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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 65 Documents
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Search results for , issue "Vol 36, No 3: December 2024" : 65 Documents clear
Leveraging transformer models for enhanced temperature forecasting: a comparative analysis in the Beni Mellal region Jdi, Hamza; Falih, Noureddine
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1694-1700

Abstract

The remarkable impact of transformers in artificial intelligence, exemplified by applications like GPT-3 in language processing, has sparked interest in their potential for time series analysis. This study aims to explore whether transformers, specifically temporal fusion transformers (TFT), can outperform conventional methods in this domain. The research question is whether TFT exhibits superior performance compared to conventional recurrent neural network (RNN) methods, specifically gated recurrent unit (GRU), and traditional machine learning approaches, notably autoregressive integrated moving average (ARIMA), in the context of time series analysis and temperature prediction. A comparative analysis is conducted among three models: ARIMA, GRU, and TFT. The study utilizes time series data spanning from 1984 to the end of 2022. The models’ performances are evaluated using multiple metrics: mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and the coefficient of determination (R2). The TFT model achieves the lowest MAE, indicating high accuracy in its predictions. It outperforms both the RNN and traditional machine learning in temperature prediction tasks. Integrating the TFT model with the FAO penman-monteith method could improve irrigation scheduling due to more accurate temperature predictions, potentially enhancing water efficiency and crop yields.
Hesitant fuzzy clustering with convolutional spiking neural network for movie recommendations Shrivastava, Vineet; Kumar, Suresh
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1849-1856

Abstract

The movie recommender system is one of the most influential and practical tools for aiding individuals in quickly selecting films to watch. Despite numerous academic efforts to employ recommender systems for various purposes, such as movie-watching and book-buying, many studies have overlooked user-specific movie recommendations. This paper introduces a novel approach for movie recommendations that combines the hesitant fuzzy clustering with a convolutional spiking neural network movie recommender system. The initial step involves acquiring input data from benchmark datasets like MovieLens 100K and MovieLens 1M. Further, content-based features are extracted from the dataset using ternary pattern and discrete wavelet transforms. After that hesitant fuzzy linguistic Bi-objective clustering (HFLBC) is applied for cluster selection based on the extracted features. Subsequently, a movie recommender scheme utilizing a convolutional spiking neural network is introduced to predict user film preferences. The efficiency of the proposed model is compared to existing methods such as multi-modal trust-dependent recommender scheme and graph-dependent hybrid recommendation scheme. The results show a significant improvement, with the proposed model achieving 13.79% and 16.47% higher accuracy than the existing methods. The findings highlight the potential of proposed system in enhancing the accuracy and personalization of movie recommendations.
Developed improved lion optimization for breast cancer classification using histopathology images Ali Khan, Pattan M. D.; Rathina, Xavier Arputha
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1613-1619

Abstract

Breast cancer, a prevalent kind of cancer, is a major health problem among women. Researchers recently achieved categorization effectiveness of breast cancer (BC) detection in histopathology picture database using convolutional neural networks (CNNs) of medical image processing. Although CNN method parameter settings were complex, employing breast cancer histopathological database (BCHD) data for categorization was valued as expensive. This research used uniform experimental design (UED) to solve these issues and improved lion optimization (ILO) breast cancer histopathology image categorization. To optimize the variables at UED-ILO, a regression method was employed. According to the experimental data, the proposed approach of UED-ILO (uniform experimental design based improved lion optimization) variable optimization provided a categorization accuracy rate of 84.41%. Finally, the proposed approach can effectively increase classification accuracy, with results that outperform others of an equivalent nature.
Improve fractal interpolation function with Sierspinski triangle Susanti, Eka; Puspita, Fitri Maya; Supadi, Siti Suzlin; Yuliza, Evi; Fadhila Chaniago, Redina An
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1485-1492

