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 49 Documents
 1   2   3   4   5 
Search results for , issue "Vol 40, No 3: December 2025" : 49 Documents clear
Potential field-based approaches for nanobotics in drug delivery Kamajaya, Leonardo; Siradjuddin, Indrazno; Al Azhar, Gillang; Fitri, Fitri; Fahmi Fahanani, Agwin
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1298-1307

Abstract

Nanorobotics has transformed targeted drug delivery by enhancing therapeutic efficacy, minimizing off-target effects, and increasing precision. However, navigating complex biological environments is challenging. In the field of macroscopic robotics, potential field (PF)-based approaches that utilize attractive and repulsive virtual forces provide a promising framework that can be applied to path planning for nanorobots. This study modifies PF algorithms for nanorobotic navigation to address challenges such as avoiding dynamic obstacles, escaping local minima, and optimizing trajectories in real time. We evaluated the movement of the nanorobot through simulations under static and dynamic conditions for the targets and obstacles. The results demonstrate that nanorobotics with hybrid PF methodologies enhance navigation performance, enabling nanorobots to successfully navigate through biological barriers and efficiently reach their target locations. This work is a significant step towards intelligent and autonomous nanorobotic drug delivery systems and contributes to practical biomedical applications.
Miniaturized reconfigurable metamaterial based bandstop filter for wireless applications Chavda, Khyati; K. Sarvaiya, Ashish; K. Vala, Mehul
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1337-1344

Abstract

The design of compact size and high efficiency metamaterial based reconfigurable microstrip bandstop filter for IEEE 802.11 WLAN applications is developed. This paper presents a switchable dual-mode filter, it resonant at 2.4 GHz and 3.6 GHz. The hexagonal metamaterial resonator inserted switch as PIN diode which form reconfigurable filter. By changing the DC bias of the diode, the filter can be reconfigured with a controlled precision, resulting in the frequency reconfigurable. The CST simulator used to simulate filter design, measuring a return loss over -29.12 dB and a low insertion loss less than -0.2 dB, which is a great performance. The filter is compact at the size of 8 mm×12 mm×1.6 mm design using Rogers RT Duroid 5880 substrate.
Artificial intelligence in diagnostic medicine: a case study of kidney disease applications Douache, Malika; Benbakreti, Samir; Benbakreti, Soumia; Nawal Benmoussat, Badra
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1232-1240

Abstract

The rapid evolution of artificial intelligence (AI), particularly in convolutional neural networks (CNNs) and deep learning, has revolutionized numerous domains, ranging from medical imaging to creative arts and legal analytics. This research emphasizes the role of pre-trained CNN architectures in identifying kidney conditions, leveraging a dataset comprising images of healthy kidneys as well as those affected by cysts, tumors, and stones. The pretrained models known for their outstanding image recognition capabilities, were adapted for this classification task through transfer learning (TL) techniques. By refining these models and carefully calibrating key parameters like learning rate, batch size, and network depth, they demonstrated superior performance compared to traditional machine learning approaches. The findings underscore the transformative potential of pre-trained CNNs in advancing the precision of kidney disease diagnostics, with implications for broader medical applications.
Dynamic driver of digital devices for embedded systems design Kunle Akinde, Olusola; Adeola Ajagbe, Sunday; Abiodun Afe, Rotimi; Bethel Mutanga, Murimo
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1251-1260

Abstract

A wide-ranging exploration of the diverse applications of embedded systems (ES) is delved in in this study, tracing their evolution from early industrial control to their current pervasive influence on modern technological landscapes. The study underscores their crucial role in various sectors, including consumer electronics, automotive technology, medical and healthcare, education and research, industrial automation, telecommunications, smart cities, edge computing, and the convergence of 5G and artificial intelligence (AI). It accentuates the versatility and transformative potential of ES. The paper reviews the historical, current, and future contributions and evolution of ES in shaping contemporary technological landscapes. Emphasizing the broad impact of ES, the paper highlights their significance for researchers, practitioners, and enthusiasts navigating the dynamic intersection of technology and diverse disciplines.
Enhancing evaluation practices for Islamic inheritance calculation systems: toward a standardized benchmark Reda Kurdi, Ghader; Mohammad Justanieah, Hala
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1549-1566

