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 66 Documents
 1   2   3   4   5 
Search results for , issue "Vol 24, No 3: December 2021" : 66 Documents clear
An agent based model for assessing transmission dynamics and health systems burden for COVID-19 Narassima M. S.; Anbuudayasankar S. P.; Guru Rajesh Jammy; Rashmi Pant; Lincoln Choudhury; Aadharsh Ramakrishnan; Denny John
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i3.pp1735-1743

Abstract

Coronavirus disease of 2019 (COVID-19) pandemic has caused over 230 million infections with more than 4 million deaths worldwide. Researches have been using various mathematical and simulation techniques to estimate the future trends of the pandemic to help the policymakers and healthcare fraternity. Agent-based models (ABM) could provide accurate projections than the compartmental models that have been largely used. The present study involves a simulation of ABM using a synthetic population from India to analyze the effects of interventions on the spread of the disease. A disease model with various states representing the possible progression of the disease was developed and simulated using AnyLogic. The results indicated that imposing stricter non-pharmaceutical interventions (NPI) lowered the peak values of infections, the proportion of critical patients, and the deceased. Stricter interventions offer a larger time window for the healthcare fraternity to enhance preparedness. The findings of this research could act as a start-point to understand the benefits of ABM-based models for projecting infectious diseases and analyzing the effects of NPI imposed.
Enhancement of digital chest images using a modified Sobel edge detection algorithm Archana J. N.; Aishwarya P.; Hanson Joseph
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i3.pp1718-1726

Abstract

Computed tomography (CT) images are an essential factor in the diagnosing procedure for various diseases affecting the internal organs. Edge detection can be used for the appropriate enhancement of the lung CT scan images for the diagnosis of the various interstitial lung diseases (ILD). In order to solve the issues of edge detection provided by the traditional Sobel operator, the paper proposes a Sobel 12D edge detection algorithm which uses the additional direction templates for the better identification of the edge details. First, the vertical and horizontal directions available in the traditional Sobel operator are extended to few more directions (a total of 12 directions) which enhances the edge extraction ability. Next part, compute the edge detected image using the Sobel 12D, Laplace, Prewitt, Robert’s Cross and Scharr operators for edge detection separately. It is followed by image fusion method which optimizes the edge detection by combining the edge detected images obtained using the Sobel 12D approach and the Laplace operator. The experimental results shows that the proposed algorithms generates a better detection of the edges than the other edge detection operators.
Trend of the spread of COVID-19 in Indonesia using the machine learning prophet algorithm Nur Hayati; Fauziah Fauziah; Dendi Rizka Poetra; Dede Wandi
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i3.pp1780-1788

Abstract

Based on information on the BNPB website on 2 September 2020, the positive rate for coronavirus disease (COVID-19) in Indonesia reached 25.25% on 30 August 2020. This is a big challenge for the Indonesian government to reduce the positivity rate to meet the standards safe accepted by World Health Organization (WHO) is 5%. To ensure the accuracy of government policies, accurate data predictions are needed. Therefore, the prophet's machine learning algorithm can be used to see trends in the spread of COVID-19 in the next one year. This algorithm has a fairly high level of accuracy because the data contains time variables which are adjusted to the dataset. In several previous research, the dataset was vast uncertain and small. Meanwhile in this research, data was taken from 2 March 2020 to 12 February 2021 on the KawalCOVID19 website. This data is used to predict from 13 February 2021 to 12 February 2022. There are 3 data used; namely data confirmed, recovered and died. Based on the analysis, the confirmed patient was 22.60-42.11%, died amounted to 21.67%-39.00%, and recovered by 22.53-41.82%. The prediction percentage that the average cases died was 2.43% every day. The accuracy of data confirmed was 43.97%, died was 72.50% and recovered was 84.24%.
Parallel extreme gradient boosting classifier for lung cancer detection Rana Dhia’a Abdualjabar; Osama A. Awad
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i3.pp1610-1617

