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
 673   674   675   676   677 
In-depth analysis of dynamic degree load balancing technique in public cloud for heterogeneous cloudlets Aparna Shashikant Joshi; Shyamala Devi Munisamy
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp1119-1126

Abstract

Load balancing is one of the challenges of the distributed computing worldview. With the enormous development in clients and their interest for different administrations on the distributed computing stage, compelling or productive asset usage in the cloud climate has turned into an urgent concern. Load balancing is critical to keeping cloud computing running smoothly. This study examines the research using four scheduling algorithms: dynamic degree balance CPU based (D2B_CPU), dynamic degree balanced membership based (D2B_Membership), dynamic degree memory balanced allocation (D2MBA) and hybrid dynamic degree balance (HDDB) algorithm. Central processing unit (CPU) utilisation, bandwidth utilisation, and memory utilisation are used as performance measures to verify the performance of these algorithms. The CloudSim simulation programme was used to simulate these algorithms. The primary goal of this work is to aid in the future construction of new algorithms by researching the behaviour of various existing algorithms.
Blockchain associated machine learning and IoT based hypoglycemia detection system with auto-injection feature Rahnuma Mahzabin; Fahim Hossain Sifat; Sadia Anjum; Al-Akhir Nayan; Muhammad Golam Kibria
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp447-455

Abstract

Hypoglycemia is an unpleasant phenomenon caused by low blood glucose. The disease can lead a person to death or a high level of body damage. To avoid significant damage, patients need sugar. The research aims at implementing an automatic system to detect hypoglycemia and perform automatic sugar injections to save a life. Receiving the benefits of the internet of things (IoT), the sensor’s data was transferred using the hypertext transfer protocol (HTTP) protocol. To ensure the safety of health-related data, blockchain technology was utilized. The glucose sensor and smartwatch data were processed via Fog and sent to the cloud. A Random Forest algorithm was proposed and utilized to decide hypoglycemic events. When the hypoglycemic event was detected, the system sent a notification to the mobile application and auto-injection device to push the condensed sugar into the victim’s body. XGBoost, k-nearest neighbors (KNN), support vector machine (SVM), and decision tree were implemented to compare the proposed model's performance. The random forest performed 0.942 testing accuracy, better than other models in detecting hypoglycemic events. The system’s performance was measured in several conditions, and satisfactory results were achieved. The system can benefit hypoglycemia patients to survive this disease.
Intelligent water flow monitoring system based on internet of things for residential pipeline Siti Sufiah Abd Wahid; Shakira Azeehan Azli; Mohd Sufian Ramli; Khairul Kamarudin Hasan
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp20-27

Abstract

Providing sustainable water supply is a huge challenge for Malaysia whereby the residential areas are still equipped with the conventional water meter with lack of monitoring options. In order to detect the locations of internal leakage, the process requires costly plumber service while manual comparison may be inaccurate and time-consuming. Therefore, digitalization transformation aligned with the industrial revolution IR 5.0 is crucial especially with the recent occurrences of high water bills reports during the movement control order (MCO). The objectives of this project is to develop an intelligent water flow monitoring system using Arduino as a microcontroller and to construct a system that can monitor the water usage behaviour at any distant with internet of thing (IoT). It can be installed anywhere in a pipeline whereby the water flow sensor measures the real-time water parameters. The data transferred to the cloud are sent to the homeowner to display the accuracy and availability of their water system via Blynk, a mobile-compatible and user-friendly application that generates clear data visualization. The key goal of this project is to provide a wireless, mobile, economical and systematic solution for residents to self-monitor their water consumption as compared to the conventional manual monitoring.
The application YAG:Ce3+@SiO2 phosphor for improving color deviation of phosphor-converted light-emitting diode Thanh Binh Ly; Phan Xuan Le
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp618-625

Abstract

The yellow phosphor Y3Al5O12:Ce3+ (YAG:Ce3+), which sees its most popular use in white light-emitting diode (wLEDs), possess an optical spectrum that lacks the red element. The following article will propose a fresh solution for this problem, which involves adjusting the properties of Ce3+ spectrum by using exterior dye particles of ATTO-Rho101, possessing dramatic, wide absorption within the zone of green-yellow spectrum of Ce3+ emission and significant release of the red element. The globular YAG:Ce3+, which is micrometer and nanometer in size with significant dispersion (micro/nano-YAG:Ce3+) was created by employing an altered solvothermal technique. The YAG:Ce3+ produced by said technique, along with the heated micro-YAG:Ce3+ and commercial phosphors, were exteriorly covered with SiO2 and immersed in dye at the same time. Effective radiant transmission/reabsorption from Ce3+ within the YAG’s internal bowel to the dye particles of the exterior hull of SiO2, regardless of the phosphors’ size, was displayed in the YAG: Ce3+@SiO2+ dye powder amassed over the stimulation of the light of blue, which boosted the red element of it. The fluorescent microscope was considered an effective device intended for detecting the reabsorption event in grinded substances.
Machine learning model to classify modulation techniques using robust convolution neural network Nadakuditi Durga Indira; Matcha Venu Gopala Rao
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp811-819

