cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
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 9,138 Documents
 841   842   843   844   845 
Histopathological cancer detection based on deep learning and stain images Ibrahim, Dina M.; Hammoudeh, Mohammad Ali A.; Allam, Tahani M.
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp214-230

Abstract

Colorectal cancer (CRC)-a malignant growth in the colon or rectum- is the second largest cause of cancer deaths worldwide. Early detection may increase therapy choices. Deep learning can improve early medical detection to reduce the risk of unintentional death from an incorrect clinical diagnosis. Histopathological examination of colon cancer is essential in medical research. This paper proposes a deep learning-based colon cancer detection method using stain-normalized images. We use deep learning methods to improve detection accuracy and efficiency. Our solution normalizes image stain variations and uses deep learning models for reliable classification. This research improves colon cancer histopathology analysis, which may enhance diagnosis. Our paper uses DenseNet-121, VGG-16, GoogLeNet, ResNet-50, and ResNet-18 deep learning models. We also analyze how stain normalization (SN) improves our model on histopathology images. The ResNet-50 model with SN yields the highest values (9.94%) compared to the other four models and the nine models from previous studies.
Probing the depths: assessing the efficacy of the two-tier deception-driven security model Gamilla, Anazel P.; Palaoag, Thelma D.; Naagas, Marlon A.
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.pp1631-1639

Abstract

In the age characterized by relentless cyber threats, the need for innovative and proactive security measures has never been more important. Deception is defined as the deliberate structure of tricks, traps, and false information to mislead and discourage threats, while providing timely warning signals and useful information to defenders. The two-tier deception-driven security model's implementation focuses on applying deception security techniques to deceive potential attackers and protect network resources, with an emphasis on a proactive defense approach. The study emphasized the deployment and deep testing of the model, which aims to assess its efficacy and feasibility in real-time practice. The study shows that the two-layered approach effectively defends the network within the multiple layers using a combination of decoys, honeypots, and deceptive network segments. The deception security model effectively prevents and confuses potential threats, improving the network's overall resilience and threat defense capabilities. The findings suggest that integrating deception techniques into cybersecurity frameworks can provide a robust layer of protection against evolving cyber threats. Furthermore, this research contributes to the ongoing discourse on proactive cybersecurity strategies and offers practical insights for improving network defense mechanisms.
MetaLung: Meticulous affine-transformation-based lung cancer augmentation method Nam, Diana; Panina, Alexandra; Pak, Alexandr; Hajiyev, Fuad
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp401-413

Abstract

The limitation of medical image data in open source is a big challenge for medical image processing. Medical data is closed because of confidential and ethical issues, also manual labeling of medical data is an expensive process. We propose a new augmentation method named MetaLung (Meticulous affine-transformation-based lung cancer augmentation method) for lung CT image augmentation. The key feature of the proposed method is the ability to expand the training dataset while preserving clinical and instrumental features. MetaLung shows a stable increase in image segmentation quality for three CNN-based models with different computational complexity (U-Net, DeepLabV3, and MaskRCNN). Also, the method allows in reduce the number of False Positive predictions.
Stacking-based ensemble learning for identifying artist signatures on paintings Hidayati, Shintami Chusnul; Irawan Rahardja, Agustinus Aldi; Suciati, Nanik
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.pp1683-1693

Abstract

Identifying artist signatures on paintings is essential for authenticating artworks and advancing digital humanities. An artist’s signature is a consistent element included in each painting that the artist creates, providing a unique identifier for their work. Traditional methods that rely on expert analysis and manual comparison are time-consuming and are prone to human error. Although convolutional neural networks (CNNs) have shown promise in automating this process, existing single-model approaches struggle with the diversity and complexity of artistic styles, leading to limitations in their performance and generalizability. Therefore, this study proposes an ensemble learning approach that integrates the predictive power of multiple CNN-based models. The proposed framework leverages the strengths of three state-of-the-art CNNs: EfficientNetB4, ResNet-50, and Xception. These models were independently trained, and the predictions were combined using a meta-learning strategy. To address class imbalance, data augmentation techniques and weighted loss functions were employed. The experimental results obtained on a dataset of more than 8,000 paintings from 50 artists demonstrate significant improvements over individual CNN architectures and other ensemble methods, thereby effectively capturing complex features and improving generalizability.
An optimal machine learning-based algorithm for detecting phishing attacks using URL information Devaraj, Nandeesha Hallimysore; Thimappa, Prasanna Bantiganahalli
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp631-638

