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 64 Documents
 1   2   3   4   5 
Search results for , issue "Vol 28, No 2: November 2022" : 64 Documents clear
An enhanced approach for solving winner determination problem in reverse combinatorial auctions Jawad Abusalama; Sazalinsyah Razali; Yun-Huoy Choo
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 2: November 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i2.pp934-945

Abstract

When a disaster occurs, the single agent does not have complete knowledge about the circumstances of the disaster. Therefore, the rescue agents should coordinate with each other to perform their allocated tasks efficiently. However, the task allocation process among rescue agents is a complex problem, which is NP-complete problem, and determining the rescue agents that will perform the tasks efficiently is the main problem, which is called the winner determination problem (WDP). This paper proposed an enhanced approach to improve rescue agents’ tasks allocation processes for WDP in reverse combinatorial auctions. The main objective of the proposed approach is to determine the winning bids that will perform the corresponding tasks with minimum cost. The task allocation problem in this paper was transformed into a two-dimensional array, and then the proposed approach was applied to it. The main contribution of the proposed approach is to shorten the search space size to determine the winners and allocate the corresponding tasks for a combination of agents (i.e., more than two agents). The proposed approach was compared to the genetic algorithm regarding the execution time, and the results showed good performance and effectiveness of the proposed approach.
Data storage model in low-cost mobile applications I Made Sukarsa; I Kadek Ari Melinia Antara; Putu Wira Buana; I Putu Agung Bayupati; Ni Wayan Wisswani; Dina Wahyuni Puteri
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 2: November 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i2.pp1128-1138

Abstract

Mobile applications that have data transactions between users require a database relational database management system (RDBMS) and RESTful API operating on the hosting service so that all users can access the data. Renting a hosting service is not cheap and creating a RESTful API takes plenty of time. As an alternative to hosting, a free version of the Cloud Firestore service gives full access rights to the database and has an application programming interface (API) to manage data or access data. However, the free version of Cloud Firestore has limitations in terms of storage capacity, read, write, and delete processes. Therefore, redesigning process of the database was carried out into a low-cost version of the database model consisting of SQLite database and a low-cost version of the NoSQL database to overcome this problem. The goal is to reduce storage space usage and read, write, and delete processing on Cloud Firestore. The low-cost version of the database was tested with 6,030 data. The results obtained were savings of 47.27% storage usage, 83.08% write usage, 91.26% read process usage, and 83.19% delete process usage compared to the test results of the relational database model.
Photovoltaic system DC series arc fault: a case study Alaa Hamza Omran; Dalila Mat Said; Siti Maherah Hussin; Sadiq H. Abdulhussain
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 2: November 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i2.pp625-635

Abstract

Photovoltaic (PV) systems are becoming increasingly popular; however, arc faults on the direct current (DC) side are becoming more widespread as a result of the effects of aging as well as the trend toward higher DC voltage levels, posing severe risk to human safety and system stability. The parallel arc faults present higher level of current as compared with the series arc faults, making it more difficult to spot the series arc. In this paper and for the aim of condition monitoring, the features of a DC series arc fault are analyzed by analysing the arc features, performing model’s simulation in PSCAD, and carrying out experimental studies. Various arc models are simulated and investigated; for low current arcs, the heuristic model is used where a set of parameters established. Moreover, the heuristic model’s simulated arc has been shown to be compatible with the experimental data. The features of arc noise in the electrode separation region and steady-arcing states with varied gap widths are investigated. It has been discovered that after an arc fault occurs, arc noise increases, notably in the frequency range below 50 kHz; where this property is useful for detecting DC series arc faults. Besides that, variations in air gap width are more sensitive to frequencies under 5 kHz.
Healthcare assessment for beauty centers using hybrid sentiment analysis Abeer Khalid Al-Mashhadany; Ahmed T. Sadiq; Sura Mazin Ali; Amjed Abbas Ahmed
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 2: November 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i2.pp890-897

Abstract

Because of COVID-19, healthcare became the first interesting domain at the world. Here, comes the role of researchers to do what they can to guide people. Nowadays, the most wanted field is beauty industry. It achieved large market. And the estimation is toward the growing. Researchers can give advice to prevent unhealthy causes in this field. They can apply sentiment analysis methods to make decision whether a Beauty center is healthy or unhealthy. This work develops an improved method of sentiment analysis to classify the beauty centers in Iraq into healthy and unhealthy classes. Researchers used comments of beauty centers’ Facebooks to perform the assessment. The methodologies encompass the two approaches lexicon-based and machine-learning-based. Three machine learning mechanisms had been applied; rough set theory, naïve bayes, and k-nearest neighbors. It will be shown that rough set theory is the best compared with the others two. Rough set theory achieved 95.2%, while Naïve Bayes achieved 87.5% and k-nearest neighbors achieved 78%.
A deep learning based system for accurate diagnosis of brain tumors using T1-w MRI Mona Ahmed; Fahmi Khalifa; Hossam El-Din Moustafa; Gehad Ahmed Saleh; Eman AbdElhalim
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 2: November 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i2.pp1192-1202

Abstract

Detection and classification of brain tumors are of formidable importance in neuroscience. Deep learning (DL), specifically convolution neural networks (CNN), has demonstrated breakthroughs in the field of brain image analysis and brain tumors classification. This work proposes a novel CNN based model for brain tumor classification. Our pipeline starts with prepossessing and data augmentation techniques. Then, a CNN classification step is developed and utilizes ResNet50 architecture as its core. Particularly, our design modified the ResNet50 output with a global average pooling (GAP) layer to avoid over-fitting. The proposed model is trained and tested using different optimization algorithms. The final classification is achieved using a sigmoid layer. We tested the proposed structure on T1 weighted contrast-enhanced magnetic resonance images (T1-w MRI) that are collected from three datasets. A total of 3586 images containing two classes (i.e., bengin, and malignant) were used in our experiments. The proposed model reach highest accuracy 99.8%, and optimal error 0.005 using Adam when compared with other six well-known CNN architectures.
ISO/IEC 25010 based evaluation of rice seed analyzer: a machine vision application using image processing technique Ertie Abana; Benedict Sy
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 2: November 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i2.pp994-1001

