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
 870   871   872   873   874 
Multi-camera multi-person tracking with DeepSORT and MySQL Raghavendra, Shashank Horakodige; Sorapalli, Yashasvi; Poojar S. V., Nehashri; Maddirala, Hrithik; Kumar P., Ramakanth; Nasreen, Azra; Trivedi, Neeta; Agarwal, Ashish; K., Sreelakshmi
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp997-1009

Abstract

Multi-camera multi-object tracking refers to the process of simultaneously tracking numerous objects using a network of connected cameras. Constructing an accurate depiction of an object’s movements requires the analysis of video data from many camera feeds, detection of items of interest, and their association across various camera perspectives. The objective is to accurately estimate the trajectories of the objects as they navigate through a monitored area. It has several uses, including surveillance, robotics, self-driving cars, and augmented reality. The current version of an object tracking algorithm, DeepSORT, doesn’t account for errors caused by occlusion or implementation of multiple cameras. In this paper, DeepSORT has been extended by introducing new states to improve the tracking performance in scenarios where objects are occluded in the presence of multiple cameras. The communication of track information across multiple cameras is achieved with the help of a database. The suggested system performs better in situations where objects are occluded, whether due to object occlusions or person occlusions.
Indonesian sentiment analysis in natural environment topics Octovianto, Christofer; Ibrohim, Muhammad Okky; Budi, Indra
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1353-1366

Abstract

Indonesia is one of the countries that is rich in biodiversity and has a high population growth. This condition can cause Indonesia to have problems related to the natural environment that are more complex than other countries. Hence, this has created a lot of discussions regarding natural environmental issues in Indonesia on social media platforms. In this case, stakeholders like the government in general can utilize sentiment analysis (SA) to comprehend the public’s views to allow them to better fit the public’s expectations when formulating a particular policy that related to the environmental sustainability (ES) issues. This paper built the first open dataset of Indonesian SA dataset in ES topics collected from Instagram. As the benchmark of our dataset, we used IndoBERT model variant for constructing the model and the experiment result shows that model based on IndoBERT-large-p2 obtained the best performance with 72.44% of F1-score.
A recurrent network technique for energy optimization in 6G networks with dynamic device-to-device communication Aneesh, Sonia; Shaikh, Alam N.
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp897-903

Abstract

Energy efficiency has become a paramount concern in the design and deployment of 6G networks, driven by the exponential growth of connected devices and increasing traffic demands. For domain experts grappling with dynamic device-to-device (D2D) communication scenarios, optimizing energy consumption while maintaining reliable connectivity poses a significant challenge. To address this issue, we propose a novel recurrent network technique that dynamically configures D2D communication patterns, adaptively allocating temporary base stations among network nodes to enable efficient data transmission while minimizing energy expenditure. Our simulations demonstrate substantial energy savings, extended node lifetimes, and reliable performance, with a 37% reduction in overall network energy consumption and a 65% increase in average node lifetime compared to traditional cellular communication scenarios. In conclusion, this innovative approach paves the way for sustainable and energy efficient 6G communication systems, benefiting society by reducing operational costs, minimizing environmental impact, and prolonging the usability of mobile devices.
Enhance big data security based on HDFS using the hybrid approach Zine-Dine, Fayçal; Alcabnani, Sara; Azouaoui, Ahmed; El Kafi, Jamal
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1256-1264

Abstract

Hadoop has emerged as a prominent open-source framework for the storage, management, and processing of extensive big data through its distributed file system, known as Hadoop distributed file system (HDFS). This widespread adoption can be attributed to its capacity to provide reliable, scalable, and cost-effective solutions for managing large datasets across diverse sectors, including finance, healthcare, and social media. Nevertheless, as the significance and scale of big data applications continue to expand, the challenge of ensuring the security and safeguarding of sensitive data within Hadoop has become increasingly critical. In this study, the authors introduce a novel strategy aimed at bolstering data security within the Hadoop storage framework. This approach specifically employs a hybrid encryption technique that leverages the advantages of both advanced encryption standard (AES) and data encryption standard (DES) algorithms, whereby files are encrypted in HDFS and subsequently decrypted during the map task. To assess the efficacy of this method, the authors performed experiments with various file sizes, benchmarking the outcomes against other established security measures.
Spatial-temporal data imputation for predictive modeling in intelligent transportation systems Widi Prasetyo, Yohanes Pracoyo; Linawati, Linawati; Wiharta, Dewa Made; Sastra, Nyoman Putra
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp794-807