Abstract

In recent years, more websites have been collecting personal information for many processes, such as banks, internet connections, and government services. The public needs to provide all personal information, such as Aadhar, PAN, date of birth, and phone number. The personal and sensitive information is at risk of being used for phishing attacks through URL manipulation. In addition, a phishing attack cause’s financial and reputational loss. Hence protecting sensitive information by adapting required protection is extremely valuable for global security. To overcome this, we proposed a method to detect phishing attacks based on previous history, including the duration of operation, customer reviews, web traffic, and the URL. Based on these parameters, the proposed optimal machine learning-based algorithm (OmLA) analyze the previous information about URLs and predict whether it is phishing- or legitimate. As per simulation and performance analysis, the proposed method outperforms conventional methods such as random forest (RF), support vector machine (SVM), and genetic algorithms (GA) by 8%, 18%, and 23%, respectively in terms of accuracy. Additionally, it achieves detection times of 0.2%, 0.6%, and 0.9%, respectively, and excels in response times of 0.45%, 0.56%, and 0.62%, respectively.
Android malware detection using the random forest algorithm El Attaoui, Anas; El Hami, Norelislam; Koulou, Younes
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.pp1876-1883

Abstract

The rapid growth in Android device usage has resulted in a significant increase in malware targeting this platform, posing serious threats to user security and privacy. This research tackles the challenge of Android malware detection by leveraging advanced machine learning techniques, with a particular emphasis on the random forest (RF) algorithm. Our primary objective is to accurately identify and classify malicious applications to enhance the security of Android devices. In this study, we employed the RF algorithm to analyze a comprehensive dataset of Android applications, where the classification of each application as either malware or benign is known. The method was rigorously tested, yielding impressive results: an average accuracy of 98.47%, a sensitivity of 98.60%, and an F-score of 98.60%. These metrics underscore the effectiveness of our approach. Moreover, we conducted a comparative analysis of the RF algorithm against other malware detection methods. The results demonstrate that the RF algorithm outperforms these alternative methods, offering superior detection capabilities and contributing to more robust Android security measures.
Automatic human height measurement system based on camera sensor with deep-learning and linear regression analysis Fadllullah, Arif; Zulfia, Rahmatuz; Pradana, Awang; Yudhistira Akbar, Muhammad Adhiya
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1627-1636

Abstract

This study proposes a new approach for automatically measuring human height using a camera sensor with deep learning and linear regression analysis. The camera sensor is used to capture real-time images of human objects. The image is then processed with a YOLO4-based convolutional neural network (CNN) to separate the region of interest (ROI) of the human object from the background. The pixel value of the ROI vertical line is then converted into height in centimeters by the linear regression equation. The system was tested on 40 primary samples, with 20 samples used as control data and 20 samples used as test data. From the results of testing 20 control data samples, the linear regression equation was obtained as y' = 0.4034x + 24.938, which was then applied to convert the system's predicted height in centimeters for 20 test samples. The test results for 20 test samples showed that an average F1_score was 1, the R_square obtained was 0.93, the root mean square errors (RMSE) was 0.02, and the percentage of accuracy was 99.00%. The test results showed that the system was able to automatically detect human height with a very high level of correlation/similarity and accuracy between actual and predicted height.
Performance of dyslexia dataset for machine learning algorithms Jincy, J.; Hency Jose, P. Subha
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp994-1001

Abstract

Learning disability is a condition usual amongst most populace due to poor phonological capability in humans making them impaired. One such neurological disorder is developmental dyslexia, a lack of reading and writing skills leading to difficulty in school education. The essential causes of developmental dyslexia are the consumption of more drug treatments during pregnancy, the over-the-counter purchase of medicines for minor ailments without the recommendation of physicians, and uncared-for head accidents during early life. The occurrence of this trouble is acute in India. Attempts were made by many to detect dyslexic children to reduce the intensity of this hassle. In this proposed effort, machine learning is used to locate significant styles characterizing people using EEG samples. A dataset is used for examination of developmental dyslexia, and classification is done using K nearest neighbor (KNN), decision tree, linear discriminant analysis (LDA), and support vector machine (SVM) to evaluate the performance. This piece of research work is done on MATLAB to provide results on simulation with classification accuracy of 90.76% for SVM, sensitivity of 89% for SVM, and LDA with 91.89% specificity for SVM providing optimum yield.
Virality classification from Twitter data using pre-trained language model and multi-layer perceptron Tedjasulaksana, Jeffrey Junior; Girsang, Abba Suganda
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1952-1962

