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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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Smart Navigation Equipment Monitoring System Muhammad Arif Sulaiman; Trio Adiono
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2061

Abstract

Digital image processing is a processing of digital frames using digital computation. Image processing has been used in many sectors such as military, biomedics, and in this paper, the authors will implement it in the civil aviation sector by introducing a new method to monitor an aviation navigation equipment. It can be used on all LED-based Built-in Monitor navigation equipment, despite it is a low-cost system. The image processing of this research is done by doing perspective correction and then continue with BLOB detection in a segmentation stage. The final result will be displayed on a web page. Compared to its predecessor, this method gives better flexibility which does not need to be electrically connected with monitored equipment and not limited to certain brands.
Security and Privacy for the Internet of Things Biplab Sikdar
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2062

Abstract

The Internet of Things (IoT) represents a great opportunity to connect people, information, and things, which will in turn cause a paradigm shift in the way we work, interact, and think. The IoT is envisioned as the enabling technology for smart cities, power grids, health care, and control systems for critical installments and public infrastructure. This diversity, increased control and interaction of devices, and the fact that IoT systems use public networks to transfer large amounts of data make them a prime target for cyber attacks. In addition, IoT devices are usually small, low cost and have limited resources. Therefore, any protocol designed for IoT systems should not only be secure but also efficient in terms of usage of chip area, energy, storage, and processing. This presentation will start by highlighting the unique security requirements of IoT devices and the inadequacy of existing security protocols and techniques of the Internet in the context to IoT systems. Next, we will focus on security solutions for the IoT, with special focus on protection against physical and side channel attacks. In particular, we will focus on mutual authentication protocols for IoT devices based on security primitives that exploit hardware level characteristics of IoT devices.
Ball and Beam Control using Adaptive PID based on Q-Learning Brilian Putra Amiruddin; Rusdhianto Effendi Abdul Kadir
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2063

Abstract

The ball and beam system is one of the most used systems for benchmarking the controller response because it has nonlinear and unstable characteristics. Furthermore, in line with the increasing of computation power availability and artificial intelligence research intensity, especially the reinforcement learning field, nowadays plenty of researchers are working on a learning control approach for controlling systems. Due to that, in this paper, the adaptive PID controller based on Q-Learning (Q-PID) was used to control the ball position on the ball and beam system. From the simulation result, Q-PID outperforms the conventional PID and heuristic PID controller technique with the swifter settling time and lower overshoot percentage.
Framework Design for the Retrieval of Instant Messaging in Social Media as Electronic Evidence Linda Rosselina; Yohan Suryanto; Tofan Hermawan; Fahdiaz Alief
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2064

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 the case of 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 in the mobile forensic investigation process to guarantee the integrity of the data obtained. 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.
Memory Prediction on Real-Time User Behavior Traffic Detection Rahmat Budiarto
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2065

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.
Experimental Investigation of Algorithms for Simultaneous Localization and Mapping Tamara Zhukabayeva; Aigul Adamova; Laula Zhumabayeva
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2066

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.
An Overview of Knowledge Mapping in Digital Business Industry: A Systematic Literature Review Dana I. Sensuse; Alifiannisa Lawami Diar; Sofian Lusa; Damayanti Elisabeth
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2067

Abstract

The increasing number of studies in the knowledge map shows attention from researchers in academic and professional areas. However, the knowledge map implementation has not effectively implemented in an organization whose business in the digital business industry, especially startup organization. The main reason is the lack of stakeholder's understanding of the knowledge map concept. Thus, this study gives a comprehensive understanding of knowledge map implementation in the digital business industry within the last five years period. The study will answer what problems knowledge map tackled, tools, and techniques used currently, the obstacles and benefits of using a knowledge map. The review was conducted through the structured systematic literature review procedure. It started with a review protocol declaration and ended with an analysis of the prior researches obtained from five credible sources. Only 25 of 775 studies remain after several filtering stages. It is found that a knowledge map is mostly used for decision-making purposes. Most studies adopted a visual knowledge map and concept map, even though it is difficult to align the knowledge depth. In the end, this study's result will help stakeholders to reflect on their existing knowledge relationship structure. This study also offers directions for future research and professional practices in digital business industry firms to perfect their existing organizational intellectual capital through a knowledge map.
Human Related Challenges in Agile Software Development of Government Outsourcing Project Amaliah Khoirun Nisyak; Khairiyah Rizkiyah; Teguh Raharjo
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2068

Abstract

In 2019, a government organization in Indonesia has developed several systems that will run in parallel using Agile by utilizing vendor services. Based on internal project reports, there are indications of human-related issues or challenges during the development process of these systems. The case study is one of the critical systems of failed projects in this government organization. In this study, a Systematic Literature Review (SLR) was used to identify human-related challenges or issues that could lead to failure in an ASD project. These issues or challenges were qualitatively validated based on expert judgment from external and internal organizations by interview and questionnaire. The final results of this study were 20 human-related challenges grouped into 5 categories, which were identified as human-related challenges that led to the failure of the ASD project in this case study. Proposed solutions based on best practices are also provided for each challenge or issue by conducting business research methods with open and axial coding. Besides, the comparison of views between vendors and organizations on human-related challenges as well as the implications of this study are also presented at the end, so that readers can get insight into these challenges.
Practical application of IOT and its implications on the existing software Israa Sadu; Zahraa A. Jaaz; Haider Hadi Abbas; Haider Abdulshaheed
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2069

Abstract

The data management from end-to-end level is done by cloud-assisted IOT for its users and they keep a goal in increasing their number of users with the course of time. From saving the infiltration of data from both internal and external threats to the system, IOT is the best-proposed method used for securing the database. Connecting objects/individuals with the Internet via safe interaction is the main objective of IOT. It can assemble all the hardware devices that are designed to store data for an individual or an organization. The associated applications and the way in which it can be deployed in the present organization in order to optimize the current working system. This paper focuses on providing an overall systematic secured data sharing portal that is devoid of threats from internal as well as external entities. By using CIBPRE data encryption a major security reform is introduced by IOT in storing and sharing of data on a regular basis.
Deep Convolutional Architecture for Block-Based Classification of Small Pulmonary Nodules Ahmed Samy Ismaeil; Mohammed A.-Megeed Salem
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2070

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 reduction techniques 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.

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