cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Tole Sutikno
Contact Email
ijece@iaesjournal.com
Phone
-
Journal Mail Official
ijece@iaesjournal.com
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
International Journal of Electrical and Computer Engineering
Published by Institute of Advanced Engineering and Science
ISSN : 20888708     EISSN : 27222578     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of Advanced Engineering and Science (IAES). The journal is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 111 Documents
 1   2   3   4   5 
Search results for , issue "Vol 14, No 5: October 2024" : 111 Documents clear
Wind-powered water pumping system for corn plantations under the food estate program on Sumba Island, Indonesia Aziz, Amiral; Rostyono, Didik; Zaky, Toha; Hesty, Nurry Widya; Ifanda, Ifanda; Fauziah, Khotimatul; Prasetyo, Ridwan Budi; Wijayanto, Rudi Purwo; Witjakso, Ario; Syawitri, Taurista Perdana; Mayasari, Agustina Putri
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp4940-4955

Abstract

The Food and Agriculture Organization (FAO) released a communiqué in March 2020 cautioning about the possibility of a worldwide food emergency due to coronavirus disease (COVID-19). As a response to the food shortages brought on by the COVID-19 outbreak, the authorities of Indonesia initiated a nationwide program aimed at improving the country's food supply known as the food estate (FE), which was later incorporated into national strategic programs. The climate and availability of surface water sources in this region make establishing an FE area in the Central Sumba Regency difficult. Sumba, on the other hand, possesses wind energy resources that can be transformed into electrical energy and used to pump underground water for agricultural purposes. A wind-powered water pump (WPW) is being developed in this study to provide water for maize plantations in the FE region in Central Sumba District, Indonesia. The study on the levelized cost of energy (LCOE) for water pumping indicates that the wind-powered system is more economically viable than the diesel-powered alternative. The LCOE for a WPW pumping system is 6,994 IDR/kWh, whereas the LCOE for a diesel-powered system is 16,667 IDR/kWh. The overall net present value of WPW and diesel-powered systems is 708,667,200 IDR and 2,158,349,000 IDR, respectively. This study contributes significantly to informed decision-making for enhancing the performance viability of the wind water pumping system for the food estate program in Indonesia.
New image encryption approach using a dynamic-chaotic variant of Hill cipher in Z/4096Z Rrghout, Hicham; Kattass, Mourad; Qobbi, Younes; Benazzi, Naima; JarJar, Abdellatif; Benazzi, Abdelhamid
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5330-5343

Abstract

Currently, digital communication generates a considerable amount of data from digital images. Preserving the confidentiality of these images during transmission through network channels is of crucial importance. To ensure the security of this data, this article proposes an image encryption approach based on enhancing the Hill cipher by constructing pseudo-random matrices operating in the ring Z/212Z injected into a controlled affine transformation. This approach relies on the use of chaotic maps for generating matrices used in the encryption process. The use of the ring Z/212Z aims to expand the key space of our cryptosystem, thus providing increased protection against brute-force attacks. Moreover, to enhance security against differential attacks, a matrix of size (4×4), not necessarily invertible, is also integrated into a diffusion phase. The effectiveness of our technique is evaluated through specific tests, such as key space analysis, histogram analysis, entropy calculation, NPCR and UACI values, correlation analysis, as well as avalanche effect assessment.
Utilizing digital elevation models and geographic information systems for hydrological analysis and fire prevention in Khuan Kreng peat swamp forest, Southern Thailand Wanthong, Uraiwun; Ruang-On, Somporn; Limchoowong, Nunticha; Sricharoen, Phitchan; Musik, Panjit
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5408-5419

