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
 804   805   806   807   808 
Performance improvement in photovoltaic-grid system using genetic algorithm Rangasamy Sankar; Durairaj Chandrakala; Rengaraj Hema; Dakshnamurthy Padmapriya
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1327-1336

Abstract

In recent, photov oltaic (PV) power generation has increased in importance. The growing significance of PV power production has generated the demand for enhancing energy efficiency via continuous operation at the maximum power point (MPP). To enable effective MPP trac king, the suggested system integrates a proportional - integral (PI) controller with the p erturb and observe (P&O) technique. In order to improve performance in a PV grid system, this work provides a unique method using a proportional - integral - derivative (PI D) controller optimized using a genetic algorithm (GA). The proposed controller architecture integrates the GA algorithm with a PID controller in the voltage source inverter (VSI) of the PV system. To enable effective grid integration, the GA is used to co ntinually optimize the PID controller settings. The converter’ s design criteria and computations are discussed, and MATLAB simulations are used to assess the system’ s performance. Compared to traditional PID controllers, the observed findings show increas ed efficiency, cheaper cost, and enhanced controllability. The suggested GA - PID controller offers opportunities for more study and development in this area while showing potential for improving PV grid system performance.
Computationally efficient handwritten Telugu text recognition Buddaraju Revathi; M. V. D. Prasad; Naveen Kishore Gattim
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp1618-1626

Abstract

Optical character recognition (OCR) for regional languages is difficult due to their complex orthographic structure, lack of dataset resources, a greater number of characters and similarity in structure between characters. Telugu is popular language in states of Andhra and Telangana. Telugu exhibits distinct separation between characters within a word, making a character-level dataset sufficient. With a smaller dataset, we can effectively recognize more words. However, challenges arise during the training of compound characters, which are combinations of vowels and consonants. These are considered as two or more characters based on associated vattus and dheerghams with the base character. To address this challenge, each compound character is encoded into a numerical value and used as input during training, with subsequent retrieval during recognition. The segmentation issue arises from overlapping characters caused by varying handwritten styles. For handling segmentation issues at the character level arising from handwritten styles, we have proposed an algorithm based on the language's features. To enhance word-level accuracy a dictionary-based model was devised. A neural network utilizing the inception module is employed for feature extraction at various scales, achieving word-level accuracy rates of 78% with fewer trainable parameters.
Formal validation of authentication scheme in 5G-enabled vehicular networks using AVISPA Mays A. Hamdan; Amel Meddeb Maklouf; Hassene Mnif
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp396-406

Abstract

Smart transportation may come from 5G-enabled cars. Traffic reports include congestion, roads, and driving. Urbanisation and population growth increase traffic accidents and travel time. Traffic accidents kill and injure most people worldwide. Intelligent transportation systems (ITS) improves driver and pedestrian safety. This study connects the VANET to 5G to create a 5G-enabled vehicle network because the road-side unit (RSU) is expensive and unsecure. This study connects numerous automobiles to TA for 5G-BS D2D communication. Data transmissions between autos are risky. Several scholars suggest authentication techniques for safe vehicle-to-vehicle communications. Overhead may enable side-channel attacks with these tactics. A secure and effective efficient and secure authentication-privacy-preserving (ES-APP) system connected TA, 5G-BS, and on-border unit (OBU) was presented. Initialization, vehicle registration, parameter renewal, message signing, single and batch verification are ES-APP steps. The formal evaluation automated verification of internet security protocols and applications (AVISPA) tool with on-the-fly model-checker (OFMC) and attack searcher (ATSE) back-ends secures the suggested ES-APP technique. ES-APP appears impervious to active and passive AVISPA assaults.
IoT-based system to detect and control natural gas leaks in residential kitchens Pillco-Sanchez, Max Jhonatan; Huatuco-Villanueva, Alex Antonio; Sanchez-Ramirez, Jhony Miguel; Cabana-Cáceres, Maritza; Castro-Vargas, Cristian
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp761-778