Abstract

Data imputation is necessary to overcome data loss in intelligent transportation systems (ITS) due to the many sensors used to monitor traffic conditions. Sensor malfunction, hardware limitations, and technical glitches can lead to incomplete data, potentially leading to errors in traffic data analysis. This analysis investigated spatial-temporal data imputation approaches applied for predictive modeling in ITS. Each approach's strengths, weaknesses, and applicability in the context of ITS are evaluated. We analyzed various imputation approaches involving statistical, machine learning, and combined methods. Statistical methods are more straightforward but could effectively handle modern traffic's complexity. On the other hand, machine learning and combined approaches, such as hybrid convolutional neural network (CNN)- long short-term memory (LSTM), offer more robust capabilities in capturing non-linear patterns present in spatio-temporal data. This research aims to investigate the effectiveness of each approach in overcoming data incompleteness and the accuracy of predicting future traffic conditions with the widespread adoption of IoT, electric vehicles, and autonomous vehicles. The results of this investigation provide an understanding of the most suitable approaches to address the challenges of spatio-temporal data imputation and provide practical guidance for predictive modeling in ITS.
Enhancing uncollateralized loan risk assessment accuracy through feature selection and advanced machine learning techniques Salahudin, Shahrul Nizam; Dasril, Yosza; Arisandy, Yosy
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1149-1161

Abstract

Accuracy in evaluating the risk of credit applications is crucial for lenders, particularly when dealing with unsecured loans. Accuracy can be enhanced by selecting suitable features for a machine learning model. To better identify high-risk borrowers, this study applies an elaborate feature selection technique. This study uses the light gradient boosting machine (LGBM) Classifier model with boosting type gradient boosting decision tree (GBDT) algorithm and n_estimator value 100 for feature selection process. This work uses advanced machine learning techniques namely stacking to improve accuracy model perform. The dataset consists of 307,506 applicants from European lenders who have applied for loans in Southeast Asia. Each applicant is described by 126 different features. Using GDBT algorithm GBDT, 30 best features were selected based on their maximum accuracy compared to another feature. By employing a stacking technique that combines the LGBM, gradient boosting (GB), and random forest (RF) models, and utilizing logistic regression (LR) as the final estimator, an accuracy of 0.99637 was reached. This study demonstrates an improved the accuracy compared to previous research. This discovery indicates that utilizing feature selection and stacking method can provide one of the most precise choices for modelling the binary class classification among the current models.
Temperature-dependent based optimal reactive power dispatch by chaotic equilibrium optimization algorithm Dao, Minh Trung; Vo, Ngoc Dieu
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp698-712

Abstract

The optimal reactive power dispatch (ORPD) problem is considered as an important aspect in power system operation of the reactive power, which is vital to maintain network voltage within desirable limit for system reliability. In conventional ORPD problem, the resistance of components in power systems is considered to be independent to their temperature variations. Actually, there is a correlation between the branch resistance and temperature, thus the temperature should be taken into account when performing power flow analysis to improve the accuracy in the calculation of the power flow and power loss on branches. This paper proposes a new chaotic equilibrium optimization (CEO) method to solve the temperature-dependent based optimal reactive power dispatch (TDORPD) problem in power systems by optimizing the reactive power loss and voltage deviation. The proposed CEO algorithm is implemented for the conventional ORPD and TDORPD problems on the benchmark IEEE 30 bus testing network. Moreover, the effects of temperature variations on the considered TDORPD problem are also considered. The obtained results have demonstrated a better performance of the proposed CEO algorithm compared to the original EO and other methods in the literature review for the problem in terms of the solution quality, which confirms its efficacy to effectively resolve the ORPD and TDORPD problem.
Enhancing accessibility: deep learning-based image description for individuals with visual impairments Shah, Nidhi B.; Ganatra, Amit P.
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1051-1060