Abstract

The objectives of this research were to create a topographic model using Mathematica and hydrologic model using ArcGIS for water management aimed at preventing forest fires in the Khuan Kreng peat swamp forest. Pan basin area in Kreng Sub-district, characterized by low mountains, where the Cha-Uat canal intersects the krajood forest, was revealed by the hydrographic model. Kreng Sub-district was traversed by three main streams: Khuan canal, Hua Pluak Chang canal, and Laem canal. Additionally, several tributary canals that interconnect, ultimately converging into the Cha-Uat Phraek Muang canal were identified. During the dry period, the water from these canals flowed into the Cha-Uat Phraek Muang canal. To mitigate the risk of fires, it was essential to install water table measuring devices and underground barrier gates at the drain points. This ensured the return of water from the Cha-Uat Phraek Muang canal to the Khuan Kreng peat swamp forest. Maintaining sufficient water table level was crucial, as the occurrence of fires was more likely when the water table dropped below the soil surface. When the swamp forest was adequately hydrated, wildfires were confined to a narrow area since they could only burn on the forest surface, which was easier to extinguish.
Empowering E-learning through blockchain: an inclusive and affordable tutoring solution Lgarch, Saadia; Hnida, Meriem; Retbi, Asmaa
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5554-5565

Abstract

This study presents an innovative approach using the Ethereum blockchain to democratize access to tutoring services, advancing educational technology by bridging the affordability gap for learners with limited financial resources. This solution enables low-income learners to access tutoring services without significant expenses by eliminating intermediaries through smart contracts. Learners can directly book tutoring services based on fees and evaluations, ensuring a fair and accessible experience. The findings show that this approach reduces tutoring expenses and improves trust and accountability through transparent transactions and feedback mechanisms. The proposed system demonstrates how blockchain technology can foster a more equitable and efficient educational landscape, offering personalized
Empowering crop cultivation: harnessing internet of things for smart agriculture monitoring Alsayaydeh, Jamil Abedalrahim Jamil; Yusof, Mohd Faizal; Magenthiran, Mithilanandini S.; Hamzah, Rostam Affendi; Mustaffa, Izadora; Herawan, Safarudin Gazali
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp6023-6035

Abstract

Agriculture, the foundation of human civilization, has relied on manual practices in the face of unpredictable weather for millennia. The contemporary era, however, witnesses the transformative potential of the Internet of things (IoT) in agriculture. This paper introduces an innovative IoT-driven smart agriculture system empowered by Arduino technology, making a significant contribution to the field. It integrates key components: a temperature sensor, a soil moisture sensor, a light-dependent resistor, a water pump, and a Wi-Fi module. The system vigilantly monitors vital environmental parameters: temperature, light intensity, and soil moisture levels. Upon surpassing 30°C, an automatic cooling fan alleviates heat stress, while sub-300CD light levels trigger light-emitting diode lighting for optimal growth. Real-time soil moisture data is relayed to the “Blynk” mobile app. Temperature thresholds align with specific crops, and users can manage the water pump via Blynk when manual intervention is required. This work advances agricultural practices, optimizing water management by crop type. Through precise coordination of soil moisture, temperature, and light intensity, the system enhances productivity while conserving water resources and maintaining fertilizer balance.
Photovoltaic power prediction using deep learning models: recent advances and new insights Saad, Basma; El Hannani, Asmaa; Aqqal, Abdelhak; Errattahi, Rahhal
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5926-5940

Abstract

Artificial intelligence (AI) and its application across various domains have sparked significant interest, with each domain presenting distinct characteristics and challenges. In the renewable energies sector, accurate prediction of power output from photovoltaic (PV) panels using AI is crucial for meeting energy demand and facilitating energy management and storage. The field of data analysis has grown rapidly in recent years, with predictive models becoming increasingly popular for forecasting and prediction tasks. However, the accuracy and reliability of these models depend heavily on the quality of data, data preprocessing, model learning and evaluation. In this context, this paper aims to provide an in-depth review of previous research and recent progress in PV solar power forecasting and prediction by identifying and analyzing the most impacting factors. The findings of the literature review are then used to implement a benchmark for PV power prediction using deep learning models in different climates and PV panels. The aim of implementing this benchmark is to gain insights into the challenges and opportunities of PV power prediction and to improve the accuracy, reliability and explainability of predictive models in the future.
Detection and counting of wheat ear using YOLOv8 Mas, Muhammad Sabri; Saidah, Sofia; Ibrahim, Nur
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5813-5823