Abstract

Interpolation techniques can be used to determine the approximate value of a parameter if it is known that two values are bound to a certain interval. Interpolation can be done numerically or fractal. The fractal interpolation value is influenced by the vertical scale factor and the fractal interpolation function (FIF). This research introduces fractal interpolation technique with FIF which is constructed from Sierspinski triangles. As an example of application, the interpolation technique is applied to determine the approximate value of the rice demand parameter in the inventory model. The accuracy of the interpolation results is determined using the mean absolute percentage error (MAPE). The number of triangles obtained and the interpolation values for each successive iteration are 3???? and 3????+1. MAPE values from 6 to 9 iteration were 24.603%, 24.603%, 23.858%, 23.772% respectively. There is a decrease in the value of MAPE, this indicates an increase in the value of the accuracy of the interpolation results. It can be concluded that the MAPE value is also influenced by the number of iterations of the interpolation technique.
Development of image extraction using the centerline method in the identification of appendicitis in ultrasonography Rizki, Syafrika Deni; Yuhandri, Yuhandri; Fitri, Iskandar
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1750-1758

Abstract

Appendicitis is a disease that refers to inflammation of the appendix caused by obstruction, or blockage, in the lumen of the appendix. We investigated that this disease can be detected early through medical imaging such as ultrasonography (USG). However, the role of ultrasound in these cases is still limited due to the low visualization rate of the visible appendix. Based on this, this research aims to develop an image extraction process using the Centerline method in the process of identifying appendicitis in ultrasound images. The development of the extraction process is presented in the performance of the centerline and boundary extraction (CBE) algorithm which can represent image objects as boundaries that limit and separate one area from other areas. The research dataset used was 2097 ultrasound images sourced from 90 patients at the West Sumatra Lung Hospital. Based on the tests that have been carried out, it has been proven that it can reduce the width of the image object iteratively until the object is represented as a center line or the thinnest representation. The performance of the CBE algorithm in the identification process is sufficient to provide accuracy results of 92%. These results can be a new extraction concept that can provide accuracy in the identification process.
Plant disease classification using novel integration of deep learning CNN and graph convolutional networks Maheswara Rao, Saka Uma; Sreekala, Keshetti; Rao, Pulluri Srinivas; Shirisha, Nalla; Srinivas, Gunnam; Sreedevi, Erry
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1721-1730

Abstract

Plant diseases present substantial challenges to global agriculture, significantly affecting crop yields and jeopardizing food security. Accurate and timely detection of these diseases is paramount for mitigating their adverse effects. This paper proposes a novel approach for plant disease classification by integrating convolutional neural networks (CNNs) and graph convolutional networks (GCNs). The model aims to enhance classification accuracy by leveraging both visual features extracted by CNNs and relational information captured by GCNs. Using a Kaggle dataset containing images of diseased and healthy plant leaves from 31 classes, including apple, corn, grape, peach, pepper bell, potato, strawberry, and tomato. Standalone CNN models were trained on image data from each plant type, while standalone GCN models utilized graph-structured data representing plant relationships within each subset. The proposed integrated CNN-GCN model capitalizes on the complementary strengths of CNNs and GCNs to achieve improved classification performance. Through rigorous experimentation and comparative analysis, the effectiveness of the integrated CNN-GCN approach was evaluated alongside standalone CNN and GCN models across all plant types. Results demonstrated the superiority of the integrated model, highlighting its potential for enhancing plant disease classification accuracy.
No-reference image quality assessment based on visual explanation images and deep transfer learning Ahmed, Basma; Omer, Osama A.; Singh, Vivek Kumar; Rashed, Amal; Abdel-Nasser, Mohamed
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1521-1531