Abstract

The traditional approach for quality assessment of rice is done by a human inspector manually which leads to inconsistencies and uncertainties in the assessment due to human error. To address this problem, researchers develop rice classification systems applying different methods. The development of these kinds of applications will contribute to the larger objective of maximizing the production of global food. This study introduced a new method of rice seed classification that applies hashing techniques pre-processing of image prediction and its precision rate is 93.06 percent, with a speed of 8.31 seconds per image. The developed application in this study was evaluated using ISO/IEC 25010 with total mean scores of 4.31 for functional suitability, 4.31 for performance efficiency, 4.58 for compatibility, 4.31 for usability, 4.58 for reliability, 4.51 for security, 4.28 for maintainability, and 4.42 for portability.
Single current sensor based fault tolerant control of interior permanent magnet synchronous machine for drive applications Sankhadip Saha; Urmila Kar
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 2: November 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i2.pp674-685

Abstract

This paper presents an integrated method for current sensor fault detection and fault tolerant control (FTC) for traction interior permanent magnet synchronous motor (IPMSM). The proposed current sensor fault detection method is based on detecting changes in the d-q axis current. The FTC is based on d-q axis current estimation from the reference d-q axis current and the phase current measured by the surviving current sensor. The current estimation process is independent of machine parameters. Hence the estimation is robust and requires less computational cost. The effectiveness of the FTC method is verified by the transient analysis. Such FTC is suitable for electric vehicle traction applications to ensure non-stop control operation of the drive in the entire range of speed. The efficacy of the proposed FTC method is tested through extensive simulations in MATLAB/Simulink environment. The real-time applicability of the proposed FTC method using the cost-effective digital signal processor (DSP) is verified on Texas Instruments© TMSF28379D through the processor in loop (PIL) simulation model.
Performance study of reactive routing protocol in wildfire detection using mobile ad-hoc network Nadia Al-Aboody; Muhsin Al-Amery
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 2: November 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i2.pp859-866

Abstract

The routing protocols play an important role in creating routes and sending packets between sensor nodes. There are many methods in the literature that presented and applied several protocols in various domains. However, there is a lack of using routing protocols in the detection of wildfire. Moreover, most methods have used a single number of sensor nodes, where there is a need to investigate the routing protocol based on different simulation parameters such as the number of sensor nodes. Therefore, in this paper, we propose a type of reactive routing protocol that is named Location Aided Routing (LAR). The simulation of LAR protocol has been conducted based on a various number of sensor nodes in order to deeply study and investigate the LAR protocol in the detection of wildfire. In addition, different performance metrics are used for evaluating the performance of the LAR protocol. In the simulation, the performance of LAR protocol shows promising results in the wildfire detection.
A prediction model based machine learning algorithms with feature selection approaches over imbalanced dataset Alaa Khalaf Hamoud; Mohammed Baqr Mohammed Kamel; Alaa Sahl Gaafar; Ali Salah Alasady; Aqeel Majeed Humadi; Wid Akeel Awadh; Jasim Mohammed Dahr
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 2: November 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i2.pp1105-1116

Abstract

The educational sector faced many types of research in predicting student performance based on supervised and unsupervised machine learning algorithms. Most students' performance data are imbalanced, where the final classes are not equally represented. Besides the size of the dataset, this problem affects the model's prediction accuracy. In this paper, the Synthetic Minority Oversampling Technique (SMOTE) filter is applied to the dataset to find its effect on the model's accuracy. Four feature selection approaches are applied to find the most correlated attributes that affect the students' performance. The SMOTE filter is examined before and after applying feature selection approaches to measure the model's accuracy with supervised and unsupervised algorithms. Three supervised/unsupervised algorithms are examined based on feature selection approaches to predict the students' performance. The findings show that supervised algorithms (LMT, Simple Logistic, and Random Forest) got high accuracy after applying SMOTE without feature selection. The prediction accuracies of unsupervised algorithms (Canopy, EM, and Farthest First) are enhanced after applying feature selection approaches and SMOTE filter.
Congestion aware and game based odd even adaptive routing in network on chip many-core architecture Radha Doraisamy; Minal Moharir; Rajakumar Arul
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 2: November 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i2.pp962-972

Abstract

The era of single processors had almost reached a saturation state, and the industry had moved to multi-core processors for the newer generation of many-core architecture. Interconnections between multiple cores with network on chip (NoC) surpass traditional bus architecture for its quality of service (QoS) and other additional services. Seamless communication among the cores is more significant for better performance and the proper utilization of the cores. The rise in the cores count in a semiconductor chip adds the complexity of the communication among cores. Cache misses request and packet transmission’s traffic possibly will reduce the performance of the architecture. A theoretical game-based methodology is proposed to improvise the performance and communication by routing the request packets in the NoC of the many core architectures and the throughput is maximized with reduced latency by using the stag-hunt game (SHG) model. The proposed communication algorithm routes the packets in an adaptive way by detecting the congestion in routers. The SHG based odd-even routing algorithm is adaptive and can divert the packets towards less congested routers using the information gathered about congestion in the system, so that the overall performance of the system in terms of latency and throughput is improved.

Page 2 of 7 | Total Record : 64

 1   2   3   4   5 

Filter by Year

2022 2022


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