2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI)最新文献

{"title":"Comparison of Maintainability Index Measurement from Microsoft CodeLens and Line of Code","authors":"Akuwan Saleh","doi":"10.23919/EECSI50503.2020.9251901","DOIUrl":"https://doi.org/10.23919/EECSI50503.2020.9251901","url":null,"abstract":"Higher software quality demands are in line with software quality assurance that can be implemented in every step of the software development process. Maintainability Index is a calculation used to review the level of maintenance of the software. MI has a close relationship with software quality parameters based on Halstead Volume (HV), Cyclomatic Complexity McCabe (CC), and Line of Code (LOC). MI calculations can be carried out automatically with the help of a framework that has been introduced in the industrial world, such as Microsoft Visual Studio 2015 in the form of Code Matric Analysis and an additional software named Microsoft CodeLens Code Health Indicator. Previous research explained the close relationships between LOC and HV, and LOC and CC. New equations can be acquired to calculate the MI with the LOC approach. The LOC Parameter is physically shaped in a software program so that the developer can understand it easily and quickly. The aim of this research is to automate the MI calculation process based on the component classification method of modules in a rule-based C # program file. These rules are based on the error of MI calculations that occur from the platform, and the estimation of MI with LOC classification rules generates an error rate of less than 20% (19.75 %) of the data, both of which have the same accuracy.","PeriodicalId":6743,"journal":{"name":"2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI)","volume":"13 1","pages":"235-239"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89071537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spoken Word and Speaker Recognition Using MFCC and Multiple Recurrent Neural Networks","authors":"Yoga F. Utomo, E. C. Djamal, Fikri Nugraha, F. Renaldi","doi":"10.23919/EECSI50503.2020.9251870","DOIUrl":"https://doi.org/10.23919/EECSI50503.2020.9251870","url":null,"abstract":"Identification of spoken word and speaker has been featured in many kinds of research. The problem or obstacle that persists is in the pronunciation of a particular word. So it is the noise that causes the difficulty of words to be identified. Furthermore, every human has different pronunciation habits and is influenced by several variables, such as amplitude, frequency, tempo, and rhythmic. This study proposed the identification of spoken sounds by using specific word input to determine the patterns of the speaker and spoken using Mel-frequency Cepstrum Coefficients (MFCC) and Multiple Recurrent Neural Networks (RNN). The Mel coefficient of MFCC is used as an input feature for identifying spoken words and speakers using RNN and Long Short Term Memory (LSTM). Multiple RNN works spoken word and speaker in parallel. The results obtained by multiple RNN have an accuracy of 87.74%, while single RNNs have 80.58% using Adam of new data. In order to test our model computational regularly, the experiment tested K-fold Cross-Validation of datasets for spoken and speakers with an average accuracy of 86.07%, which means the model to be able to learn on the dataset without being affected by the order or selection of test data.","PeriodicalId":6743,"journal":{"name":"2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI)","volume":"221 1","pages":"192-197"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89773301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Framework Design for the Retrieval of Instant Messaging in Social Media as Electronic Evidence","authors":"Linda Rosselina, Y. Suryanto, T. Hermawan, Fahdiaz Alief","doi":"10.23919/EECSI50503.2020.9251888","DOIUrl":"https://doi.org/10.23919/EECSI50503.2020.9251888","url":null,"abstract":"The rapid growth of social media features not only brings many advantages but also causes problems. Mainly related to digital evidence when cybercrime occurs. One of the social media features that are currently popular is the unsend message feature in instant messaging applications such as Instagram, Whatsapp, Facebook Messenger, Skype, Viber, and Telegram. In cybercrime, the perpetrator can delete the messages and erase digital evidence, making it difficult to trace. Those artifact messages might be useful for law enforcement or forensic investigators to be used as digital evidence in court. Therefore, an effective and efficient framework is needed to guarantee the data integrity in the mobile forensic investigation process. This paper will discuss the review of several international standards on mobile forensics, namely NIST SP 800–101, ISO/IEC, and SWGDE. This paper also proposes a framework design to retrieve unsend data artifacts on social media according to official and widely used international mobile forensic standards.","PeriodicalId":6743,"journal":{"name":"2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI)","volume":"155 1","pages":"209-215"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79775116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Convolutional Architecture for Block-Based Classification of Small Pulmonary Nodules","authors":"Ahmed Samy Ismaeil, M. A. Salem","doi":"10.23919/EECSI50503.2020.9251305","DOIUrl":"https://doi.org/10.23919/EECSI50503.2020.9251305","url":null,"abstract":"A pulmonary nodule is a small round or oval-shaped growth in the lung. Pulmonary nodules are detected in Computed Tomography (CT) lung scans. Early and accurate detection of such nodules could help in successful diagnosis and treatment of lung cancer. In recent years, the demand for CT scans has increased substantially, thus increasing the workload on radiologists who need to spend hours reading