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
Proceeding of the Electrical Engineering Computer Science and Informatics
Published by Institute of Advanced Engineering and Science
ISSN : 2407439X     EISSN : -     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Proceeding of the Electrical Engineering Computer Science and Informatics publishes papers of the "International Conference on Electrical Engineering Computer Science and Informatics (EECSI)" Series in high technical standard. The Proceeding is aimed to bring researchers, academicians, scientists, students, engineers and practitioners together to participate and present their latest research finding, developments and applications related to the various aspects of electrical, electronics, power electronics, instrumentation, control, computer & telecommunication engineering, signal processing, soft computing, computer science and informatics.
Arjuna Subject : -
Articles 649 Documents
 50   51   52   53   54 
Low-Power And High Performance Of An Optimized FinFET Based 8T SRAM Cell Design Nurul Ezaila Alias; Afiq Hamzah; Michael Loong Peng Tan; Usman Ullah Sheikh; Munawar A. Riyadi
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.1951

Abstract

The development of the nanotechnology leadsto the shrinking of the size of the transistors to nanometerregion. However, there are a lot of challenges due to sizescaling of the transistors such as short channel effects (SCEs)and threshold voltage roll-off issues. Fin-Type Field EffectTransistor (FinFET) is another alternative technology tosolve the issues of the conventional MOSFET and increasethe performance of the Static Random Access Memory(SRAM) circuit design. FinFET based SRAMs are faster andmore reliable which are often used as memory cache for highspeed operation. However, 6T SRAM cell suffers from accesstransistor sizing conflict resulting in a trade-off between readand write stability. This paper presents an investigation ofthe stability performance in retention, read and write modeof 22nm FinFET based 8T SRAM cell. The performancecomparison of 22nm FinFET based 6T and 8T SRAMs weremade. The simulation of the SRAM model are carried out inGTS Framework TCAD tool based on 22nm technology. In8T SRAM cell, two n-FinFETs are added to the conventional6T SRAM cell which will be controlled by the Read WordLine (RWL) to isolate the read and write operation path forbetter read stability. FinFET based 8T SRAM cell givesbetter performance in Static Noise Margin (SNM) and powerconsumption than 6T SRAM cells. The simulation resultsaffirms the proposed FinFET based 8T SRAM improvedread static noise margin by 166.67% and power consumptionby 76.13% as compared to the FinFET based 6T SRAM.
Testing Big Data Applications Narinder Punn; Sonali Agarwal; Muhammad Syafrullah; Krisna Adiyarta
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.1952

Abstract

Today big data has become the basis of discussion for the organizations. The big task associated with big data stream is coping with its various challenges and performing the appropriate testing for the optimal analysis of the data which may benefit the processing of various activities, especially from a business perspective. Big data term follows the massive volume of data, (might be in units of petabytes or exabytes) exceeding the processing and analytical capacity of the conventional systems and thereby raising the need for analyzing and testing the big data before applications can be put into use. Testing such huge data coming from the various number of sources like the internet, smartphones, audios, videos, media, etc. is a challenge itself. The most favourable solution to test big data follows the automated/programmed approach. This paper outlines the big data characteristics, and various challenges associated with it followed by the approach, strategy, and proposed framework for testing big data applications.
Object Distance Measurement System Using Monocular Camera on Vehicle Fussy Mentari Dirgantara; Arief Syaichu-Rohman; Lenni Yulianti
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.1953

Abstract

To support autonomous vehicles that are currently often studied by various parties, the authors propose to make a system of predicting the distance of objects using monocular cameras on vehicles. Distance prediction uses four methods and the input parameter was obtained from images processed with MobileNets SSD. Calculations using linear regression are the simplest calculations among the four methods but have an error of 1% with a standard deviation of 1.65 meters. While using the first method, the average error value is 9% with a standard deviation of 0.43 meters. By using the second calculation, the average error resulted in 6% with a standard deviation of 0.35 meters. The experimental method had an average error of 1% with a standard deviation of 0.26 meters, so the experimental method was used.
Technologies, methods, and approaches on detection system of plant pests and diseases Devie Rosa Anamisa; Muhammad Yusuf; Wahyudi Agustiono; Mohammad Syarief
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.1954

