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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
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Internet of things-based floor cleaning robot Abdul Hafiz Kassim; Mohamad Yusof Mat Zain; Mohd Abdul Talib Mat Yusoh; Mohd Nazrul Sidek; Raja Mohd Noorhafizi Raja Daud; Ahmad Izzat Mod Arifin; Mazratul Firdaus Mohd Zin
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.pp1353-1360

Abstract

Internet of t hings (IoT) based floor cleaning robot (FC - Rob) is a floor cleaning robot that uses a smartphone to assist users, primarily housewives and mothers, in completing their chores. The NodeMCU ESP8266 serves as the robot’ s “brain” and is controlled by a smartphone application for the purpose of this research. In accordance with current trends, a n iOS and Android application using Blynk has also been developed for users to control the robot’ s movements, making it the ideal solution for time - crunched individuals. FC - Rob is propelled by two d irect current (DC) motors to ensure comprehensive floor cl eaning. The results of this research are strengthened by tests conducted on battery life and two types of fabric, as well as a comparison with two types of commercially available robots. The positive findings of these tests on this robot demonstrate its effectiveness and efficiency in cleani ng houses, as well as its reasonable cost and educational value for children.
Intelligent-of-things multiagent system for smart home energy monitoring Ratna kumari Vemuri; Chinni Bala Vijaya Durga; Syed Abuthahir Syed Ibrahim; Nagaraju Arumalla; Senthilvadivu Subramanian; Lakshmi Bhukya
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.pp1858-1867

Abstract

The proliferation of IoT devices has ushered in a new era of smart homes, where efficient energy management is a paramount concern. Multiagent artificial intelligence-of-things (MAIoT) has emerged as a promising approach to address the complex challenges of smart home energy management. This research study examines MAIoT's components, functioning, benefits, and drawbacks. MAIoT systems improve energy efficiency and user comfort by combining multiagent systems and IoT devices. However, privacy, security, interoperability, scalability, and user acceptability must be addressed. As technology advances, MAIoT in smart home energy management will offer more sophisticated and adaptable solutions to cut energy consumption and promote sustainability. This article describes how energy status and internal pricing signals affect group intelligent decision making and the interaction dynamics between consumers or decision makers. In a multiagent configuration based on the new concept of artificial intelligence-of-things, this intelligent home energy management challenge is simulated and illustrated using software and hardware. Based on sufficient experimental simulations, this paper suggested that residential clients can significantly improve their economic benefit and decision-making efficiency.
Improving ventilation classification in under-actuated zones: a k-nearest neighbor and data preprocessing approach Yaddarabullah Yaddarabullah; Aedah Abd Rahman; Amna Saad
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.pp233-244

Abstract

This study investigates the use of k-nearest neighbors (k-NN) for classifying occupant positions in under-actuated zones, aiming to enhance ventilation control. The focus is on evaluating different data preprocessing techniques, particularly cumulative moving average (CMA), Kalman filtering (KF), and their combination, to boost the k-NN model's reliability and accuracy. The research uses received signal strength indicator (RSSI) data in a controlled setting. The methodology involves dividing the dataset into training and testing subsets and using root mean squared error (RMSE) to determine the best k value for model validation. The study performs a comparative analysis of the k-NN model's performance with both original and preprocessed RSSI data, focusing on metrics such as accuracy, precision, recall, F1-score, and RMSE. The findings emphasize the significant impact of the combined CMA-KF preprocessing technique in improving the model's accuracy and reliability. Specifically, this approach achieved an accuracy of 98.58%. The RMSE values are particularly noteworthy, exhibiting a perfect fit (RMSE of 0) for training data and a remarkably low RMSE of 0.119 for testing data, confirming the model's high accuracy and predictive capability.
Impacts of Eu2+ -doped K3LuSi2O7 phosphor and a scattering particle on conventional white light emitting diodes Duy, Le Doan; Thai, Nguyen Le; Cong, Pham Hong; Tran, Thinh Cong
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.pp743-750

Abstract

The K3LuSi2O7 phosphor doping Eu2+ rare-earth ions (KLS:Eu) was reported to possess broad emission band from near-ultraviolet to nearinfrared. Additionally, this phosphor showed a wide absorption band of 250-600 nm, allowing it to be excited by blue-light chip of 460 nm, making it one of the suitable phosphor materials for a light emitting diode (LED). Besides, the scattering particle material CaCO3 is incorporated into the yellow phosphor layer to serve the scattering-enhancement purpose. The combination of both materials aims at accomplishing improvements in performance of commercial LED package. The concentration of KLS:Eu is constant while that of CaCO3 is modified. As a result, the scattering factor is regulated and become the key factor influencing the optical outputs of the simulated LED. The increasing CaCO3 concentration enhances the phosphor scattering efficiency of light, helping to improve the lumen output and color-temperature consistency of the LED. However, the color rendering performance declines as a function of the CaCO3 growing amount, despite the presence of a KLS:Eu phosphor layer. Further works should be done to optimize the application of KLS:Eu in cooperation with scattering particles for a higher-quality LED device.
A proposed model using Naïve Bayes and generalized linear models for early detection of heart attack risk Oman Somantri; Linda Perdana Wanti
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 2: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i2.pp1169-1176

