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 57 Documents
 1   2   3   4   5 
Search results for , issue "Vol 39, No 3: September 2025" : 57 Documents clear
Performance evaluation of distribution network with change of load by connecting wind DG Sankepally, Swathi; Kumar Bali, Sravana
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1459-1466

Abstract

The aim of this research is to determine the optimal location and size of a minimum number of distributed generators (DGs) needed to maintain the stable operation of an IEEE 85-bus distributed network. The main objective is to ensure the stability of the distribution network by optimizing the placement and capacity of DGs. This is accomplished through the utilization of particle swarm optimization (PSO). The stability of the distribution network is checked by evaluating the voltages and power losses using load flow. The stability of the distribution network is assessed using boundary criteria that are not altered by more than 5% of the nominal voltage value. The distribution network voltage stability is assessed using various case studies, one of that involves a change in load driven by connecting WDG and the other by a change in power supply from wind DGs due to varying wind speed. The PSO is implemented in IEEE-85 bus distribution network using MATLAB software.
Comprehensive secure code review analysis of web application security vulnerabilities Aziz, Azlinda Abdul; Mohd Suradi, Nur Razia; Handan, Rahayu; Rizal Arbain, Mohd Noor
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1807-1814

Abstract

A secure code review is a process of software development involves systematic examination of application code. However, web applications evolving of cyber threats makes it challenging to conduct adequate security. Therefore, this paper conducts a comprehensive secure code review analysis to protect any crucial aspect of web security from potential threats and vulnerabilities. The application code is scanned for security issues during the real review and the results are classified according to the areas of vulnerability. As a result, the application code risk level and list of risk categories were defined. This result assists in prioritizing issues for resolution, beginning with the most critical problems to lower risk levels. Next, list of risk categories that give the most significant security vulnerabilities affect to application codes are defined. SQL injection, weak password handling, insecure direct object reference, information exposure, improper session management, missing input validation, deprecated functions, and lack of comments are defined as a risk category. Moreover, the result of application code weakness in the security of the application code is determined based on the level of risk and categories. Thus, analysis result offers the developers a clear perspective on protects the web applications from threats and vulnerabilities.
Development of an automatic processing system for predicting the earthquake signals using machine learning techniques Gupta, Mukesh Kumar; Kumar, Brijesh
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp2023-2031

Abstract

Earthquake signals are crucial for minimizing the impact of seismic activities. Current algorithms face difficulties in correctly identifying P-waves and assessing magnitudes, which affects the amount of advance warning given. It is crucial to establish standardized methods for the effective selection and integration of multiple algorithms. Machine learning techniques could considerably enhance detection reliability. The research seeks to rectify this shortfall and strengthen automated detection as well as prediction capabilities. The model's performance is assessed using real earthquake data in simulations compared to individual algorithms. The objective of this research is to develop an optimized multi-algorithm framework that enhances the warning lead times and overall reliability. This framework underpinning this method is shaped by the operational demands inherent in early warning systems. The objective of the work is to contribute to the betterment of seismic risk reduction. An ML methodology, merging several distinct detection algorithms, will be deployed along with a tailored prioritization system. The intention is to strengthen the model's dependability and its overall level of consistency. The ML-based multi-algorithm framework significantly boosts the performance of Early Earthquake Warning Systems, providing a scalable approach to enhance automated detection and public safety, ultimately advancing the effectiveness of seismic hazard reduction through quicker and more accurate warnings.
A smart wearable posture correcting device based on spine curvature and vibration measurement Christhudass, Jerome; Perumal, Manimegalai; Balachandran, Kowsalya; Chella Muthu, Sella Dharshini; Balasubramanian, Keerthana
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1514-1524