Abstract

Twitter is one of the well-known text-based social media that is often used to disseminate content. According to Katadata, Indonesia ranked fifth in the world in 2023. So many people or organizations want to make tweets go viral. Therefore, this research aims to develop a model that uses tweet data from the Indonesian language Twitter social media to categorize the level of virality. There are several tasks in classifying the level of virality, such as upsampling data, predicting sentiment and emotion, and text embedding. Upsampling data was carried out because the dataset used was an imbalanced dataset. Data upsampling, emotions, and text embedding is carried out using the bidirectional encoder representation from transformers (BERT) model. Meanwhile, sentiment prediction uses the Ro-bustly optimized BERT pretraining approach (RoBERTa). The results of text embedding, sentiment, emotion, will be combined with Twitter metadata then all features will be fed into the multi-layer perceptron (MLP) model to classifying the level of virality which is divided into 3 classes based on the number of retweets, namely low, medium and high. The proposed method produces an F1-score of 49% and an accuracy of 95% and performs better than the baseline model.
Neurons to heartbeats: spiking neural networks for electrocardiogram pattern recognition Mohamad Noor, Nor Amalia Dayana Binti; Chiew, Wong Yan; Noh, Zarina Mohd; Sarban Singh, Ranjit Singh
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp863-871

Abstract

The electrocardiogram (ECG) is one of the most significant methods of diagnostics for determining heart rhythm disorders. For this study, raw ECG signals from the Physio Bank database are subjected to an important preprocessing step that uses empirical mode decomposition (EMD) on signal denoising and distortion elimination. Establishing functioning spiking neural networks (SNN) involves figuring out the neuron’s state through its activity level, challenging due to its resemblance to the human brain’s data processing, yet appealing due to factors like improved unsupervised learning methods, with ten parameters chosen for the learning algorithm of SNN. A comprehensive set of 15 different time-domain features and 10 Cepstral domain features is precisely extracted to train the SNN classifier. An extensive study is conducted to analyse the learning parameters that affect SNN performance, significantly influencing result accuracy. Through a two-classification process, the differentiation between normal and abnormal ECG patterns can be achieved in this study. A maximum testing accuracy of 91.6667% and a maximum training accuracy of 99.1667% have been attained by the process. These results demonstrate the competency of the system in distinguishing between distinct ECG classes, particularly in identifying normal and abnormal cardiac rhythms through ECG classification.