Abstract

Detection and calculation of wheat ears are critical for land management, yield estimation, and crop phenotype analysis. Most methods are based on superficial and color features extracted using machine learning. However, these methods cannot fulfill wheat ear detection and counting in the field due to the limitations of the generated features and their lack of robustness. Various detectors have been created to deal with this problem, but their accuracy and calculation precision still need to be improved. This research proposes a deep learning method using you only look once (YOLO), especially the YOLOv8 model with depth and channel width configuration, stochastic gradient descent (SGD) optimizer, structure modification, and convolution module along with hyperparameter tuning by transfer learning method. The results show that the model achieves a mean average precision (mAP) of 95.80%, precision of 99.90%, recall of 99.50%, and frame per second (FPS) of 22.08. The calculation performance of the wheat ear object achieved accurate performance with a coefficient of determination (R^2) value of 0.977, root mean square error (RMSE) of 2.765, and bias of 1.75.
A fuzzy logic scheme based on spread rate and population for pandemic vaccine allocation Kareem, Abdul; Kumara, Varuna
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5941-5948

Abstract

This paper deals with a novel decision-making scheme for inferring the allocation of vaccines to the provincial health care authorities by the central health care authority of a country in pandemic scenarios. This novel scheme utilizes a fuzzy logic-based inference scheme that utilizes the spread rate and population of a province as inputs to infer the vaccination rate. The proposed scheme is evaluated on the coronavirus disease (COVID-19) data from six southern states of India during the first week of October 2020, collected from the database maintained by the Government of India. The findings demonstrate that the suggested plan, which takes population and spread rate into account, makes sure that enough vaccination doses are distributed to the provinces with a larger spread rate with a higher priority, and that immunizations are not delayed in provinces with controlled spread rates. Also, in due course, all territories will appropriately distribute enough vaccine supply to control the spread. Therefore, this plan strengthens the efforts to control the pandemic outbreaks by ensuring the proper and balanced delivery of vaccines in a timely, efficient, and objective manner.
Gaussian filter-based dark channel prior for image dehazing enhancement Nurhayati, Oky Dwi; Surarso, Bayu; Syafei, Wahyul Amien; Nugraheni, Dinar Mutiara Kusumo
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5765-5778

Abstract

The presence of haze in an image is one of the challenges in computer vision tasks, such as remote sensing, object monitoring, and traffic monitoring applications. The hazy image is considered to contain noise and it can interfere with the image analysis process. Thus, image dehazing becomes a necessity as part of image enhancement. Dark channel prior (DCP) is one of the images dehazing methods that works based on a physical degradation model and utilizes low-intensity values from outdoor image characteristics. The DCP method generally consists of some steps, which are finding the dark channel and gradient image, estimating the sky region, atmospherical light, and transmission map, and reconstructing the dehazed image. This study introduces image dehazing by utilizing the Gaussian filter combined with the DCP method to increase the sharpness and accentuate the details of hazy images. Experimental results show that the proposed method could produce dehazed images with a visual quality is 18.94 dB on average or an increase of 11.91% compared to the original hazy image with a similarity index is 66.71% on average or an increase of 8.10%. Therefore, it is expected that this study can contribute to the image dehazing method enrichment based on DCP.
Detection of elements of personal safety for the prevention of accidents at work with convolutional neural networks Bonfante, Maria Claudia; Hernandez, Ivan; Montes, Juan Contreras; Arrieta Rodríguez, Eugenia; Cama-Pinto, Alejandro
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5824-5833

Abstract

The task of recognizing personal protective elements in workplace environments in real time is fundamental to protecting the employees in case of any accidents. This can be achieved by deploying a convolutional neural network (CNN) algorithm that can efficiently detect protective elements through surveillance devices. Therefore, this work proposes the construction of a model, implementing the you only look once (YOLO) detector, whose architecture has been one of the most tested according to literature review. YOLOv5 and YOLOv7 versions were used and a dataset of 2,000 images for four classes considered. This dataset was collection from various sources and labelled by the authors, of which 80% was used for training, 15% for testing and 5% for model validation. The most important metrics are presented, making a comparison between the models, and finally it was identified that YOLOv7 achieved a higher success rate, which could be considered a more complete solution for occupational health and safety management in companies.