Abstract

Natural gas is widely used in many homes for cooking, but a lack of gas leak detection has led to large fires and accidents. This article presents the design and implementation of a natural gas detection and extraction system for domestic kitchens in Lima, Peru. The ESP32 microcontroller allowed remote circuit control, resulting in a more convenient setup than the Arduino UNO microcontroller. After calibration of the sensors and their corresponding programming, three actions were established in response to different gas levels: alarm activation, space ventilation and gas extraction, and thermal shutdown. Strategic sensor placement and improved physical presentation of the system were performed to ensure accurate readings and effective deployment. The results demonstrate the proper functioning of the circuit and its ability to prevent accidents related to gas leaks. The designed system offers the advantage of remote monitoring, providing access to the user from any location. In conclusion, this project offers a comprehensive solution to prevent accidents caused by gas leaks in home kitchens. With satisfactory results in terms of operation and rapid response, the project demonstrates its effectiveness in accident prevention. This design offers a practical and accessible solution, improving security and bringing peace of mind to homes.
An efficient high throughput BCH module for multi-bits error correction mechanism on hardware platform Rohith Puttaraju; Ramesha Muniyappa
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp1499-1508

Abstract

The bose-chaudhuri-hocquenghem (BCH) codes are a cyclic error correction codes (ECC) class. The BCH is constructed by using a polynomial over the Galois field. The BCH codes can detect and correct the multi-bits with an easy decoding mechanism. The BCH codes are used in most of the storage device's cryptography, disk drives, and satellite applications. This manuscript presents an efficient high-throughput BCH module with an encoding and decoding mechanism for multi-bit corrections. The BCH code of (15, k) is used to construct the encoder and decoder architectures. The BCH encoder decoder (ED) module with single error correction (SEC), double error correction (DEC), and triple-error correction (TEC) are discussed in detail. The BCH encoder module uses a linear feedback shift register (LFSR). The BCH decoder with SEC and DEC is constructed using the syndrome generator module (SGM) and chien search module (CSM). The BCH decoder with TEC is designed using SGM, inversion-based berlekamp-massey-algorithm (BMA), and CSMs. The BCH-ED module with SEC, DEC, and TEC utilizes <1 % chip area on Artix-7 FPGA. The BCH-ED with SEC, DEC, and TEC achieves a throughput of 7.13 Gbps, 1.2 Gbps, and 0.803 Gbps, respectively. Lastly, the BCH module is compared with existing BCH approaches with better improvement in chip area, frequency, and throughput parameters.
SWT-PCA-CNN: hyperspectral image classification with multi-stage feature extraction and parameter tuning Tilottama Goswami; Kandi Navya Shruthi; Sindhu Chokkarapu; Raghavendra Kune; Mukesh Kumar Tripathi
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp59-68

Abstract

Hyperspectral imaging is an increasingly popular technique in remote sensing, offering a wealth of spectral information for a range of applications. This paper presents a comparative study of hyperspectral image classification techniques using three different datasets: Indian Pines, Salinas, and Pavia University. The study employs a combination of three methods, namely stationary wavelet transforms (SWT), principal component analysis (PCA), and convolutional neural network (CNN), to develop a model for hyperspectral image classification. The proposed approach combines SWT and PCA for spatial feature extraction and dimensionality reduction, followed by classification using CNN. Furthermore, the study performs parameter tuning by changing the optimizer, activation function, and filter size of the CNN model on the Indian Pines dataset. The results demonstrate that the proposed SWT-PCA-CNN approach outperforms the conventional DWT-PCA and PCA-KNN algorithms, achieving an overall classification accuracy of 98.2%, 99.86%, 99.80% on the Indian Pines, Salinas and Pavia University datasets respectively. The study highlights the effectiveness of the proposed approaches for hyperspectral image classification and their potential for applications in remote sensing and other fields.
Ensemble learning techniques against structured query language injection attacks Odeh, Ammar; Taleb, Anas Abu
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1004-1012