Page 843 of 914 | Total Record : 9138

 841   842   843   844   845 

Filter by Year

2012 2026


Reset
Filter By Issues
All Issue Vol 41, No 1: January 2026 Vol 40, No 3: December 2025 Vol 40, No 2: November 2025 Vol 40, No 1: October 2025 Vol 39, No 3: September 2025 Vol 39, No 2: August 2025 Vol 39, No 1: July 2025 Vol 38, No 3: June 2025 Vol 38, No 2: May 2025 Vol 38, No 1: April 2025 Vol 37, No 3: March 2025 Vol 37, No 2: February 2025 Vol 37, No 1: January 2025 Vol 36, No 3: December 2024 Vol 36, No 2: November 2024 Vol 36, No 1: October 2024 Vol 35, No 3: September 2024 Vol 35, No 2: August 2024 Vol 35, No 1: July 2024 Vol 34, No 3: June 2024 Vol 34, No 2: May 2024 Vol 34, No 1: April 2024 Vol 33, No 3: March 2024 Vol 33, No 2: February 2024 Vol 33, No 1: January 2024 Vol 32, No 3: December 2023 Vol 32, No 1: October 2023 Vol 31, No 3: September 2023 Vol 31, No 2: August 2023 Vol 31, No 1: July 2023 Vol 30, No 3: June 2023 Vol 30, No 2: May 2023 Vol 30, No 1: April 2023 Vol 29, No 3: March 2023 Vol 29, No 2: February 2023 Vol 29, No 1: January 2023 Vol 28, No 3: December 2022 Vol 28, No 2: November 2022 Vol 28, No 1: October 2022 Vol 27, No 3: September 2022 Vol 27, No 2: August 2022 Vol 27, No 1: July 2022 Vol 26, No 3: June 2022 Vol 26, No 2: May 2022 Vol 26, No 1: April 2022 Vol 25, No 3: March 2022 Vol 25, No 2: February 2022 Vol 25, No 1: January 2022 Vol 24, No 3: December 2021 Vol 24, No 2: November 2021 Vol 24, No 1: October 2021 Vol 23, No 3: September 2021 Vol 23, No 2: August 2021 Vol 23, No 1: July 2021 Vol 22, No 3: June 2021 Vol 22, No 2: May 2021 Vol 22, No 1: April 2021 Vol 21, No 3: March 2021 Vol 21, No 2: February 2021 Vol 21, No 1: January 2021 Vol 20, No 3: December 2020 Vol 20, No 2: November 2020 Vol 20, No 1: October 2020 Vol 19, No 3: September 2020 Vol 19, No 2: August 2020 Vol 19, No 1: July 2020 Vol 18, No 3: June 2020 Vol 18, No 2: May 2020 Vol 18, No 1: April 2020 Vol 17, No 3: March 2020 Vol 17, No 2: February 2020 Vol 17, No 1: January 2020 Vol 16, No 3: December 2019 Vol 16, No 2: November 2019 Vol 16, No 1: October 2019 Vol 15, No 3: September 2019 Vol 15, No 2: August 2019 Vol 15, No 1: July 2019 Vol 14, No 3: June 2019 Vol 14, No 2: May 2019 Vol 14, No 1: April 2019 Vol 13, No 3: March 2019 Vol 13, No 2: February 2019 Vol 13, No 1: January 2019 Vol 12, No 3: December 2018 Vol 12, No 2: November 2018 Vol 12, No 1: October 2018 Vol 11, No 3: September 2018 Vol 11, No 2: August 2018 Vol 11, No 1: July 2018 Vol 10, No 3: June 2018 Vol 10, No 2: May 2018 Vol 10, No 1: April 2018 Vol 9, No 3: March 2018 Vol 9, No 2: February 2018 Vol 9, No 1: January 2018 Vol 8, No 3: December 2017 Vol 8, No 2: November 2017 Vol 8, No 1: October 2017 Vol 7, No 3: September 2017 Vol 7, No 2: August 2017 Vol 7, No 1: July 2017 Vol 6, No 3: June 2017 Vol 6, No 2: May 2017 Vol 6, No 1: April 2017 Vol 5, No 3: March 2017 Vol 5, No 2: February 2017 Vol 5, No 1: January 2017 Vol 4, No 3: December 2016 Vol 4, No 2: November 2016 Vol 4, No 1: October 2016 Vol 3, No 3: September 2016 Vol 3, No 2: August 2016 Vol 3, No 1: July 2016 Vol 2, No 3: June 2016 Vol 2, No 2: May 2016 Vol 2, No 1: April 2016 Vol 1, No 3: March 2016 Vol 1, No 2: February 2016 Vol 1, No 1: January 2016 Vol 16, No 3: December 2015 Vol 16, No 2: November 2015 Vol 16, No 1: October 2015 Vol 15, No 3: September 2015 Vol 15, No 2: August 2015 Vol 15, No 1: July 2015 Vol 14, No 3: June 2015 Vol 14, No 2: May 2015 Vol 14, No 1: April 2015 Vol 13, No 3: March 2015 Vol 13, No 2: February 2015 Vol 13, No 1: January 2015 Vol 12, No 12: December 2014 Vol 12, No 11: November 2014 Vol 12, No 10: October 2014 Vol 12, No 9: September 2014 Vol 12, No 8: August 2014 Vol 12, No 7: July 2014 Vol 12, No 6: June 2014 Vol 12, No 5: May 2014 Vol 12, No 4: April 2014 Vol 12, No 3: March 2014 Vol 12, No 2: February 2014 Vol 12, No 1: January 2014 Vol 11, No 12: December 2013 Vol 11, No 11: November 2013 Vol 11, No 10: October 2013 Vol 11, No 9: September 2013 Vol 11, No 8: August 2013 Vol 11, No 7: July 2013 Vol 11, No 6: June 2013 Vol 11, No 5: May 2013 Vol 11, No 4: April 2013 Vol 11, No 3: March 2013 Vol 11, No 2: February 2013 Vol 11, No 1: January 2013 Vol 10, No 8: December 2012 Vol 10, No 7: November 2012 Vol 10, No 6: October 2012 Vol 10, No 5: September 2012 Vol 10, No 4: August 2012 Vol 10, No 3: July 2012 More Issue