Abstract

This research aims to identify the technology, methods, approaches applied in developing plant pest and disease detection systems. For this purpose, it mainly reviews systematically related research on identification, monitoring, detection, and control techniques of plant pests and diseases using a computer or mobile technology. Evidence from the literature shows previous both academia and practitioners have used various technologies, methods and approaches for developing detection system of plant pests and diseases. Some technologies have been applied for the detection system, such as web-based, mobile-based, and internet of things (IoT). Furthermore, the dominant approaches are expert system and deep learning. While backward chaining, forward chaining, fuzzy model, genetic algorithm (GA), K-means clustering, Bayesian networks and incremental learning, Naïve Bayes and Certainty Factors, Convolutional Neural Network, and Decision Tree are the most frequently methods applied in the previous researches. The review also indicated that no single technology or technique is best for developing accurate pest/disease detection system. Instead, the combination of technologies, methods, and approaches resulted in different performance and accuracies. A possible explanation for this is because the systems are used for detecting, controlling and monitoring various plants, such as corn, onion, wheat, rice, mango, flower, and others that are different. This research contributes by providing a reference for technologies, methods, and approaches to the detection system for plant pests and diseases. Also, it adds a way of literature review. This research has implications for researchers as a reference for researching in the computer system, especially for the detection of plant pest and disease research. Hence, this research also extends the body of knowledge of the intelligence system, deep learning, and computer science. For practice, the method references can be used for developing technology for detecting plant pest and disease.
Case Based Reasoning Adaptive E-Learning System Based On Visual-Auditory-Kinesthetic Learning Styles Abdul Rahman; Utomo Budiyanto
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.1955

Abstract

Current technological developments have reached all fields including education. With the support of technology, teaching and learning activities can increase to a better level. The problem that occurs at this time in improving the quality of education is the difficulty of students to get grades that are in accordance with the Minimum Completeness Criteria, the difficulty of the teacher providing material in accordance with each student's learning style. This study aims to develop adaptive E-Learning to assist teachers in recommending material that is suitable for each student's learning style. This adaptive e-learning adopts a Visual Auditory Kinesthetic (VAK) learning style and to recommend material using the Case Based Reasoning (CBR) method. Student test results after using adaptive E-learning have fulfilled the Teaching Mastery Criteria with an average grade of 85. This suggests that under adaptive E-learning has been able to improve student grades.
Prediction Of Students Academic Success Using Case Based Reasoning Abdul Rahman; Rezza Anugrah Mutiarawan; Agung Darmawan; Yan Rianto; Mohammad Syafrullah
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.1956

Abstract

Academic success for a student is influenced by many factors during their study period. Factors such as student gender, student absenteeism, parental satisfaction with schools, relations and parents who are responsible for students can influence student success in the academic field. Researchers try to find out what are the most dominant factors in determining academic success for a student at different levels of education such as elementary, middle and high school level. Previous research grouped the level of student academic success into three levels, namely low, medium, high and obtained 15 Association Rules Generated By Apriori Algorithm. This study tried to find out and predict the possible level of academic success of students by using 9 Association Rules Generated By Apriori Algorithm from previous research. The method used to predict the level of student academic success is case based reasoning with the nearest neighbor algorithm. By using the Association Rules Generated By Image Algorithm and with the data set from the xAPIEducational Mining Dataset the case similarity value was obtained with knowledge data that is 1 with a percentage of 81%, and data that had a similarity value of less than 1 was 19%. While in the previous study the best classification accuracy was 80.6% by the Voting classifier. And the grouping of success data is divided into two, namely low and high.
OTEC Potential Studies For Energy Sustainability In Riau Islands Ibnu Kahfi Bachtiar; Risandi Dwirama Putra
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.1957

Abstract

Interest in the use of alternative renewable energy resources has been developed recently due to increased energy consumption and depletion of fossil fuel reserves. A major concern the world to reduce is dependence impact from fossil fuel consumption with renewable energy. Renewableenergy sources have enormous economic, environmental benefits and provide energy security. The most potential renewable energy sources of ocean energy include Ocean Thermal Energy Conversion (OTEC). OTEC is a technology to generate electricity using a heat source thermal energy stored in the sea and is becoming increasingly attractive option to supply additional energy for many tropical countries and islands such as Riau Islands. Two monitoring stations were collected in Bintan Island using CTD. CTD profiler allows to the determination of derived and relevant quantities in situ measurement ocean temperature per depth. The relationship between ocean temperature and ocean depth represented with Regression Model Fit Analysis (RMFA). RMFA models to estimates ocean temperature profiles from CTD measurements. To predict ocean depths up to 2000 meters using Equation of State Model (EoSM) of ocean water. The OTEC efficiency value can be calculated using the equation of Carnot efficiency (η). Carnot efficiency maximum in Riau Island is η <0.7.
Marine Vessel Telemetry Data Processing Using Machine Learning Herry Susanto; Gunawan Wibisono
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.1958