Abstract

In the United States, aalmost $50 billion is expended in neck pain therapy each year. Poor posture, which affects the primary tendon responsible for reproducing finished tasks on time, has previously been recognized as a major source of upper spine discomfort. The primary objective of this study is to design and develop a device that not only detects deviations in posture but also employs vibration alerts to encourage corrective actions. The methodology involves the integration of an inertial measurement unit (IMU) sensor and a Flex Sensor to measure the angle and position of the spine, enabling real-time posture assessment. Additionally, a Piezo-electric sensor is incorporated to measure the vibration of the user's spine. The device provides real-time feedback via a mobile application to help users maintain optimal posture. Data analysis involved filtering and machine learning-based classification to assess posture deviations. The system demonstrated an accuracy of 90% in classifying posture states, with an average error of 2.7° in spine curvature measurement. This research contributes to the field of wearable technology by offering an innovative solution for posture correction, emphasizing the importance of proactive interventions in fostering healthy habits.
Comprehensive multiclass debris detection for solar panel maintenance using ANN models S. M., Renuka Devi; J., Vaishnavi; A., Gayatri; K., Ragini; K., Ramesh Reddy; B., Koti Reddy
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1489-1498

Abstract

Solar photovoltaic (PV) technology has emerged as a leading renewable energy solution globally. However, maintaining optimal performance remains a challenge due to the accumulation of debris, including dust, bird droppings, and other contaminants on the panels. These deposits significantly reduce the efficiency of solar panels, necessitating regular monitoring and cleaning. Automated inspection systems provide a cost-effective alternative to traditional methods by minimizing labor-intensive efforts. This study proposes a machine learning-based framework for detecting and classifying several types of debris on solar panels. The methodology utilizes gray-level co-occurrence matrix (GLCM) texture features and key statistical features extracted from RGB, HSV, and LAB color spaces. A dataset comprising 19 distinct classes, such as “Without Dust,” “Bird Droppings,” “Black Soil,” and “Sand,” was employed to train and evaluate the models. Among the tested classification techniques, artificial neural networks (ANN) achieved a notable accuracy of 93.94%, demonstrating their effectiveness in identifying and categorizing debris. This work underscores the potential of machine learning-based feature extraction and classification techniques to automate solar panel inspection and facilitate targeted cleaning interventions, thereby enhancing overall system efficiency.
Enhancing privacy in document-oriented databases using searchable encryption and fully homomorphic encryption Belhaj, Abdelilah; Ziti, Soumia; Lagmiri, Souad Najoua; El Bouchti, Karim
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1661-1672

Abstract

In cloud-based not only SQL (NoSQL) databases, maintaining data privacy and the integrity are critically challenged by the risks of unauthorized external access and potential threats from malicious insiders. This paper presents a proxy-based solution that provides privacy-preserving by combining searchable encryption and brakerski-fan-vercauteren (BFV) fully homomorphic encryption (FHE) to facilitate secure search and aggregate query execution on encrypted data. Through extensive performance evaluations and security analyses, we show that our approach offers a robust solution for privacy-preserving data operations, with performance overhead introduced by the use of FHE. This solution gives an opportunity for a robust framework for secure data management and querying in NoSQL databases, with promising implications for practical deployment and future research. This work represents a significant advancement in the secure handling of data in NoSQL oriented databases, supplying a practical solution for privacy-conscious organizations.
An improved hybrid AC to DC converter suitable for electric vehicles applications Mahafzah, Khaled A.; A. Obeidat, Mohamad; Alsalem, Hesham; Mansour, Ayman; Riva Sanseverino, Eleonora
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1499-1513

Abstract

This paper introduces a novel hybrid AC-DC converter designed for various applications like DC micro-grids, Electric Vehicle setups, and the integration of renewable energy resources into electric grids. The suggested hybrid converter involves a diode bridge rectifier, two interconnected single ended primary inductor converter (SEPIC) and Flyback converters, and two additional auxiliary controlled switches. These extra switches facilitate switching between SEPIC, Flyback, or a combination of both. The paper ex-tensively discusses the operational modes using mathematical equations, deriving specific duty cycles for each switch based on the circuit parameters. This hybrid converter aims to decrease total harmonic distortion (THD) in the line current. The findings exhibit a THD of approximately 14.51%, showcasing a 3% reduction compared to prior hybrid converters, thereby enhancing the power factor of the line current. Furthermore, at rated load conditions, the proposed converter achieves 90% efficiency. To validate the proposed hybrid converter’s functionality, a 4.5 kW converter is simulated and performed using MATLAB/Simulink after configuring the appropriate passive parameters.
SDN multi-access edge computing for mobility management Lakkaiah, Sri Ramachandra; Kumbhinarasaiah, Hareesh
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1846-1854

