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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
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Obtaining Reference's Topic Congruity in Indonesian Publications using Machine Learning Approach Sam F. Chaerul Haviana; Imam Much Ibnu Subroto
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.v6.1971

Abstract

There are some criteria on how an article is categorized as a good article for publications. It could depend on some aspect like formatting and clarity, but mainly it depends on how the content of the article is constructed. The consistency of the topic that the article was written could show us how the authors construct the main idea in the article content. One indication that shows this consistency is congruity in the article’s topic and the topic of literature or reference cited in the document listed in the bibliography. This works attempting to automate the topic detection on the article’s references then obtain the congruity to the article title’s topic through metadata extraction and text classification. This is done by extracting metadata of an article file to obtain all possible reference title using GROBID than classify the topic using a supervised classification model. We found that some refinements in the whole approach should be considered in the next step of this work.
Privacy Control In Social Networks By Trust Aware Link Prediction Syam Prasad Dhannuri; Sanjay Kumar Sonbhadra; Sonali Agarwal; P. Nagabhushan; M. 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.v6.1972

Abstract

Social networks are exceedingly common in today’s society. A social network site is an online platform where people build social relations with others and share information. For the last two decades, rapid growth in the number of users and applications with these social networking sites, make the security as the most challenging issue. In this virtual environment, some greedy people intentionally perform illegal activities by accessing others’ private information. This paper proposes a novel approach to detect the illegal access of a particular’s information by using trustaware link prediction. The facebook dataset is used for experiments and the results justify the robustness andtrustworthiness of the proposed model.
Paraphrase Detection Using Manhattan's Recurrent Neural Networks and Long Short-Term Memory Achmad Aziz; Esmeralda Contessa Djamal; Ridwan Ilyas
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.v6.1973

Abstract

Natural Language Processing (NLP) is a part of artificial intelligence that can extract sentence structures from natural language. Some discussions about NLP are widely used, such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) to summarize papers with many sentences in them. Siamese Similarity is a term that applies repetitive twin network architecture to machine learning for sentence similarity. This architecture is also called Manhattan LSTM, which can be applied to the case of detecting paraphrase sentences. The paraphrase sentence must be recognized by machine learning first. Word2vec is used to convert sentences to vectors so they can be recognized in machine learning. This research has developed paraphrase sentence detection using Siamese Similarity with word2vec embedding. The experimental results showed that the amount of training data is dominant to the new data compared to the number of times and the variation in training data. Obtained data accuracy, 800,000 pairs provide accuracy reaching 99% of training data and 82.4% of new data. These results are better than the accuracy of the new data, with half of the training data only yielding 64%. While the amount of training data did not effect on training data.
Securing IoT Network using Lightweight MultiFog (LMF) Blockchain Model Muhammad Yanuar Ary Saputro
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.v6.1974

Abstract

Security is one of the most important issues in the Internet of Things (IoT). The Mirai botnet case in September 2016 revealed a serious vulnerability in IoT devices. Researchers try to mitigate the issues using several approaches. One of them uses Blockchain for the solution. At first, the integration of the Blockchain on IoT seems promising. However, there are problems in resource consumption and latency. Several solutions emerge to make Blockchain uses low resource consumption i.e., LSB and FogBus. Unfortunately, each solution has its weaknesses. FogBus has a weakness in integrity, whereas LSB has a weakness in its availability when an attack occurs on a broker. We introduce Lightweight Multi-Fog (LMF) Blockchain Model to increase availability in the LSB model. The main idea is increasing the integrity availability by splitting location based on Broadcast Domains while using Fog Computing on each Broadcast Domain. An attack in some Broadcast Domain cannot impact transactions and process in other Broadcast Domain and each Broadcast Domain have its separate transaction and process. LMF enhances the integrity and availability of the Light Blockchain Model. However, it still requires simulations in the future to get a better understanding of LMF performance, resource consumption, and latency
Line Detection Using Arranging Coordinate Point Method Rumaisa Ramadhani; Arief Syaichu Rohman; Yulyan Wahyu Hadi
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.v6.1975

Abstract

Line detection system is a system line that canrecognize the line mark painted on the road. This is one of thedigital driver assistance tools that improve driving safety. Thevideo streaming captured using a camera which is installed infront of the vehicle as the input system will detected the line byusing the Arranging Coordinate Point Algorithm. The systemwill provide the correction value of the vehicle distance from thecenter of the road and guide the driver to stay on the track.Based on the experiment result, the system could detect astraight and curved line. The line is well detected by the systemin good condition such as less of noise of the road, good weather,and clear lane line. The system has a computational process0.0625 fps with average error calculation of position from thecenter of the road is 0.0992 m and standard deviation is 0.62448m.
Decision Support System with Simple Additive Weighting for Recommending Best Employee Painem Painem; Hari Soetanto
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.v6.1976