Abstract

Technological developments in artificial intelligence, namely in the area of deep learning, have created new avenues for enhancing accessibility for those with visual impairments. In order to improve the capacity of people who are blind or visually impaired to understand and interact with visual material, this research investigates the creation and use of deep learning-based image description systems. We provide a comprehensive method that uses recurrent neural networks (RNNs) to generate natural language descriptions and convolutional neural networks (CNNs) and Autoencoders for extracting picture features. Our technology automatically creates comprehensive, context-aware descriptions of photographs by incorporating these models, giving users a better knowledge of their surroundings. We show the accuracy and reliability of the system on a wide range of photos through comprehensive testing. According to our research, deep learning-based picture description systems and converting the description in audio and making a promise to empower people who are visually impaired and foster diversity in the digital sphere.
Credit card fraud detection using CNN and LSTM Upadhyay, Nishant; Bansal, Nidhi; Rastogi, Divya; Chaturvedi, Rekha; Asim, Mohammad; Malik, Suraj; Jayant, Khel Prakash; Vajpayee, Abhay Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1402-1410

Abstract

Credit card fraud is an evolving problem with the fraudsters developing new technologies to perform fraud. Fraudsters have found diverse ways to make a fraud transaction to the card holder. Thus, detecting suspicious behavior of a card is critical for preventing fraudulent transactions to happen. Artificial intelligence techniques, in particular deep learning algorithms can tackle these credit card fraud attacks by identifying patterns that predict transactions as fraud or legitimate. One-dimensional convolutional neural network (1D CNN) and long short-term memory (LSTM) both performs well on the sequential data especially on transactions data, yet there are not many studies done on combining these two algorithms to make an effective fraud detection approach. However, the dataset is highly imbalanced containing only 492 fraud transaction out of two lacs transactions. In this experimental study, firstly datasets will get prepared by using different sampling techniques along with their hybrid techniques secondly, observing the performance of individual CNN and LSTM on the datasets, finally on those datasets in which CNN and LSTM are performing well, by implementing ensemble on those data. The performance of the ensembles is observed using the performance metrics namely accuracy, F1-score, precision and recall. In the proposed experimental study, getting the F1-score of 99.96% and 99.89% in ensemble: early fusion and ensemble: late fusion respectively.
Acute lymphoblastic leukemia diagnosis and subtype segmentation in blood smears using CNN and U-Net Reza, Hamim; Tareq, Nazrul Islam; Rabbi, M M Fazle; Tanim, Sharia Arfin; Rudro, Rifat Al Mamun; Nur, Kamruddin
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp950-959

Abstract

Acute lymphoblastic leukaemia (ALL) is a severe disease requiring invasive, expensive, and time-consuming diagnostic tests for definitive diagnosis. Initial diagnosis using blood smear pictures (BSP) is crucial but challenging due to the similar indications and symptoms of ALL, often leading to misdiagnoses. This study presents a custom approach using Convolutional Neural Networks (CNNs) to detect all cases and categorize subtypes. Utilizing publicly available databases, the study includes 3562 blood smear images from 89 patients. The innovative combination of U-Net for segmentation and various CNN architectures (U-Net, MobileNetV2, InceptionV3, ResNet50, NASNet) for feature extraction, with DenseNet201 being the most effective, forms the core of this method. The U-Net model achieved a segmentation accuracy of 98% by recognizing patterns within blood smear images. Following segmentation, CNN architectures extracted high-level features, with DenseNet201 proving the most effective in diagnostic and classification tasks. Our proposed custom CNN model achieved a test accuracy of 98%, with a training accuracy of 99.31% and validation accuracy of 97.09%. This approach enables an accurate distinction between ALL and non-pathologic cases.

Page 872 of 914 | Total Record : 9138

 870   871   872   873   874 

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