through CT-scanned images. Computer-Aided Detection (CAD) systems are designed to assist radiologists in the reading process and thus making the screening more effective. Recently, applying deep learning to medical images has gained attraction due to its high potential. In this paper, inspired by the successful use of deep convolutional neural networks (DCNNs) in natural image recognition, we propose a detection system based on DCNNs which is able to detect pulmonary nodules in CT images. In addition, this system does not use image segmentation or post-classification false-positive r eduction t echniques which are commonly used in other detection systems. The system achieved an accuracy of 63.49% on the publicly available Lung Image Database Consortium (LIDC) dataset which contains 1018 thoracic CT scans with pulmonary nodules of different shapes and sizes.","PeriodicalId":6743,"journal":{"name":"2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI)","volume":"132 1","pages":"230-234"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75022801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent Wheelchair Control System Based on Finger Pose Recognition","authors":"Iswahyudi, K. Anam, Azmi Saleh","doi":"10.23919/EECSI50503.2020.9251907","DOIUrl":"https://doi.org/10.23919/EECSI50503.2020.9251907","url":null,"abstract":"In the old day, wheelchairs are moved manually by using hand or with the assistance of someone else. Users of this wheelchair get tired quickly if they have to walk long distances. The electric wheelchair emerged as a form of innovation and development for the manual wheelchair. This paper presented the control system of the electric wheelchair based on finger poses using the Convolutional Neural Network (CNN). The camera is used to take pictures of five-finger poses. Images are selected only in certain sections using Region of Interest (ROI). The five-finger poses represent the movement of the electric wheelchair to stop, right, left, forward, and backward. The experimental results indicated that the accuracy of the finger pose detection is about 93.6%. Therefore, the control system using CNN can be a potential solution for an electric wheelchair.","PeriodicalId":6743,"journal":{"name":"2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI)","volume":"33 1","pages":"257-261"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79970979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental Investigation of Algorithms for Simultaneous Localization and Mapping","authors":"T. Zhukabayeva, A. Adamova, Laula Zhumabayeva","doi":"10.23919/EECSI50503.2020.9251883","DOIUrl":"https://doi.org/10.23919/EECSI50503.2020.9251883","url":null,"abstract":"This paper describes a mobile robot system designed for simultaneous localization and mapping. The architecture of a robotic mobile system based on the mini-tractor chassis is considered. The existing and modern methods and approaches to solving the SLAM problem are described, as well as the results of experimental studies of the work of methods on a mobile robot. A description of the developed robotic system for solving the navigation problem and constructing a route map is given. The issues addressed in this paper include the design, development and experimental testing of the mobile robot. The advantages, disadvantages of the algorithm, as well as the direction of further research are described in this work.","PeriodicalId":6743,"journal":{"name":"2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI)","volume":"48 1","pages":"5-9"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83178164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Memory Prediction on Real-Time User Behavior Traffic Detection","authors":"R. Budiarto","doi":"10.23919/eecsi50503.2020.9251295","DOIUrl":"https://doi.org/10.23919/eecsi50503.2020.9251295","url":null,"abstract":"Human brain is a learning system. Human have to learn by getting exposed to something. This capability of learning system to recognize new patterns is called generalization. The abilities of human brain to perform generalization are yet to be matched by neural network or even by any of artificial intelligence algorithm in general. Thus, the need for new machine intelligence approach is imperative. Neural network is designed to take advantages of the speed of computers to solve engineering and computational complex problems intelligently. On the other hand, human brain is somewhat not computationally powerful. Human brain is not even able to calculate quadratic problems within milliseconds. Instead, it uses its vast amounts of memory to store everything human know and have learned. According to a modern neuroscience theory named memory-prediction framework, introduced by Hawkins and Blakeslee in 2005, human brain uses this memory-based model to make continuous predictions of future events. Therefore, a hybrid approach that possesses the ability to compute like neural network and at the same time think like human brain will shed some light in the advancement of machine learning research as well as the development of a truly intelligent machine. This talk discusses the memory-prediction framework and proposes simplified single cell assembled sequential hierarchical memory (s-SCASHM) model instead of hierarchical temporal memory (HTM) in order to speed up the learning convergence. s-SCASHM consists of single neuronal cell (SNC) model and simplified sequential hierarchical superset (SHS) platform. The SHS platform is designed by simplifying to have a region with four rows columnar architecture instead of having six rows per region as in human neocortex. Then, the s-SCASHM is implemented as the prediction engine of user behavior analysis tool to detect insider attacks/anomalies. As nearly half of incidents in enterprise security triggered by the Insider, it is important to deploy