Abstract

Structured query language (SQL) injection threats pose severe risks to web applications, necessitating robust detection measures. This study introduced DSQLIA, employing ensemble learning algorithms-Bagging, Stacking, and AdaBoost classifiers-for SQL injection detection. Results unveiled the bagging classifier's 84% accuracy with perfect precision (100%) but moderate recall (68%). The stacking classifier achieved 85% accuracy, exceptional precision (99%), and balanced memory (72%), yielding an 83% F1-Score. Remarkably, the AdaBoost classifier outperformed, achieving 99% accuracy, high precision (98%), and outstanding recall (99%), leading to a remarkable 99% F1-Score. These findings highlight AdaBoost's superior ability to identify malicious queries with minimal false positives accurately. Overall, this research underscores the potential of ensemble learning in fortifying web application security against SQL injection attacks, emphasizing the AdaBoost classifier's exceptional performance in achieving precise and comprehensive detection.
Bengali sign language translator with location tracking system Tanim Rahman; Tanjia Chowdhury; Jeenat Sultana
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1760-1767

Abstract

Designing an embedded system to convert sign language to sound forms to communicate with the outside world can be a challenging yet rewarding project, especially for mute people. To convey a speaker's thought through sign language, hand shapes, hand orientation and movement, and facial expressions must be combined concurrently. This research is intended to design a system that translates sign language into sound forms to establish communication with the outside world for people who are deaf, those who can hear but cannot physically speak, or have trouble with spoken languages due to some other disabilities. They can thus receive prompt assistance and stay out of uncomfortable circumstances. Additionally, this system incorporates a tracking system that uses a global system for mobile communications (GSM)/ global positioning system (GPS) module to locate a person using a tracking device and send the location to previously saved emergency contact numbers so that someone nearby can quickly locate and assist the person. Typically, each nation has its own native sign language. This project will create a few essential and typical sentences and phrases in Bengali.
A novel artificial intelligent-based approach for real time prediction of telecom customer’s coming interaction Reyad Hussien; Mohamed Mahgoub; Shahenda Youssef; Ashraqat Torky; Nermin K. Negied
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp540-556

Abstract

Predicting customer’s behavior is one of the great challenges and obstacles for business nowadays. Companies take advantage of identifying these future behaviors to optimize business outcomes and create more powerful marketing strategies. This work presents a novel real-time framework that can predict the customer’s next interaction and the time of that interaction (when that interaction takes place). Furthermore, an extensive data exploratory analysis is performed to gain more insights from the data to identify the important features. Transactional data and static profile data are integrated to feed a deep learning model which is implemented using two methodologies: time-series approach and statistical approach. It is found that the time-series approach gives the best performance and fulfills all the requirements. The experiments show that the proposed framework introduces a good overall performance in comparison to existing approaches based on standard metrics like accuracy and mean absolute error (MAE) values. What makes the proposed work novel and special is that it is the first approach that addresses the telecom customer’s next future interaction not just churn prediction like the other approaches in literature.
Combined wavelet transforms and neural network feed-forward model for ECG peak detection and classification Badiger, Raghavendra; Manickam, Prabhakar
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1343-1360

Abstract

We have focused on development of a combined approach for electrocardiogram (ECG) signal filtering and various ECG peak detection. The filtering model is based on the combination of wavelet transform and neural network where after computing the wavelet coefficients the neural network feed-forward model is used to update the weights. The filtered signal is processed through the convolution layers and bidirectional long short-term memory (Bi-LSTM) architecture to perform the ECG peak detection. Further, we apply a combined feature extraction strategy where wavelet transform and morphological feature are extracted to classify the ECG beats as classify 5 different types of heartbeats, including premature ventricular contraction (PVC), left bundle branch block (LBBB), right bundle branch block (RBBB), PACE, and atrial premature contraction (APC) to examine the heart condition. The feature extraction phase uses wavelet transform, morphological features and high-order statistics to generate the robust features. The obtained feature vector is processed through the principal component analysis (PCA) module to reduce the dimension of feature vector. These features are trained by using support vector machine (SVM) and k-nearest neighbor (KNN) supervised model. The proposed approach is tested on publicly available MIT-BIH dataset where performance analysis shows that the proposed approach obtained average precision, sensitivity and error as 99.98%, 99.96%, and 0.101 which outperforms the existing filtering and peak detection schemes.

Page 806 of 914 | Total Record : 9138

 804   805   806   807   808 

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