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
 475   476   477   478   479 
Deployment of TinyOS for Online Water Sensing Xin Wang; Pan Xu
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 6: June 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i6.pp4802-4807

Abstract

Current quality assessment methods of water parameters are mainly laboratory based, require fresh supplies of chemicals, trained staff and are time consuming. Sensor networks are great alternatives for such requirements. We present a practical application of wireless networks: a remote water monitoring system running TinyOS. The contents of several chemicals in the water are sensed and transmitted. The sensor data are collected and transmitted via ZigBee and GPRS. Instead of focusing on theoretic issues such as routing algorithms, network lifetime and so on, we investigate special techniques involved in the implementation of the system while employing TinyOS and its special programming language.
A Clustering Expert System using Particle Swarm Optimization and K-means++ for Journal Recommendation to Publish the Papers Seyedeh Malihe Khatami; Mansoureh Maadi; Rohollah Ramezani
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 2: November 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i2.pp814-823

Abstract

In this paper, an android expert system for recommending the suitable journal for publishing the researchers' papers has been presented. In so doing, the expectations of different journals for accepting an article and also the requests of papers' writers for choosing the journals have been examined. Language, quality, waiting time for judgment, waiting time for publication after acceptance, field, length of paper and cost are the system inputs and its output is the degree of suitability of journals for publishing a certain paper. This system includes a database of different journals and their parameters. It uses particle swarm optimization method and K-means++ algorithm for assessing and clustering the journals database and determines an index for every cluster of journals. The process for matching the paper with a cluster's index is done through fuzzy induction system. After choosing the most similar cluster, the paper is compared with all the journals of the cluster in the same way and the result including the most similar journals is presented. This system has been tested for journals and papers in the computer field indexed in Elsevier.
An Efficient Patient Inflow Prediction Model For hospital Resource Management Kottalanka Srikanth; D. Arivazhagan
Indonesian Journal of Electrical Engineering and Computer Science Vol 7, No 3: September 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v7.i3.pp809-817

Abstract

There has been increasing demand in improving service provisioning in hospital resources management. Hospital industries work with strict budget constraint at the same time assures quality care. To achieve quality care with budget constraint an efficient prediction model is required. Recently there has been various time series based prediction model has been proposed to manage hospital resources such ambulance monitoring, emergency care and so on.  These models are not efficient as they do not consider the nature of scenario such climate condition etc. To address this artificial intelligence is adopted. The issues with existing prediction are that the training suffers from local optima error.  This induces overhead and affects the accuracy in prediction. To overcome the local minima error, this work presents a patient inflow prediction model by adopting resilient backpropagation neural network. Experiment are conducted to evaluate the performance of proposed model inter of RMSE and MAPE. The outcome shows the proposed model reduces RMSE and MAPE over existing back propagation based artificial neural network. The overall outcomes show the proposed prediction model improves the accuracy of prediction which aid in improving the quality of health care management.
Course recommendation system using fuzzy logic approach Mohd Suffian Sulaiman; Amylia Ahamad Tamizi; Mohd Razif Shamsudin; Azri Azmi
Indonesian Journal of Electrical Engineering and Computer Science Vol 17, No 1: January 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v17.i1.pp365-371

Abstract

Course selection is a key for success in student’s academic path. In today’s education environment, various courses offered by different academic institutions required the students to explore the course outline manually. Most of them are lacking in knowledge, having dilemma and making blind selections to choose the right course. Therefore, it is essential to have a course recommendation to provide guidance to a student to choose the course related with their interest and skill. This paper proposed to develop a course recommendation system using fuzzy logic approach. The development methodology of this system involves several phases include requirements planning, user design and construction for prototyping, testing and cutover. This study used the fuzzy rules technique in order to calculate each associated student’s skill and interest level based on Mamdani fuzzy inference system method. Then, the rules will generate final outcome which recommend the suitable course path and provide the details to a user based on their course test. The result shows the functionality of this system has been achieved and works well. This study is significantly helping the students to choose their course based on the interest and skill.
Parallel Research and Implementation of SAR Image Registration Based on Optimized SIFT Quan Sun; Jianxun Zhang
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 2: February 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

A new SAR image registration method was Proposed based on improved SIFT algorithm. Which adopted multi-core system platform was used to overcoming the problem of high complexity algorithm of SIFT algorithm; According to the characteristics of SAR image, first of all, the source SAR image was enhanced in airspace, and finish the parallel extraction of feature points with the improved SIFT algorithm, then used Euclidean distance and the RANSAC algorithm to complete the matching of feature points and eliminate unmatching, finally realizes the SAR image registration. The experimental results show that the method can guarantee in the registration precision and reduce the complexity of the registration. DOI : http://dx.doi.org/10.11591/telkomnika.v12i2.4414
Parallelization of Pairwise Alignment and Neighbor-Joining Algorithm in Progressive Multiple Sequence Alignment Agung Widyo Utomo
Indonesian Journal of Electrical Engineering and Computer Science Vol 9, No 1: January 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v9.i1.pp234-242