Abstract

Most lung cancers do not cause symptoms until the disease is in its later stage. That led the lung cancer having a high fatality rate compared to other cancer types. Many scientists try to use artificial intelligence algorithms to produce accurate lung cancer detection. This paper used extreme gradient boosting (XGBoost) models as a base model for its effectiveness. It enhanced lung cancer detection performance by suggesting three stages model; feature stage, XGBooste parallel stage and selection stage. This study used two types of gene expression datasets; RNA-sequence and microarray profiles. The results presented the effectiveness of the proposed model, especially in dealing with imbalanced datasets, by having 100% each of sensitivity, specificity, precision, F1_score, area under curve (AUC), and accuracy metrics when it applied on all of the datasets used in this study.
Proof of concept for lightweight PUF-based authentication protocol using NodeMCU ESP8266 Mohd Syafiq Mispan; Aiman Zakwan Jidin; Muhammad Raihaan Kamaruddin; Haslinah Mohd Nasir
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i3.pp1392-1398

Abstract

Wireless sensor node is the foundation for building the next generation of ubiquitous networks or the so-called internet of things (IoT). Each node is equipped with sensing, computing devices, and a radio transceiver. Each node is connected to other nodes via a wireless sensor network (WSN). Examples of WSN applications include health care monitoring, and industrial monitoring. These applications process sensitive data, which if disclosed, may lead to unwanted implications. Therefore, it is crucial to provide fundamental security services such as identification and authentication in WSN. Nevertheless, providing this security on WSN imposes a significant challenge as each node in WSN has a limited area and energy consumption. Therefore, in this study, we provide a proof of concept of a lightweight authentication protocol by using physical unclonable function (PUF) technology for resource-constrained wireless sensor nodes. The authentication protocol has been implemented on NodeMCU ESP8266 devices. A server-client protocol configuration has been used to verify the functionality of the authentication protocol. Our findings indicate that the protocol used approximately 7% of flash memory and 48% of static random-access memory (SRAM) in the sensor node during the authentication process. Hence, the proposed scheme is suitable to be used for resource-constrained IoT devices such as WSN.
Grey wolf optimization algorithm for hierarchical document clustering Ayad Mohammed Jabbar; Ku Ruhana Ku-Mahamud
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i3.pp1744-1758

Abstract

In data mining, the application of grey wolf optimization (GWO) algorithm has been used in several learning approaches because of its simplicity in adapting to different application domains. Most recent works that concern unsupervised learning have focused on text clustering, where the GWO algorithm shows promising results. Although GWO has great potential in performing text clustering, it has limitations in dealing with outlier documents and noise data. This research introduces medoid GWO (M-GWO) algorithm, which incorporates a medoid recalculation process to share the information of medoids among the three best wolves and the rest of the population. This improvement aims to find the best set of medoids during the algorithm run and increases the exploitation search to find more local regions in the search space. Experimental results obtained from using well-known algorithms, such as genetic, firefly, GWO, and k-means algorithms, in four benchmarks. The results of external evaluation metrics, such as rand, purity, F-measure, and entropy, indicates that the proposed M-GWO algorithm achieves better document clustering than all other algorithms (i.e., 75% better when using Rand metric, 50% better than all algorithm based on purity metric, 75% better than all algorithms using F-measure metric, and 100% based on entropy metric).
Embedded acoustic long baseline localization system for autonomous underwater vehicles Redouane Es-sadaoui; Jamal Khallaayoune; Tamara Brizard
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i3.pp1445-1453

