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 9,174 Documents
 752   753   754   755   756 
Selection of autofocus algorithms for printed circuit board automated optical inspection system Rizki Putra Prastio; Rodik Wahyu Indrawan
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp856-865

Abstract

This paper presents an examination study of 11 autofocus algorithms for printed circuit board (PCB) automated optical inspection (AOI). A selection of an optimal algorithm for that application based on some criteria was carried out. Unlike microscopy, PCB optical inspection does not require very high magnification. The object in this work was also different from that of microscopy and thus influenced the image features. We analyzed 47 PCB images, size of 640×480, sequentially captured every 1 mm in the z-direction. This work utilized USB digital microscope, and the magnification was set at ten times. Each algorithm calculated the sharpness values of the image sequences, and the plot of the sharpness profile was created. Moreover, the research also carried out experiments in several strategies, including image resizing and applying the non-local means (NLM) denoising filter to assess the algorithm performance in different situations. The algorithms were examined and ranked based on five criteria, i.e., computation time, full width at half maximum (FWHM), accuracy, number of half maxima, and range. The experimentation results showed that the Brenner gradient worked best for analyzing images both in their original dimension or resized images.
Machine learning based approach for detection of fake banknotes using support vector machine Haider Khalil Easa; Ali Ali Saber; Noor Kaylan Hamid; Hindren Ali Saber
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp1016-1022

Abstract

Currency counterfeiting is a significant offense that has an impact on a nation's finances. Due to the enormous progress in printing technology, it is now quite simple to create fake currency that resembles real currency in both appearance and texture, making it nearly difficult to manually tell them apart. The suggested approach will be helpful in identifying fake currency in financial systems. Because of the rise of fake currency in the market, numerous false note detecting techniques are available globally to address this issue, however the most of them rely on expensive technology. In this paper, we'll introduce a revolutionary way for separating fake banknotes from real ones using the support vector machine (SVM) approach. To categorize bank notes as authentic or counterfeit utilizing the data retrieved from the photos of the bank notes, SVM performs better overall and is more effective, particularly when it comes to pattern categorization. Finally, the results of our experiment will demonstrate that the suggested algorithm does really yield extremely good performance.
Machine learning in predicting whistle-blowing intention of academic dishonesty with theory of planned behaviour Suraya Masrom; Nor Hafiza Abdul Samad; Rahayu Abdul Rahman; Farah Husna Mohd Fatzel; Siti Marlia Shamsudin
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp909-916

Abstract

The COVID-19 pandemic and its aftermath have caused most higher educations to choose to implement remote learning as a new method of instruction and assessment. Nevertheless, remote learning has been criticized by having adverse impact on academic integrity. Whistle-blowing has been regarded as an effective mechanism in limiting such unethical behavior. Thus, the main objective of this study is to identify the influence attributes of whistle-blowing intention among university students. The effectiveness of the whistle-blowing attributes was observed in prediction models based on machine learning technique. This paper presents the fundamental knowledge on evaluations of tree-based machine learning algorithms namely decision tree, random forest, to be compared with logistics regression and gradient linear model. A rigorous evaluation reports are provided that includes the area under curve (AUC) as a supplementary metric to measure the model accuracy. Additionally, to provide a clearer insight on the whistle-blowing prediction models, the pattern of influences from the whistle-blowing attributes based on the adoption of theory of planned behavior (TPB) and demography are presented. The findings revealed that both TPB and demography attributes contain some degree of impressive knowledge for the machine learning to generate a good prediction result.
Impact of cell temperature on the performance of a rooftop photovoltaic system of 2.56 kWp at Universitas Pamulang Ojak Abdul Rozak; Mohd Zamri Ibrahim; Muhamad Zalani Daud; Syaiful Bakhri; Rifqi Muwaffiq
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp599-608

Abstract

The performance of solar panels greatly determines the electrical energy production of a solar power generation system. The decrease in performance has an impact on efficiency, output power, output voltage and current. Currently, at Universitas Pamulang a solar photovoltaic system (PV) is installed with a capacity of 2.56 kWp since 2018. However, no performance test and analysis have been conducted to determine its level of efficiency and reliability. This paper presents an experimental method used for performance testing of a 320 W mono-crystalline solar panel, measuring from 08.00 AM to 4.00 PM, using the solar survey 200R to measure solar irradiation, ambient and cell temperature. A digital multimeter CD800a was used to measure Voc and a PV200 tester used to measure voltage and current output. The results revealed that at an ambient temperature of 38°C and cell temperature 50.9°C, the intensity of solar radiation was 702.7 W/m2 and output voltage of 42.9 V with a performance of 78.37% and an efficiency of 27.73% was due to an increase cell temperature. Low-efficiency values with high cell temperatures indicate that this system requires an external solar panel cooling system.
A novel multiple access communication protocol for LoRa networks without LoraWAN Jeremiah Prasetyo; Musayyanah Musayyanah; Jusak Jusak
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i3.pp1440-1448