Abstract

Accurate estate distribution is a critical aspect in Islamic law, governed by complex rules that require precise inheritance calculations. Although numerous computerized inheritance calculation systems have been developed, their reliability remains questionable due to inadequate evaluation and unclear criteria for test case selection. This study addresses this gap by introducing a structured evaluation methodology to rigorously assess the functionalities of inheritance calculation systems. A new benchmark comprising 50 test cases was developed by reviewing the functionality of existing systems, collecting prior test cases and identifying coverage gaps through a detailed gap analysis. These benchmark cases were then used to assess the performance of leading online inheritance calculators, comparing their results to expert-validated solutions. Results revealed a significant drop in performance for calculators previously reported to achieve near-perfect accuracy, with scores declining to 68% and 58% compared to earlier reports of 100% and 90%. This demonstrates the effectiveness of the proposed test cases in exposing limitations within current systems. In contrast, the Almwareeth calculator, which had not been previously evaluated, demonstrated the highest accuracy (86%) and was able to handle a wider range of cases. This study lays a critical foundation for advancing the evaluation standards of Islamic inheritance calculation systems, thereby enhancing their reliability in real-world applications.
Optimizing resume information extraction through TSHD segmentation and advanced deep learning techniques Abuhamdah, Anmar; Al-Shabi, Mohammed; Jawarneh, Sana
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1453-1465

Abstract

This research focuses on a significant factor in the natural language processing area, which is extracting information from unstructured textual data through efficient methods in order to pull useful insights and structured representations from this data. This research attempts to boost the effectiveness of information retrieval systems through computational analysis. This paradigm is explored in this work using question answering models in an extractive style, a modern information extraction approach, creating a new methodology combining the topic segmentation based on headings detection (TSHD) segmentation algorithm and deep learning methods. The TSHD algorithm breaks documents into sections in which certain topics are addressed. Refined extraction models are then used to process these disjoint segments leading to more accurate and contextjudicious extraction compared to naive whole-document extraction approaches. We empirically validate this approach using the stanford question answering dataset (SQuAD) 1.1 dataset, with a specific adaptation to resumes. Experimental results show that the performance metrics increase by 7.4% in exact match (EM) and by 7.8% in F1-score. This can be concluded from these results illustrating the feasibility of the proposed approach in the automated information extraction frameworks such as resume processing.
OFDM/CDMA channel quality estimation using K-means algorithm Abrous, Mohammed; Yagoubi, Benabdellah; Cherifi, Abdelhamid
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1391-1400

Abstract

This work aims to estimate the transmission channel quality and suggest a possible way to enhance the data rates to satisfy the increasing demand for higher data rates to a certain extent. The combination of any non-orthogonal subcarrier multiplexed (SCM) with CDMA needs a large bandwidth, hence a limited number of subcarriers and number of users as well as lower data rates. In contrast, orthogonal subcarriers such as the case of OFDM which are closely spaced due to their orthogonality property as well as to their reduced frequency selectivity fading are, therefore, crucial for increasing subcarriers and thus, increasing the data rates as well as the number of users. To describe the OFDM/CDMA technique in more detail, we performed a simulation using the software Scilab 5.5.2. In this simulation, we treat a simple example of a certain number of users using a bipolar orthogonal code, particularly, the Hadamard/Welsh code for the OCDMA, and the fast fourier transform (FFT) algorithm for the OFDM. For a more realistic simulation, we have introduced a gaussian white noise in the transmission channel and studied the effect of this noise on the eye diagram. Finally, to avoid the computational complexity in calculating the BER to study the OFDM/CDMA channel system quality, we have instead computed the bias and the variance of a noisy 16- quadrature amplitude modulation (QAM) constellation at the reception using the K-means algorithm.
Energy-efficient knapsack algorithm for intelligent cluster head selection in IoT enabled wireless sensor networks Aleem, Abdul; Thumma, Rajesh
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1735-1742