Abstract

The attenuation of global positioning system (GPS) in water medium makes localization of autonomous uderwater vehicles (AUVs) particularly challenging. The long baseline (LBL) positioning system can extend GPS using beacons as references. This work aims at building an acoustic LBL-based system able to localize AUVs operating in swarms thanks to a small size acoustic transceiver embedded onboard AUVs and implementing range-based localization algorithms to estimate the swarm coordinates in real-time. The distances computation between navigating AUVs and fixed beacons were implemented in a digital signal processor (DSP) which computes the time-of-arrival (ToA) of incoming pure tone acoustic waves. The principle of design, hardware architecture, implementation, simulations and sea experiments are described in this paper. The experimental data showed an average deviation around 0.62 m when an AUV is placed at 45 m far away from a beacon. This deviation increases with distance: around 4.8 m measured at 500 m. This performance can be improved by taking into consideration the two main factors examined in this paper, which are sound velocity profile and propagation model.
The implementation of social customer relationship management for tourism information system Ali Ibrahim; Dwi Rosa Indah; Devi Indra Meytri
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i3.pp1578-1588

Abstract

Semambu island village, Ogan Ilir regency, south Sumatra has been used as an agricultural and livestock education tour destination since November 2017 and there has been no customer data management since then. The use of social media as a promotional tool has not been done to its maximum potential. This can be seen from 189 people who liked its Facebook page or 11.05% out of the reached users, 192 followers or 11.23% and those who interacted as many as 114 people or 6.67% from the total users. Meanwhile, there were 709 followers on its Instagram which consisted of 48% men and 52% women at the time of the study. This research applied social customer relationship management (social CRM) in a website-based system. The waterfall model development method supported the customer relations management by utilizing Facebook and Instagram to improve customer relationships in providing travel information, knowing interest and listening to complains as well as their suggestions based on interactions with the social media users as existing and prospective customers.
Comparative techno-economic analysis of power system with and without renewable energy sources Hephzibah Jose Queen; Jayakumar J.; Deepika T. J.
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i3.pp1260-1268

Abstract

The primary aim of this work is to feature the advantages of integrating natural source of energy from the solar and wind to the prevailing electric power systems. Two types of analysis are carried out in two test systems (standard and modified test systems) and the outcome of the test systems are compared. The two analyses are technical analysis and economic analysis. The stability of the voltage is analyzed under technical analysis and the price of energy consumed from the electric grid is calculated and analyzed under the economic analysis. Dynamic hourly load data, hourly solar radiation, hourly wind velocity, and dynamic electricity prices are considered for the standard IEEE system and modified test system (with the integration of RES). Voltage stability index (L-Index) and price of the electricity consumed from electric grid are found for standard test system and the outcome is compared with the outcome of modified test systems. MATLAB coding is done for techno-economic analysis for both test systems. It is inferred from the outcome that the integration of renewable energy sources fairly contributes to the economic benefit of the system by lowering the power purchased from the grid and enhance the stability of the system.
Distant temperature and humidity monitoring: prediction and measurement Farrukh Hafeez; Usman Ullah Sheikh; Attaullah Khidrani; Muhammad Akram Bhayo; Saleh Masoud Abdallah Altbawi; Touqeer Ahmed Jumani
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i3.pp1405-1413

Abstract

Sensing environmental measuring parameters has a pivotal role in our everyday lives. Most of our daily life activities depend upon environmental conditions. Accurate information about these parameters also helps in several industrial applications like ventilation rate calculation, energy prediction, stock maintenance in warehouses, and saving from harmful conditions. The emergence of machine learning can make it easy to predict such time series problems. This paper describes the design of a remotely controlled robotic car for measuring and predicting humidity and temperature. A customized app for accessing the robotic car is designed to indicate predicted and realtime measured values of humidity and temperature. A sensor installed builtin helps in the measurement. The recurrent neural network (RNN) model is used to predict humidity and temperature. For this purpose, experiments are carried out in both outdoor and indoor settings. Accuracy of 85% and 90% is achieved in an outdoor environment and indoor settings.

Page 1 of 7 | Total Record : 66

 1   2   3   4   5 

Filter by Year

2021 2021


Reset
Filter By Issues
All Issue 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