Abstract

In Indonesia, one of the causes of the high cost of fuel in the shipping industry is theft and misuse of fuel. This happened because ship management center unable to monitor all the activities of the ship when the ship sailing in the middle of the ocean. Lately, ship monitoring through the latest technology are being carried out, one of which is the Machine to Machine (M2M) based Vessel Monitoring System (VMS) technology. The development of VMS and telemetry technology has enabled monitoring of engines and fuel consumption of ships in real time. The problem with this VMS system is that there is still a dependency on the analysis of experts who need a long time to analyze various parameters of existing telemetry data, which lead to inaccuracy and delay in anomaly detection. This study conducted a statistical analysis of telemetry data, especially in ship movement and machine activities, and then designed the fuel consumption regularity classification system with the Naive Bayes and Logistics Regression. Naive Bayes method was chosen because it can produce maximum accuracy with little training data, and Logistics Regression was chosen for its simplicity and excellent results in prediction of numerical and discrete data. The results of this study indicate that telemetry data from the VMS system can be used to detect irregularities in Fuel consumption. Tested with selected data, Naive Bayes classification accuracy in irregularities detection is up to 92% while logistic regression is up to 96%.
Fish Eggs Calculation Models Using Morphological Operation Syaipul Ramdhan; Muhammad Syafrullah
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.1959

Abstract

Calculations on group objects are the concern of current researchers, to find optimal detection and calculation solutions. One of them is fish eggs in a group. Fish cultivators need precision in calculations, because currently conventional methods often make errors in calculations. If the calculation is wrong, it will have an impact on production and sales that are not balanced (loss). Small and easily broken fish eggs are grouped and it isdifficult to do manual calculations. The purpose of this study is to test which segmentation method is the most optimal in calculating these grouped fish egg objects and produce precise and fast calculations. The test model was developed from algorithm of morphological operations,watershed and statistical approaches with the same number of samples. The result shows morphological operation is better than the others with 96.67%, watershed 81.28% and the count statistic is 95.62% with an average calculation process speed of 54.5 seconds for morphological operations, watershed 1 minute 55 seconds and statistical approach 58.9 seconds. As a result. morphology gets the most optimal and fast calculation results.
Implementation of L3 Function on Virtualization Environment using Virtual Machine Approach Marcel Yap
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.1960

Abstract

There are 2 approaches to implement layer 3 network function on virtualization platforms, the first approach uses the conventional physical devices; while the second is software-based. Several previous studies have been carried out to test the performance of L3 function on virtualization using software-based and obtained positive result for the performance over the physical-based. While the previous studies were limited only within the scope of testing environment, this paper tries to extend the study not only limited to the performance test based-on RFC 2544 standard, but also implementation in the production environment using virtual machine (VM) approach. Mikrotik CHR (Cloud Hosted Router) designed specifically for virtualization environment will be used as the L3 platform on the VM. Implementation in the production environment was conducted at University computer laboratory that has 207 desktops (190 in the form of virtual desktops, 17 in the form of PCs) not including user' devices that connected via WiFi networks. VM-based approach for routing functions (Layer 3) using Mikrotik CHR has proven to be stable and sufficient for use in the computer laboratory after 6 months of usage. Performance test also shown that VM-based L3 function had higher transfer rates; physical-based router was about 23,4% slower for 1 routing load and 4,25% slower for 2 routings load. The characteristic of VM itself also add some benefits like VM snapshot and migration for recovery. The test also revealed that VM-based L3 function prone to performance penalties when more than one routing load performed compared with physical-based.

Page 52 of 65 | Total Record : 649

 50   51   52   53   54 

Filter by Year

2014 2020


Reset
Filter By Issues
All Issue Vol 7, No 2: EECSI 2020 Vol 7, No 1: EECSI 2020 Vol 6: EECSI 2019 Vol 5: EECSI 2018 Vol 4: EECSI 2017 Vol 3: EECSI 2016 Vol 2: EECSI 2015 Vol 1: EECSI 2014