more intelligent defense system to assist the enterprise be able to pinpoint and resolve any incidents caused by the Insider or malicious software (malware). The attacks evolve; however, current detection systems that use the deep learning techniques cannot perform online (on-the-fly) learning. Thus, an intelligent detection system with on-the-fly learning capability is required. Experimental results show that the proposed memory model is able to predict user behavior traffic with significant level of accuracy and performs on-the-fly learning.","PeriodicalId":6743,"journal":{"name":"2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI)","volume":"79 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83140510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Steering System of Electric Vehicle using Extreme Learning Machine","authors":"Sofyan Ahmadi, K. Anam, Azmi Saleh","doi":"10.23919/EECSI50503.2020.9251889","DOIUrl":"https://doi.org/10.23919/EECSI50503.2020.9251889","url":null,"abstract":"The development of electric vehicle technology is currently increasing and growing very fast. Some efforts have been conducted, one of which is using BLDC (brushless direct current) motors to improve efficiency. This study utilized extreme learning machine (ELM) embedded on the microcontroller as well as the differential method for controlling the rotational speed of the BLDC motor. The experimental results on the acceleration testing by traveling a distance of 200 meters achieved the average current of 1.09 amperes. The average power efficiency test is 104 watts. Furthermore, the results of the efficiency experiment with a track length of 3.3 km (kilometers) in 10 minutes obtained the energy efficiency of 177.34 km / kWh (kilowatt for one hour).","PeriodicalId":6743,"journal":{"name":"2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI)","volume":"53 8 1","pages":"105-108"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83406024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"KM Maturity for A Gas Company in Indonesia: G-KMMM Assessment and Improvement Recommendation","authors":"Handoko Ramadhan, Majesty Eksa Permana, D. I. Sensuse, Sofian Lusa, Damayanti Elisabeth","doi":"10.23919/EECSI50503.2020.9251885","DOIUrl":"https://doi.org/10.23919/EECSI50503.2020.9251885","url":null,"abstract":"Knowledge is an intellectual asset owned by each organization that greatly influences the performance of the organization. Knowledge management, tacit knowledge, and explicit knowledge in an organization become crucial for the organization's sustainability. In order to adjust between company objectives, it is necessary to know the KM maturity index in an organization. Knowledge Management (KM) is a science that focuses on knowledge initiatives by collecting, storing, and applying knowledge. The governance depends on many things such as organizational structure, human resources and culture, technology, and the company's vision and mission. So based on the maturity index, the organization can prepare and adjust company conditions based on the target to be achieved. Knowledge Management (KM) has helped many companies or organizations in developing companies or their organizations, especially for the oil and gas industry. In this study, the authors used the G-KMMM method to conduct KM assessments and provide recommendations for increasing KM at an oil and gas company in Indonesia. From the KM assessment results using the G-KMMM method, it was found that KM in that company is at the awareness level. These results are obtained by considering aspects of people, processes, and technology.","PeriodicalId":6743,"journal":{"name":"2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI)","volume":"53 1","pages":"244-249"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88763550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classification of Post-Stroke EEG Signal Using Genetic Algorithm and Recurrent Neural Networks","authors":"Ella Wahyu Guntari, E. C. Djamal, Fikri Nugraha, Sandi Lesmana Liemanjaya Liem","doi":"10.23919/EECSI50503.2020.9251296","DOIUrl":"https://doi.org/10.23919/EECSI50503.2020.9251296","url":null,"abstract":"Stroke is caused by a sudden burst of blood vessels in the brain, causing speech difficulties, memory loss, and also paralysis. The identification of electrical activity in the brain of post-stroke patients from EEG signals is an attempt to evaluate rehabilitation. EEG signal recording involves multiple channels with overlapping information. Therefore the importance of channel optimization is to reduce processing time and reduce the computational burden. Besides, that channel optimization can have an overfitting effect due to excessive utilization of EEG channels. This paper proposed the optimization of EEG channels for the identification of poststroke patients using Genetic Algorithms and Recurrent Neural Networks. Data was taken from 75 subjects with a recording duration of 180 seconds in a seated state. The data was segmented and extracted using Wavelet to get the frequency of the Alpha, Theta, Mu, Delta, and Amplitude changes. The next step is the channel optimization process using Genetic Algorithms. The method applied to get a combination of channels that qualifies. Then, the EEG signal identification proceeds of the optimization of the channels used Recurrent Neural Network. The result showed that applying the Genetic Algorithm afforded 12 channels configuration with 90.00% of accuracy; meanwhile, used all channels gave a 72.22% result. Therefore, channel optimization is essential to reduce redundancy and increase recognition.","PeriodicalId":6743,"journal":{"name":"2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI)","volume":"16 1 1","pages":"156-161"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78169008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}