Abstract

In wireless ccommunications receiver plays a main role to recognize modulation techniques which were used at the transmitter. While transferring information from transmitter to receiver, the receiver must retrieve original information. In order to achieve this goal we introduced a neural network architecture that recognizes the types of modulation techniques. The applications of deep learning can be categorized into classification and detection. The CNN architecture is used to perform feature extraction based on the layers to build a model which classifies the input data. A model that classifies the radio communication signals using deep learning method. The robust c (RCNN) is used to train the modulated signals; the transformations are used to help the neural network which estimate the signal to noise ratio of each signal ranges from -20dB to 18dB with loss and accuracy of 89.57% at SNR 0dB.
Effect of chaos factor in radiation pattern in planner antenna arrays with chaos adaptive invasive weed optimization Datla Rajitha; Godi Karunakar
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp692-700

Abstract

For mobile communication and spatial detection of antennas should have high directive radiation pattern, in this context pattern synthesis of planar circular antenna arrays is highly significant such design has been done inverse weed optimization. The basic objective e is to study invasive weed optimization in compression with modified chaotic adaptive invasive weed optimization. The focus of the study is the effect of chaotic factors suitable for sinusoidal mapping for chaos as applicable to the context of the design of antennas. Taking various numbers of elements of the antenna and the distance between the antenna’s radiation patterns are studied by varying chaos factors through MATLAB programming. It is found that the critical point of 2.3 for chaos factor makes the map enter into phase of chaos prior to the critical point is a phase of periodicity starting with chaos factor of 2. Below this value there is no chaos but a phase of convergence. These phases are useful having a trade of convergence and chaos. By varying the factor of chaos the impact on the radiation factor of non-uniform planar antennas has been found to give phases of convergence of chaos which are essential for making trade of between exploitation and exploration required in optimization.
A novel singular value decomposition-based ultra wide band time-of-arrival estimation for multiple targets Ibrahim Yassine Nouali; Zohra Slimane; Abdelhafid Abdelmalek
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp876-884

Abstract

It is widely admitted that the estimation of ultra wide band (UWB) time-of-arrival (TOA) for multiple targets in indoor multipath channels is a very challenging task. The existing algorithms deal with a limited number of targets and require a complex exchange of several messages. In this paper, a novel TOA estimation algorithm for multiple targets is developed. The proposed algorithm estimates the first path (FP) TOA of a number of targets without exchanging messages or using collision avoidance techniques. As a first step, the singular value decomposition (SVD) is employed to extract the first path (FP) of each target and then a matched filter, followed by an iterative threshold crossing algorithm, is used to determine the number of targets and the corresponding FP TOAs. The simulation results with four targets, using the CM1 IEEE 802.15.4a channel model, showed that the proposed novel algorithm can effectively detect the FP of each target and estimate its corresponding TOA.
Mathematical modelling of vehicle drivetrain to predict energy consumption Latha Ramasamy; Ashok Kumar Loganathan; Rajalakshmi Chinnasamy
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp638-646

Abstract

Nowadays, many firms have started producing electric vehicles (EVs). One of the biggest challenges to broad acceptance of electric vehicles is their limited range EVs. Forecasting future energy usage is one of the way to calculate the driving range. In this paper, a simulation model of the drivetrain has been developed to evaluate the energy flow of a vehicle for the given torque and speed conditions. The energy consumption of an electric vehicle is determined by the vehicle's attributes. Road torque, road speed, motor model, motor controller model, battery model, and PI controller are the primary components of the model. The overall resistive force offered by the vehicle, as well as energy consumption owing to resistive force during motoring and regeneration has been validated through the simulation results. Here, the vehicle model, Mercedes Benz Class C Saloon has been considered.
An enhanced hybridized approach for group recommendation via reliable ratings Rachna Behl; Indu Kashyap
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp413-421

Abstract

A group recommender system aim's to provide relevant information to all members of the group. To determine group preferences, the majority of existing studies use aggregation approaches. An aggregation method is a strategy for recommending products to a group by combining the individual preferences of group members. So far, a slew of different types of aggregation algorithms has been discovered. However, they only aggregate one component of the offered ratings (e.g., counts, rankings, high averages), which limits their ability to capture group members' proclivities. This study proposes a novel aggregation method called weighted count that aggregates ratings by providing weights equal to the number of users who provide ratings to an item (location). In addition, the study proposes combining additive utilitarian and weighted count approaches to highlight popular locations on which group members agreed. Experiments on a benchmark check-in dataset demonstrated that the proposed blended technique surpasses the existing methods significantly.
A comparative study to predict breast cancer using machine learning techniques Shiva Shankar Reddy; Neelima Pilli; Priyadarshini Voosala; Swaroop Ravi Chigurupati
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp171-180

Abstract

Detection of disease at the starting stage is a very crucial problem. As the population growth increases, the risk of death incurred by breast cancer rises exponentially. Breast cancer is the most common cancer in women, and it is also the most dangerous of all cancers. Deaths because of breast cancer have b een increasing in recent times. Earlier detection of the disease followed by treatment can reduce the risk and increase survival chances. There will be cases where even medical professionals can make mistakes in identifying the disease. This project deals with the detection of Breast cancer using the cell data of the tumor present in the breast. So, with the help of technologies in machine learning and artificial intelligence can substantially improve the diagnosis accuracy. The development of this project is beneficial in medical decision support systems. Several machine learning techniques, namely Adaboost, multi-layer perceptron (MLP) and stacking classifier; were used, and among all the algorithms, the stacking classifier results in the best accuracy. The accuracies 95.6%, 97.1%, and 99.2% respectively.

Page 675 of 918 | Total Record : 9174

 673   674   675   676   677 

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