be concerned.
Automatic plant recognition using convolutional neural network on malaysian medicinal herbs: the value of data augmentation Noor Aini Mohd Roslan; Norizan Mat Diah; Zaidah Ibrahim; Yuda Munarko; Agus Eko Minarno
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.v9i1.1076

Abstract

Herbs are an important nutritional source for humans since they provide a variety of nutrients. Indigenous people have employed herbs, in particular, as traditional medicines since ancient times. Malaysia has hundreds of plant species; herb detection may be difficult due to the variety of herb species and their shape and color similarities. Furthermore, there is a scarcity of support datasets for detecting these plants. The main objective of this paper is to investigate the performance of convolutional neural network (CNN) on Malaysian medicinal herbs datasets, real data and augmented data. Malaysian medical herbs data were obtained from Taman Herba Pulau Pinang, Malaysia, and ten kinds of native herbs were chosen. Both datasets were evaluated using the CNN model developed throughout the research. Overall, herbs real data obtained an average accuracy of 75%, whereas herbs augmented data achieved an average accuracy of 88%. Based on these findings, herbs augmented data surpassed herbs actual data in terms of accuracy after undergoing the augmentation technique.
Who danced better? ranked tiktok dance video dataset and pairwise action quality assessment method Irwandi Hipiny; Hamimah Ujir; Aidil Azli Alias; Musdi Shanat; Mohamad Khairi Ishak
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.v9i1.919

Abstract

Video-based action quality assessment (AQA) is a non-trivial task due to the subtle visual differences between data produced by experts and non-experts. Current methods are extended from the action recognition domain where most are based on temporal pattern matching. AQA has additional requirements where order and tempo matter for rating the quality of an action. We present a novel dataset of ranked TikTok dance videos, and a pairwise AQA method for predicting which video of a same-label pair was sourced from the better dancer. Exhaustive pairings of same-label videos were randomly assigned to 100 human annotators, ultimately producing a ranked list per label category. Our method relies on a successful detection of the subject’s 2D pose inside successive query frames where the order and tempo of actions are encoded inside a produced String sequence. The detected 2D pose returns a top-matching Visual word from a Codebook to represent the current frame. Given a same-label pair, we generate a String value of concatenated Visual words for each video. By computing the edit distance score between each String value and the Gold Standard’s (i.e., the top-ranked video(s) for that label category), we declare the video with the lower score as the winner. The pairwise AQA method is implemented using two schemes, i.e., with and without text compression. Although the average precision for both schemes over 12 label categories is low, at 0.45 with text compression and 0.48 without, precision values for several label categories are comparable to past methods’ (median: 0.47, max: 0.66).
A new approach for sensitivity improvement of retinal blood vessel segmentation in high-resolution fundus images based on phase stretch transform Kartika Firdausy; Oyas Wahyunggoro; Hanung Adi Nugroho; Muhammad Bayu Sasongko
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.v8i3.914

Abstract

The eye-fundus photograph is widely used for eye examinations. Accurate identification of retinal blood vessels could reveal information that is helpful for clinical diagnoses of many health disorders. Although several methods have been proposed to segment images of retinal blood vessels, the sensitivity of these methods is plausible to be improved. The algorithm’s sensitivity refers to the algorithm’s ability to identify retinal vessel pixels correctly. Furthermore, the resolution and quality of retinal images are improving rapidly. Consequently, new segmentation methods are in demand to overcome issues from high-resolution images. This study presented improved performance of retinal vessel segmentation using a novel edge detection scheme based on the phase stretch transform (PST) function as its kernel. Before applying the edge detection stage, the input retinal images were pre-processed. During the pre-processing step, non-local means filtering on the green channel image, followed by contrast limited adaptive histogram equalization (CLAHE) and median filtering, were applied to enhance the retinal image. After applying the edge detection stage, the post-processing steps, including the CLAHE, median filtering, thresholding, morphological opening, and closing, were implemented to obtain the segmented image. The proposed method was evaluated using images from the high-resolution fundus (HRF) public database and yielded promising results for sensitivity improvement of retinal blood vessel detection. The proposed approach contributes to a better segmentation performance with an average sensitivity of 0.813, representing a clear improvement over several benchmark techniques
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.
Broccoli leaf diseases classification using support vector machine with particle swarm optimization based on feature selection Yulio Ferdinand; Wikky Fawwaz Al Maki
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.v8i3.951

Abstract

Broccoli is a plant that has many benefits. The flower parts of broccoli contain protein, calcium, vitamin A, vitamin C, and many more. However, in its cultivation, broccoli plants have obstacles such as the presence of pests and diseases that can affect production of broccoli. To avoid this, the authors build a model to identify diseases in broccoli through leaf images with a size of 128x128 pixels. The model is constructed to classify healthy leaves, and disease leaves using the image processing method that uses machine learning stages. There are several stages, including K-Means segmentation, colour feature extraction, and classification using SVM (Support Vector Machine) with RBF kernel and PSO (Particle Swarm Optimization) for reduce dimensionality data. The model that has been built compares the SVM model and the SVM-PSO model. It produces good accuracy in the training of 97.63% and testing accuracy of 94.48% for SVM-PSO and 85.82% for training, and 86.25% for testing in the SVM model. Therefore, this proposed model can produce good results in categorizing healthy and diseased leaves in broccoli.
Online social network user performance prediction by graph neural networks Fail Gafarov; Andrey Berdnikov; Pavel Ustin
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.v8i3.859

Abstract

Online social networks provide rich information that characterizes the user’s personality, his interests, hobbies, and reflects his current state. Users of social networks publish photos, posts, videos, audio, etc. every day. Online social networks (OSN) open up a wide range of research opportunities for scientists. Much research conducted in recent years using graph neural networks (GNN) has shown their advantages over conventional deep learning. In particular, the use of graph neural networks for online social network analysis seems to be the most suitable. In this article we studied the use of graph convolutional neural networks with different convolution layers (GCNConv, SAGEConv, GraphConv, GATConv, TransformerConv, GINConv) for predicting the user’s professional success in VKontakte online social network, based on data obtained from his profiles. We have used various parameters obtained from users’ personal pages in VKontakte social network (the number of friends, subscribers, interesting pages, etc.) as their features for determining the professional success, as well as networks (graphs) reflecting connections between users (followers/ friends). In this work we performed graph classification by using graph convolutional neural networks (with different types of convolution layers). The best accuracy of the graph convolutional neural network (0.88) was achieved by using the graph isomorphism network (GIN) layer. The results, obtained in this work, will serve for further studies of social success, based on metrics of personal profiles of OSN users and social graphs using neural network methods.
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.

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