Abstract

Current studies predict there will be approximately 75 billion internet of things (IoT) devices connected to the internet by 2025. Such enormous IoT networks will require efficient communications systems to support those networks running well. We consider one of the emerging communications technologies that is widely used for wireless wide-area network, i.e., long range (LoRa). By default, LoRa wide area networking (LoRaWAN) has been equipped with a medium access control (MAC) protocol, however, several studies showed that LoRaWAN might not be the most excellent choice for certain applications of low-power wide area network, for example, peer-to-peer and mesh networks. In this work, we propose an application layer LoRa multi-communication (LMC) protocol to resolve multiple access problems in LoRa networks without employing LoRaWAN. The algorithm was embedded and tested on energy-constraint devices as building blocks of most IoT systems. Our examination in a controlled environment showed that the proposed algorithm achieved 94% averaged packet reception ratio (PRR) and 33.3 hours longer averaged battery lifetime compared to the default operation (without LMC algorithm) of LoRa networks. The proposed algorithm also introduced longer averaged time on air (ToA) compared to the default LoRa network mainly due to its best-effort service scenario applied to the proposed algorithm.
A multi-instance learning based approach for whitefly pest detection Lal Chand; Amardeep Singh Dhiman; Sikander Singh
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp1050-1060

Abstract

Agriculture constantly faces various challenges including attacks from new pests and insects. With large farm sizes and plummeting manpower in the agricultural sector, it becomes challenging to continuously monitor crops for pest infestation. In this research paper, a specific type of pest attack known as the white fly attack has been investigated which affects a variety of crops. This paper presents four different approaches for automated classification of whiteflies which are the Bayesian network, convolution neural network (CNN), ResNet and multi-instance learning-CNN. A comparative analysis with conventional machine learning and deep learning techniques has also been presented. The performance of the proposed technique has been evaluated in terms of the classification accuracy. The experimental results obtained show that the proposed technique attains a classification accuracy of 95.53%, 96.9%, 97.6% and 98.13% for the four models respectively. A comparative analysis in terms of accuracy of classificaiton, with existing techniques shows that the proposed technique outperforms baseline deep learning models identifying whitefly infestation.
Brain tumor detection in the Spark system Soumia Benkrama; Nour Elhouda Hemdani
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp755-762

Abstract

Machine learning (ML) and computer vision systems revolutionized the world, especially deep learning (DL) for convolutional neural networks, which has proven breakthroughs in brain tumor (BT) diagnosis. This study investigates a convolutional neural network (CNN) approach for image classification for BT detection using the EfficientNetB1 architecture with global average pooling (GAP) layers in a big data setting. A classification layer is done with a softMax layer. The system is created in the Apache Spark environment. Spark system is a unified and ultra-fast analysis engine for large-scale data processing. It is mainly dedicated to big data and deep learning (DL). Experiments are carried out using the brain magnetic resonance imaging (MRI) dataset containing 3,264 MRI scans to predict the performance of the model. The dataset is decomposed into two datasets. The model's performance was assessed and compared to existing models, it yielded a high precision, precision, and f1-score. In our work, we have achieved an accuracy of 97% and a performance of 98% on a dataset of 3,064 brain MRI images.
Cybersecurity in health sector: a systematic review of the literature Catherine Vanessa Peve Herrera; Jonathan Steve Mendoza Valcarcel; Mónica Díaz; Jose Luis Herrera Salazar; Laberiano Andrade-Arenas
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp1099-1108

Abstract

Currently, health centers are being affected by various cyberattacks putting at risk the confidential information of their patients and the organization because they do not have a plan or tools to help them mitigate these cyberattacks, which is important to know what measures to take to protect the privacy of personal data. The present work was carried out under a systematic literature review, which aims to show the importance of cybersecurity in the health sector knowing which tools are the most used and efficient to prevent a cyberattack. A systematic review of 301 articles was carried out, 79 of which are aligned with the objective set, fulfilling the inclusion and exclusion criteria. The search for information was carried out in the Scopus and Dimensions databases. The analysis carried out has resulted in good information that was compiled for the development of this topic, being favorable thanks to the different research of different authors.
Privacy aware-based federated learning framework for data sharing protection of internet of things devices Yuris Mulya Saputra; Ganjar Alfian
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp979-985

Abstract

Federated learning (FL) has emerged as one of the most effective solutions to deal with the rapid utilization of internet of things (IoT) in big data markets. Through FL, local data at each IoT device can be trained locally without sharing the local data to the cloud server. However, this conventional FL may still suffer from privacy leakage when the local data are trained, and the trained model is shared to the cloud server to update the global prediction model. This paper proposes a FL framework with privacy awareness to protect data including the trained model for IoT devices. First, a data/model encryption method using fully homomorphic encryption is introduced, aiming at protecting the data/model privacy. Then, the FL framework for the IoT with the encryption method leveraging logistic regression approach is discussed. Experimental results using random datasets show that the proposed framework can obtain higher global model accuracy (up to 4.84%) and lower global model loss (up to 66.4%) compared with other baseline methods.
Footprint biometric authentication using SqueezeNet Sairul Izwan Safie; Rusmawarni Ramli
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp893-901

Abstract

Biometric authentication is a process of identity verification once an identity is claimed by an individual. It uses unique features on the human body. Footprints are a new biometric feature that has sparked interest among researchers, as this feature is universal, easy to extract and has not changed throughout time. The focus of researchers in this field is to improve the recognition rate. Various techniques have been developed for this purpose, but the accuracy percentage is at 98% with an equal error rate (EER) of 6.1%. This paper proposes the use of a new technique called SqueezeNet in classifying footprint images. SqueezeNet belongs to the convolutional neural network (CNN) family. In this study, 300 footprint images were used from 15 individuals. The 70% of these images were used to train the proposed SqueezeNet network, while the rest were used for testing. At the end of this simulation, SqueezeNet has achieved an accuracy of 98.67% with an EER of 2.1%.

Page 754 of 918 | Total Record : 9174

 752   753   754   755   756 

Filter by Year

2012 2026


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