Abstract

Progressive multiple sequence alignment ClustalW is a widely used heuristic method for computing multiple sequence alignment (MSA). It has three stages: distance matrix computation using pairwise alignment, guide tree reconstruction using neighbor-joining and progressive alignment. To accelerate computing for large data, the progressive MSA algorithm needs to be parallelized. This research aims to identify, decompose and implement the pairwise alignment and neighbor-joining in progressive MSA using message passing, shared memory and hybrid programming model in the computer cluster. The experimental results obtained shared memory programming model as the best scenario implementation with speed up up to 12 times.
New Generation Solar PV Powered Sailing Boat using Boost Chopper Soumya Das; Pradip Kumar Sadhu; Suprava Chakraborty; Nitai Pal; Gourav Majumdar
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 12: December 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i12.pp8077-8084

Abstract

The objective of this paper is to establish technical and economical aspects of the application of stand-alone photovoltaic (PV) system in sailing boat using boost chopper in order to simplify the power system and minimize the cost. Performance and control of dc-dc converter, suitable for photovoltaic (PV) applications, is presented here. This converter is mainly boost converter feeding a dc load. However, for integration purpose only one inductor is sufficient for power conversion in the converter. Here, the boost converter extracts complete power from the PV source and feeds into the load. Furthermore, the PV panel provides the essential protection to the passengers of boat from the straight sunshine and also from the rainwater.
Diabetic analytics: proposed conceptual data mining approaches in type 2 diabetes dataset Sinan Adnan Diwan Alalwan
Indonesian Journal of Electrical Engineering and Computer Science Vol 14, No 1: April 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v14.i1.pp88-95

Abstract

Diabetes is a fast spreading illness, which makes to worry millions of people around the globe. The people affected by type-2 diabetes are rapidly increasing and there are no effective diagnostic systems to control the diabetics. As per global health statistics, in western countries, population effected by type 2 diabetics are higher in rate and cost factor for treatment is increasing. There are no effective methods to eradicate the diabetes and it leads to carry out an investigative study on this disease. In existing reviews, researchers are using data analysis approaches to link the cause for diabetes with the patients based on the diet, life style, inheritance details, age factor, medical history, etc. to identify the root cause of the problem. By having multiple key factors and historical datasets, there are some data mining tools were developed, to generate new rules on the root cause of the disease and discover new knowledge from the past data’s, but the accuracy was not promising. The main objective of this paper is to carry out a detail literature review and design a conceptual data mining method at initial stage and implement it to improve the result accuracy compared to other classifiers. In this research, two data-mining algorithm were proposed at conceptual level: Self Organizing Map (SOM) and Random Forest Algorithm, which is applied on adult population datasets. The data set used for this research are from UCI machine Learning Repository: Diabetes Dataset. In this paper, data mining algorithms were discussed and implementation results were evaluated. Based on the result performance evaluation, Self-organizing maps have performed better compared to the Random Forest and other data mining algorithms such as naïve Bayes, decision tree, SVM and MLP for diagnosing the diabetes with better accuracy. In future, once system is implemented, it can be integrated with diabetic detector device for faster diagnosis of TYPE 2 diabetes disease.
Systematic mapping study of economic and business models of cloud services Isaac Odun-Ayo; Toro-Abasi Williams; Olusola Abayomi-Alli; Jamaiah Yahaya
Indonesian Journal of Electrical Engineering and Computer Science Vol 18, No 2: May 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v18.i2.pp987-994

Abstract

Cloud computing is a business paradigm wherein computers and computing related services are provided by Cloud Service Providers to consumers either as software, development platform, or infrastructure. From an economic and business perspective, Cloud services involve cost, ownership quality of service and service level agreements. There are studies on economic and business models of cloud services on the cloud landscape especially in the area of pricing. Despite this, there is still a dearth of papers in this area of study. The objective of this study is to conduct a systematic mapping study to collect all relevant research on economic and business models of Cloud services. A systematic map provides a structured overview in a particular research area. The representation of the mapping process offers unique course-grained overview of the results. The results are presented in terms of research such as evaluation and solution, and contribution such as tools and method utilized. The results showed that there are more publications on pricing models in relation to tools with 6.87% and model with 14.5%, more publications on economic and business implications in terms of method with 11.45%, more publications on Cloud market in term of processes with 6.87%, more papers on security in the area of evaluation with 8.55% and validation research with 6.58%, and more papers on Cloud markets with respect to experience with 4.61% and validation with 5.92%. The research gaps identified in this study should motivate researchers to carry out more mapping studies in the field.
STATCOM with Battery and Super Capacitor Hybrid Energy Storage System for Enhancement of Voltage Stability Tanneeru Renuka; Gattu Kesavarao
Indonesian Journal of Electrical Engineering and Computer Science Vol 5, No 2: February 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v5.i2.pp250-259

Abstract

To maintain voltage stability of a power system STATCOM is better solution which can provide the required amount of reactive power under various disturbances. In previous work, STATCOM with various energy storage elements was discussed for voltage and power system stability. Apart from these previous works, this work proposes a new structure of hybrid energy storage system (HESS) for voltage stability by using battery and super capacitor. A new model of STATCOM with hybrid energy storage system is designed by using two bidirectional DC-DC converters and results are analyzed for conventional STATCOM and STATCOM with hybrid energy storage system. Results are also analyzed for STATCOM system with out any energy storage system, STATCOM with battery, STATCOM with super capacitor and STATCOM with HESS under sudden load changes by using MATLAB/Simulink.

Page 477 of 918 | Total Record : 9174

 475   476   477   478   479 

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