Abstract

In recent trends, multi-access edge computing (MEC) is becoming a realistic framework for extensive social networking. The rapid proliferation of internet of things (IoT) devices has led to an unprecedented increase in data generation, placing significant strain on conventional cloud computing infrastructure. MEC also supports ultra-reliable and low latency communications (URLLC) by delivering information and computational resources more quickly to mobile users. As a result, the need for low-latency and reliable communication has become paramount. This paper proposes an MEC architecture that integrates software defined networking (SDN) and virtualization techniques, where MEC enables the orchestration and organization of mobile edge hosts (MEH). Furthermore, the proposed MEC-SDN design minimizes latency while ensuring consistent ultra-low latency communications. The result analysis clearly demonstrates that the proposed MEC-SDN model achieves latency of 6-14 ms, bandwidth of 5.2 Mbits/sec, and SDN-BWMS of 5.4 Mbits/sec, outperforming the existing SDN-Mobile Core Network model. Mobile edge systems are enabled in this research to provide mobility support for users.
Enhancing Qur'anic recitation through machine learning: a predictive approach to Tajweed optimization Daoud, Mohamed Amine; Hadjar Kherfan, Nayla Fatima; Bouguessa, Abdelkader; Mokhtar Mostefaoui, Sid Ahmed
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1562-1570

Abstract

The human voice is a powerful medium for conveying emotion, identity, and intellect. Arabic, as the language of the Qur'an, holds deep spiritual and linguistic importance. Reciting the Qur'an correctly involves following Tajweed rules, which ensure phonetic precision and aesthetic quality. However, mastering these rules is challenging due to complex pronunciation and articulation variations, often requiring expert guidance. Traditional learning methods lack personalized feedback, making it difficult for learners to identify and correct errors. With the rise of machine learning, new opportunities have emerged to support Qur’anic recitation through intelligent analysis of Tajweed patterns and error prediction. This study presents a predictive model that identifies Qur’an reciters using ensemble learning techniques. By incorporating deep learning models like gated recurrent units (GRUs), long short-term memory (LSTM), and recurrent neural network (RNN), the system effectively captures the vocal features unique to each reciter. The model achieves an accuracy rate of 88.57%, demonstrating its potential to support Qur’anic learning and preservation. Nonetheless, its performance may be affected by audio quality and limited training data diversity. To improve adaptability and robustness, future work will focus on enriching the dataset and optimizing the model to generalize better across a broader range of reciters.
A novel deep learning-based hierarchical attention feature fusion network for automated detection of rice leaf diseases Gadag, Tejashwini C.; Raja, D. R. Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1976-1989

Abstract

Rice, as a staple crop, plays a crucial role in global food security, especially in developing countries. However, rice production is significantly impacted by diseases such as Brown Spot, Hispa, and Leaf Blast, which can reduce crop yield. Traditional methods of disease detection rely on manual inspection, which is time-consuming, labour-intensive, and prone to errors. To address these challenges, this paper presents a novel deep learning-based model for automated rice leaf disease detection. This paper proposes a novel deep learning-based model, the hierarchical attention feature fusion network (HAFFN), designed to enhance rice leaf disease detection accuracy by addressing key limitations in existing methods. The HAFFN model integrates multi-level feature extraction with a hierarchical attention mechanism to improve the detection of both small and large infected areas. The core novelty of the proposed approach lies in the combination of the deep multiscale feature fusion network (DMFN), the adaptive multiscale feature aggregator (AMFA), and the deep hierarchical attention module (DHAM). The model was trained and tested on a publicly available rice leaf disease dataset and demonstrated superior performance compared to benchmark models like LeafNet, Xception, and MobileNetV2.

Page 2 of 6 | Total Record : 57

 1   2   3   4   5 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol 41, No 2: February 2026 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