Page 3 of 12 | Total Record : 111

 1   2   3   4   5 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue Vol 16, No 1: February 2026 Vol 15, No 6: December 2025 Vol 15, No 5: October 2025 Vol 15, No 4: August 2025 Vol 15, No 3: June 2025 Vol 15, No 2: April 2025 Vol 15, No 1: February 2025 Vol 14, No 6: December 2024 Vol 14, No 5: October 2024 Vol 14, No 4: August 2024 Vol 14, No 3: June 2024 Vol 14, No 2: April 2024 Vol 14, No 1: February 2024 Vol 13, No 6: December 2023 Vol 13, No 5: October 2023 Vol 13, No 4: August 2023 Vol 13, No 3: June 2023 Vol 13, No 2: April 2023 Vol 13, No 1: February 2023 Vol 12, No 6: December 2022 Vol 12, No 5: October 2022 Vol 12, No 4: August 2022 Vol 12, No 3: June 2022 Vol 12, No 2: April 2022 Vol 12, No 1: February 2022 Vol 11, No 6: December 2021 Vol 11, No 5: October 2021 Vol 11, No 4: August 2021 Vol 11, No 3: June 2021 Vol 11, No 2: April 2021 Vol 11, No 1: February 2021 Vol 10, No 6: December 2020 Vol 10, No 5: October 2020 Vol 10, No 4: August 2020 Vol 10, No 3: June 2020 Vol 10, No 2: April 2020 Vol 10, No 1: February 2020 Vol 9, No 6: December 2019 Vol 9, No 5: October 2019 Vol 9, No 4: August 2019 Vol 9, No 3: June 2019 Vol 9, No 2: April 2019 Vol 9, No 1: February 2019 Vol 8, No 6: December 2018 Vol 8, No 5: October 2018 Vol 8, No 4: August 2018 Vol 8, No 3: June 2018 Vol 8, No 2: April 2018 Vol 8, No 1: February 2018 Vol 7, No 6: December 2017 Vol 7, No 5: October 2017 Vol 7, No 4: August 2017 Vol 7, No 3: June 2017 Vol 7, No 2: April 2017 Vol 7, No 1: February 2017 Vol 6, No 6: December 2016 Vol 6, No 5: October 2016 Vol 6, No 4: August 2016 Vol 6, No 3: June 2016 Vol 6, No 2: April 2016 Vol 6, No 1: February 2016 Vol 5, No 6: December 2015 Vol 5, No 5: October 2015 Vol 5, No 4: August 2015 Vol 5, No 3: June 2015 Vol 5, No 2: April 2015 Vol 5, No 1: February 2015 Vol 4, No 6: December 2014 Vol 4, No 5: October 2014 Vol 4, No 4: August 2014 Vol 4, No 3: June 2014 Vol 4, No 2: April 2014 Vol 4, No 1: February 2014 Vol 3, No 6: December 2013 Vol 3, No 5: October 2013 Vol 3, No 4: August 2013 Vol 3, No 3: June 2013 Vol 3, No 2: April 2013 Vol 3, No 1: February 2013 Vol 2, No 6: December 2012 Vol 2, No 5: October 2012 Vol 2, No 4: August 2012 Vol 2, No 3: June 2012 Vol 2, No 2: April 2012 Vol 2, No 1: February 2012 Vol 1, No 2: December 2011 Vol 1, No 1: September 2011 More Issue