Abstract

Quantifying image quality in the absence of a reference image continues to be a challenge despite the introduction of numerous no-reference image quality assessments (NR-IQA) in recent years. Unlike most existing NRIQA methods, this paper proposes an efficient NR-IQA method based on deep visual interpretations. Specifically, the main components of the proposed method are: i) generating a pseudo-reference image (PRI) for the input distorted images, ii) employing a pretrained convolutional network to extract feature maps from the distorted image and the corresponding PRI, iii) producing visual explanation images (VEIs) by using the feature maps of the distorted image and the corresponding PRI, iv) measuring the similarity between the two VEIs using an image similarity metric, and v) employing a non-linear mapping function for quality score alignment. In our experiments, we evaluated the efficacy of the proposed method across various forms of distortion using four benchmark datasets (LIVE, SIQAD, CSIQ, and TID2013). The proposed approach demonstrates parity with the latest methods, as evidenced by comparisons with both hand-crafted NR-IQA and deep learning-based approaches.
Risk disclosure and financial performance of Islamic banks in Jordan: the moderating role of financial technology Almomani, Mohammed Abd-Akarim; Al-Momani, Adai
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1711-1720

Abstract

Risk disclosure (RD) is important to inform investors. However, few studies examined this variable in developing countries and in Islamic bank context. This research investigates how RD affect financial performance (FP) of Islamic banks in Jordan. It also examines the moderating role of financial technology (FinTech). We use a quantitative method to examine how mandatory risk disclosure (MRD) and voluntary risk disclosure (VRD) impact return on assets (ROA) and return on equity (ROE) in Islamic banks operating in Jordan. Our results show that both MRD and VRD have a significant effect on FP of Islamic banks. Moreover, FinTech acts as a moderator in the connection between risk disclosure (MRD and VRD) and FP performance. The effect was compared before and after coronaviruses disease 2019 (COVID-19) and it shows that the COVID-19 has increased the effect of MRD and VRD on FP of Islamic banks. More focus on VRD and MRD will enhance the FP of Islamic banks in Jordan.
Hybrid feature selection of microarray prostate cancer diagnostic system Ali, Nursabillilah Mohd; Hanafi, Ainain Nur; Karis, Mohd Safirin; Shamsudin, Nur Hazahsha; Shair, Ezreen Farina; Abdul Aziz, Nor Hidayati
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1884-1894

Abstract

DNA microarray prostate cancer diagnosis systems are widely used, and hybrid feature selection methods are applied to select optimal features to address the high dimensionality of the dataset. This work proposes a new hybrid feature selection method, namely the relief-F (RF)-genetic algorithm (GA) with support vector machine (SVM) classification method. The aim is to evaluate the performance of the proposed method in terms of accuracy, computation time, and the number of selected features. The method is implemented using Python in PyCharm and is evaluated on a DNA microarray prostate cancer. The outcome of this work is a performance comparison table for the proposed methods on the dataset. The performance of GA, particle swarm optimization (PSO), and whale optimization algorithm (WOA) is compared in terms of accuracy, computation time, and the number of selected features. Results show that GA has the highest average accuracy (91.17%) compared to PSO (90.52%) and WOA (85.74%). GA outperforms PSO and WOA due to its superior convergence properties and better alignment with complex problems.
Artificial intelligence detection of refractive eye diseases using certainty factor and image processing Rachman, Rizal; Susanti, Sari; Suhendi, Hendi; Satyanegara, Adi Karawinata
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1787-1797

Abstract

Refractive errors are defined as an impairment in the eye’s capacity to focus light, resulting in the formation of blurred or unfocused images. These issues arise from alterations in the shape of the cornea, the length of the eyeball, or the aging of the crystalline lens. It is anticipated that the prevalence of visual impairment will increase in conjunction with global population growth. At present, a significant number of countries have not yet accorded sufficient priority to eye health within their healthcare systems. This has resulted in insufficient awareness and reluctance to seek costly specialized care. This study proposes the development of an advanced refractive eye disease detection system with the objective of improving diagnostic accuracy, disseminating disease information, and reducing financial barriers to specialist consultation. The research employs certainty factor (CF) methods and image processing with feature extraction. The initial results demonstrate the potential for identifying specific refractive eye diseases with high certainty through the analysis of symptoms and the examination of photographs of the eye. The proposed approach provides an alternative method for diagnosing refractive eye diseases, which could enhance access to refractive eye care services and reduce the economic burden on patients.

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