Abstract

Human resources are significant assets in an organization. To increase work motivation for employees, various strategies are needed, such as giving rewards to employees who excel, giving sanctions to employees who break the rules and training employees. Rewarding for employees at Universitas Budi Luhur (UBL) is still based on the subjective assessment of the leadership. Determination of employees who perform well also has not been based on standard criteria or assessment. Therefore, in this study, a decision support system was developed to conduct the best employee assessment and selection. This study uses the Simple Additive Weighting (SAW) method. The SAW method was chosen because it was able to select the best alternative from several alternatives. Determination of the best employees using nine criteria, namely discipline, appearance, achievement, interpersonal skills, the ability to provide input, does not cause problems, ability to cooperate, coordination skills, and motivating abilities. The test results using the ISO-9126 model for web-based DSS applications developed in this study indicate that the quality of applications is 81%, which means that the criteria are excellent.
An Android-based Hoax Detection for Social Media Harrizki Arie Pradana
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.v6.1977

Abstract

Hoax is defined as a try to convince any readers to believe particular deception. A news frequently spreads on social media. Hoaxes news is engineered to meet a personal purpose and caused by predefined factors. In Indonesia, one of the top discussions is about hoax news related to natural disasters spread through social media. Some people have been sentenced to jail because of making and spreading hoax news. To date, the internet community is unable to distinguish hoax news from the actual news. This is because there is no available tool to detect hoaxes. Therefore, this paper propose an initiative to have a hoax detection application in Android-based devices by using the web scraping technique to extract data downloaded from the inputted URL with a simplified interface. The retrieved data are optimized by utilizing Nazief-Adriani stemming algorithm and analyzed by using Rabin-Karp algorithm
Hybrid Improved Differential Evolution and Splinebased Jaya for Photovoltaic MPPT Technique Khusnul Hidayat; Rini Nur Hasanah; Hadi Suyono
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.v6.1978

Abstract

Some Soft Computing algorithms to solve themaximum power point tracking (MPPT) method problem ofthe photovoltaic system under partially shaded conditions willstop tracking Global Maxima and produce reference voltage orthe best duty-cycle if the difference between the worst and thebest candidate solution is smaller than the specified threshold.A large threshold value will produce fast converging, but theaccuracy value will be low, and vice versa, then thedetermination of the threshold value will be very dilemma.Therefore, this study proposed a combination of ImprovedDifferential Evolution (IDE) and Jaya optimization based onpredictive curves using cubic spline interpolation to determinethe best particles after the IDE reaches convergent criteria, sothat with a large threshold value it will still get high accuracyand high convergent speed. Furthermore, the algorithmproposed in this study is known as Improved DifferentialEvolution and Jaya Based Spline (IDESJaya). The proposedalgorithm is compared with conventional P&O, Jaya based onSpline, and IDE. Simulation results show that the IDESJayatechnique is faster converging, provides a better overalltracking efficiency and higher accuracy.
Optimization Info Rate Using APSK Modulation Scheme for Delivery GSM ABIS over Satellite Communications Hillman Damanik; Merry Anggraeni
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.v6.1979

Abstract

Mobile operators move quickly from 2G GSM networks in urban areas to remote rural areas, which are 2G networks by offering voice connectivity. As a result, more and more technology is optimizing cellular operators that reduce and perform bandwidth efficiency that will be implemented. The optimization solution for this cellular operator produces voice communication on GSM, in a cost-effective application for satellites. This paper discusses and applies to creating GSM links via satellite communication. The ABIS interface on GSM, which is defined between the Base Transceiver Station (BTS) of GSM remote cells and the Base Station Controller (BSC), is considered here to be transferred via GSM communication with the Modulation and Coding scheme 16 APSK 5/6. The MODCOD scheme determines the efficiency of what MHz is needed to send one Mbps. The efficiency value achieved by allocating, bandwidth (MHz) generated by 1.0 Mhz is an efficiency of 3.222 [bit / baud]. And Info Data Rate is generated from the value (Mbps) of 3,175. The highest traffic intensity with the value of Traffic Volume (Hours) = 3.5, Traffic Intensity (Erlang) 0.145833333. While the lowest traffic intensity with the value of Traffic Volume (Hour) = 2.6, traffic intensity = 0.108333333 (Erlang). The value obtained on Traffic Volume and Traffic Intensity is 0.1%. Service levels are very good at grade of service, because of the small possibility of access fail. Calculation of the availability of link network availability links, using ACM 16APSK LDPC 5/6 techniques that can increase up to 100%.
Optimized Fuzzy Backpropagation Neural Network using Genetic Algorithm for Predicting Indonesian Stock Exchange Composite Index Anwar Rifa’i
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.v6.1980

Abstract

Investment activities in the capital market have the possibility to generate profits and at the same time also cause losses. The composite stock price index as an indicator used to determine investment continues to change over time. Uncertainty of stock exchange composite index requires investors to be able to make predictions so as to produce maximum profits. The aim of this study is to forecast the composite stock price index. The input variables used are Indonesia interest rates, rupiah exchange rates, Dow Jones index, and world gold prices. All data obtained in the period from January 2008 to March 2019. Data are used to build the Fuzzy Backpropagation Neural Network (FBPNN), model. The weight of FBPNN model was optimized using Genetic Algorithm then used to forecast the composite stock price index. The forecasting result of the composite stock price index for April to June 2019 respectively were 5822.6, 5826.8, and 5767.3 with the MAPE value of 8.42%. These results indicate that Indonesia interest rates, rupiah exchange rate, Dow Jones index, and the gold price are the proper indicators to predict the composite stock price index.

Page 54 of 65 | Total Record : 649

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