Abstract

The demand for wireless sensor networks (WSN) has grown rapidly with the development of the internet of things (IoT), which requires sensors that are both energy-efficient and scalable to support continuous data collection and real-time monitoring applications. The main challenge is limited battery life in network nodes, which necessitates effective energy management strategies to prolong network lifespan. This paper introduces an energyefficient knapsack algorithm (EEKA) for smart cluster head (CH) selection in IoT WSNs, aiming to optimize energy use while enhancing network stability and data transmission efficiency. The approach features a CH selection strategy based on residual energy, ensuring an even distribution of energy among sensor nodes. The incorporation of the knapsack optimization technique enhances resource allocation, thereby minimizing energy consumption and maximizing transmission reliability. Simulation results using NS2.34/2.35 show remarkable improvement in performance metrics compared to existing techniques: EEKA extends the network lifetime by 16% whereas throughput is enhanced by 17% with reduced latency by 14% under efficient data distribution. Moreover, adaptive CH selection strategy extends coverage by another 20% for wider and effective monitoring. All these results therefore confirm that EEKA has successfully focused on improving energy efficiency, stability, and scalability regarding IoT-driven WSNs to make it a practical solution for real-world applications like smart cities, environmental observation, and industrial automation.
Applied differential comparative study of VANET simulators: TrAD protocol study using veins and VNS VANET simulators in both real and standard city maps Sedjelmaci, Amina; Benosman, Hayet; Abdul Rahuman, Ahmed
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1357-1367

Abstract

This study presents a comprehensive evaluation of vehicular ad-hoc networks (VANETs) by analysing the performance of two leading simulation frameworks: VEINS and VNS. With the increasing demand for efficient vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, understanding the capabilities of data dissemination protocols is crucial for enhancing traffic safety and optimizing route management. We investigate the traffic adaptive data (TrAD) protocol, which dynamically adapts to real-time traffic conditions to ensure reliable communication in high-density vehicular scenarios. Simulations were conducted using OMNeT++ with VEINS and NS-3 with VNS across urban environments in Manhattan and Tlemcen, evaluating TrAD’s effectiveness under diverse traffic conditions. The findings offer valuable insights into the operational strengths of the two simulation frameworks and their implications for advancing vehicular communication systems. This work contributes to the development of robust VANET protocols, supporting innovations in smart and sustainable transportation systems.
The acceptance and adoption of technology on government environment: a bibliometric analysis Hildawati, Hildawati; Wallang, Muslimin; Khairri Shariffuddin, Mohd Dino
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1586-1597

Abstract

This study examines technology acceptance and adoption in government, particularly in the context of public service delivery, through a bibliometric analysis conducted using VOSviewer. The analysis aims to identify key research trends, thematic relationships, and emerging patterns in the field of digital governance. Data were retrieved from the Scopus database, covering publications related to the acceptance and adoption of technology in government from 2020 to 2025. The network visualization results indicate that artificial intelligence (AI), digital governance, public transport, e-health, and COVID-19 are among the dominant research themes, reflecting the rapid adoption of technology in transforming public services. The cooccurrence analysis reveals strong linkages among topics such as public health, AI, blockchain, and public trust, underscoring the increasing integration of digital technologies within governance systems. Furthermore, the overlay visualization demonstrates a thematic shift from fundamental studies on acceptance factors—such as trust, security, and digital literacy— toward implementation-oriented strategies, including digital transformation, smart governance, and public service efficiency. The findings suggest that technology adoption in public service continues to expand and diversify; however, significant challenges remain, particularly concerning data security, transparency, and citizen trust. Future research should focus on exploring the application of AI, the use of blockchain for governance, and the integration of internet of things (IoT) in smart city development to support a sustainable, efficient, and citizen-centric digital transformation in the public sector.

Page 3 of 5 | Total Record : 49

 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