Abstract

Timely identification of diseases, particularly heart attacks is crucial for individuals, particularly the elderly, to accurately anticipate the onset of the disease based on symtoms. The objective of this study is to develop a highly accurate model for early detection of heart disease using the Naïve Bayes (NB) and generalized linear model (GLM) techniques. In addition, another concern is the model’s subfar accuracy levels, promting the implementation of measures to optimize it. The suggested approach fot optimization involves the utilization of a genetic algorithm (GA). The research findings indicate that the NB and GLM approaches achive a reasonably high level of accuracy. Specifically, the NB model achieves an accuracy of 82.53%, while the GLM achieves an accuracy of 84.50%. Following optimization, the accuracy levels notably improved, with the NB_M-GA model reaching 85.83% and the GLM_M-GA model achieving 86,48%.
An improved secured cloud data using dynamic rivest-shamir-adleman key Ugbedeojo Musa; Marion O. Adebiyi; Francis Bukie Osang; Abayomi Aduragba Adebiyi; Ayodele Ariyo Adebiyi
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.pp433-441

Abstract

Encryption methods had been widely used for secure data transmission and communication in both public and private organizations against intruders. Rivest-shamir-adleman (RSA) encryption algorithm is one of the most popular and efficient encryption schemes that has been in used for decades. Due to technological advancement and innovation, there is a threat to this algorithm. It is believed that introduction of quantum computer will break RSA algorithm easily. In view of this, it is pertinent to research into how RSA algorithm could be strengthened against all adversaries. This research aim at protecting client/server communication and file sharing by generating dynamic public and private keys. The proposed method was implemented in visual basic.net 2008. The result shows that dynamic keys do not affect the performance of the system and it is capable of protecting communication and file sharing between client/server. As the key generated keeps changing at an interval, it will difficult for most advance computer to factor any of the keys before another key is generated. This is the basis of the security of the proposed system.
Enhancing reconnaissance security: a 2-tier deception-driven model approach (2TDDSM) Anazel P. Gamilla; Thelma D. Palaoag; Marlon A. Naagas
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.pp1999-2006

Abstract

The emergence of network security has revolutionized the way educational institutions operate, providing advanced connectivity, enhanced communication, and efficient management of resources. However, with the increasing dependence on interconnected systems, institutions and organizations became vulnerable targets for cyber threats. To address these security challenges, a two-tier deception-driven model specifically designed to for the initial phase of attacks in reconnaissance period where the adversaries is to gather information of the targets. Defending threats in this phase can provide active and proactive defense allowing the administrator to identify potential attackers and understanding their methods, motivation and potential target assets. The model's layered approach creates a resilient defense mechanism that aligns with the advanced deception techniques which aims to misguide potential threats attempting to gather intelligence within the network.
IoT-based viscometer fabrication using the falling ball method for laboratory applications Alwi Nofriandi; Yulkifli Yulkifli; Asrizal Asrizal; Nur Anisa Sati’at
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.pp89-97

Abstract

This study outlines the production procedure of internet of things (IoT)- enabled viscometers designed for laboratory use. These viscometers utilize photodiode sensors, lasers, and falling ball techniques. The system is equipped with a temperature sensor that is utilized to quantify the impact of temperature on viscosity. The temperature sensor’s characterization yielded a R-square value of 0.999. The photodiode and laser sensors are utilized to operate a timer within the system, ensuring precise time measurement. The R-square value for the sensor characterization is 0.996. A viscometer equipped with an integrated IoT module for seamless wireless transmission of data. The photodiode timer sensor has an accuracy of 95.76% and a precision of 99.96%, while the temperature sensor has an accuracy of 99.43% and a precision of 99.93%. The viscometer transmits the measured viscosity data to the server using wireless technology. This IoT viscometer has the potential to enhance the efficiency and precision of liquid viscosity measurement in laboratory settings. Additionally, it enables real-time monitoring and data collection for subsequent analysis and research purposes.
Image and noise reduction for assessing driver incompetence in cases of sudden unintended acceleration Eugene Rhee; Junhee Cho
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 2: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i2.pp832-838

Abstract

This paper explores using cameras aimed at the accelerator and brake pedals during sudden unintended acceleration in cars, removing noise from captured images to determine driver incompetence. A car model was constructed using Raspberry Pi to simulate brake malfunction using random functions, increasing the revolutions per minute (RPM) to simulate sudden acceleration. By employing a DC encoder motor to measure the motor's rotational speed through waveform counts, the RPM was calculated. The study recognized sudden acceleration when the brake malfunctioned through the DC encoder motor, causing an abnormal RPM increase, allowing camera capture toward the accelerator and brake during sudden acceleration events. Precautions were taken for problems arising from noise in captured images. The Unix operating system was utilized to apply Gaussian filter image processing techniques for noise removal. While using an average value filter led to abrupt changes by replacing with the average of surrounding signals, resulting in an unsmooth image, a Gaussian filter was used in this study to decrease weights as distance from the center increased, mitigating these issues.
Homogenous and multilayer electromagnetics models for estimating skin reflectance Amani Yousef Owda; Majdi Owda
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.pp82-92

Abstract

Reflectance measurements of human skin are widely limited over the millimeter wave (MMW) band in literature. This is due to the cost and technical difficulties of the experimental setup. This paper proposes homogenous and multilayer skin models for estimating the reflectance of the forearm and palm of the hand skin over the MMW band 30-100 GHz. The simulation results demonstrate that the differences in reflectance between the homogenous and multilayer models of forearm skin are limited to 0.014, indicating that the thin stratum corneum (SC) layer in the multilayer skin models has a minimal impact on the interaction with MMW of the forearm skin. However, in the palm of hand skin, there is a substantial difference in reflectance calculations between the homogenous and the multilayer skin models in the range of 0.099 to 0.143. These differences are attributed to the presence of a thick SC layer in the palm of the hand. Thus, the simulation results suggested that two-layer should be used for the palm of hand skin as it better captures the reflectance characteristics of this region. The importance of having those models are in calculating the skin reflectance that can be used for the non-invasive diagnosis of skin conditions.

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