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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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Comparison of Maintainability Index Measurement from Microsoft Code Lens and Line of Code Gilang Heru Kencana; Akuwan Saleh; Haryadi Amran Darwito; Rizki Rachmadi; Elsa Mayang Sari
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.v7.2071

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.
Method Using IOT Low Earth Orbit Satellite to Monitor Forest Temperature in Indonesia Ariesta Satryoko; Arthur Josias Simon
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.v7.2072

Abstract

The Purpose of this paper is to ensure the proper functioning of the Monitoring Forest Temperature program in Indonesia using the IoT Narrow-Band Low earth orbit Satellite. As a new technology for monitoring the temperature continue to expand, its implementation in developing countries particularly in Indonesia requires strategic guidance of how the whole process will be executed. Nevertheless, due to this, cross-sectoral partnership in technology, policy, budget, industry is essential to be addressed. The World Bank has recorded the loss from forest fire where 28 million people directly affected including 19 people who died and over 500 thousand people suffered from respiratory problems. Smokes from forest and land fires have also struck Malaysia, Singapore, and Brunei Darussalam respectively. To respond to this, the IoT ( Internet of Things ) now comes with an extensive feature, using the capability of satellite reach. The Narrow Band Low Earth Orbit Satellite has released a feature for IoT connect to Low Orbit Satellite and transmit the data from the sensor directly. Therefore, we argue that this technology is crucial and needs to be functioned immediately to monitor forest temperature in Indonesia.
KM Maturity for A Gas Company in Indonesia: G-KMMM Assessment and Improvement Recommendation Handoko Ramadhan; Majesty Permana; Dana I. Sensuse; 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.v7.2073

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. The governance depends on many things such as organizational structure, human resources and culture, technology, and the company's vision and mission. In order to adjust between company objectives, it is necessary to know the KM maturity index in an organization. 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.
Exploring Success Factor for Mobile based Smart Regency Service using TRUTAUT Model Approach Aang Kisnu Darmawan; Daniel Siahaan; Tony Dwi Susanto; Hoiriyah Hoiriyah; Busro Umam; Anwari Anwari
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.v7.2074

Abstract

Currently, almost every country struggles to apply city management to the concept of intelligent cities. Several previous studies have modeled the success, maturity, and success of information systems to use smart city principles. However, there are significant differences between city and district definition in terms of governance frameworks, regional size, livelihood differences, population, socio-economic, and socio-cultural dimensions. Therefore, work on the Smart District IT assessment requires new and unique studies that can differ substantially from smart cities. This study aims to explore the determinants of the success of Smart Regency services with mobile technology. The model and approach are the TRUTAUT model, which combines the concepts for the TRI and the UTAUT model. Two hundred eighty-nine participants could collect data with a smart cellular district service system - data processing using the SmartPLS v.3.2.8 software. Recent findings indicate that the variables proposed in the TRUTAUT model are a positive and essential relation. This study helps to determine the success of the application of intelligent mobile regional services applications. This study confirms that policymakers pay more considerable attention to critical questions that affect the district's smart cellular services' success.
Intelligent Wheelchair Control System based on Finger Pose Recognition Iswahyudi Yudi; Khairul Anam; Azmi Saleh
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.v7.2075

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.
Collaborative Learning in Virtual Learning Environment using Social Network Analysis Fitria Amastini; Cristin Kaunang; Afifah Nefiratika; Dana Indra Sensuse; Sofian Lusa
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.v7.2076

Abstract

Distance learning is supposed to provide not only independent learning activities but also two-way interaction and collaborative learning based on inquiry model to control students' learning. E-learning is one of the platform to implement two-way interaction and inquiry model. Universitas Terbuka (UT) is the first open distance education university in Indonesia. This paper will study and visualize participation in discussion and interaction on the virtual learning environment (VLE) UT using Social Network Analysis (SNA). This paper also used a questionnaire to detect knowledge sharing behavior (KSB) in the Collaborative Learning Environment (CLE) based on Social Presence, Perceived Online Attachment Motivation, Perceived Online Relationship Commitment, and Altruism indicators. For the perception of students and evaluation about e-learning UT, we use Yilmaz's Transactional Distance. The results of the measurement network in forum discussion can detect that the tutors are most important, and who are mostly reply to other student's posts or which students' post are mostly commented by others. Personal/Informal network shows that students tend to interact only with students on same location registered region office.
A Machine Learning Model on Virtual University of Senegal's Educational Data Based on Lambda Architecture Serigne Mbacke Gueye; Alassane Diop; Amadou Dahirou Gueye
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.v7.2077

Abstract

Nowadays, a new form of learning has emerged in higher education. This is e-Learning. Lessons are taught on a Learning Content Management Systems (LCMS). These platforms generate a large variety of data at very high speed. This massive data comes from the interactions between the system and the users and between the users themselves (Learners, Tutors, Teachers, administrative Agents). Since 2013, UVS (Virtual University of Senegal), a digital university that offers distance learning through Moodle and Blackboard Collaborate platforms, has emerged. In terms of statistics, it has 29340 students, more than 400 active Tutors and 1000 courses. As a result, a large volume of data is generated on its learning platforms. In this article, we have set up an architecture allowing us to execute all types of queries on all data from platforms (historical data and real-time data) in order to set up intelligent systems capable of improving learning in this university. We then set up a machine learning model as a use case which is based on multiple regression in order to predict the most influential learning objects on the learners' final mark according to his learning activities.
Designing Feature Application for User Experience to Censor Inappropriate Scene in Indonesia Ariesta Satryoko; Arthur Josias Simon
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.v7.2078

Abstract

The objective of this article is to build a better and more realistic user interface for Vlarm, an application we developed to censor inappropriate scenes during the COVID-19 pandemic. This premise was built by the fact that most children demand home entertainment, such as watching movies during the quarantine. Thus, this research introduces a new feature in our censorship software that enables parents to exchange their censorship findings so that other parents can use such findings without doing so to the same movie. On the other hand, the Indonesian Government imposes a large-scale social restriction (PSBB) due to the situation of COVID-19 in the country. This interaction is to provide information on the user's experience to eventually build more user-friendly interfaces and features that enable users to conveniently utilize Vlarm application.
Quality in Use of Digital Wallet based on ISO/IEC 25022 Windy Rahmadia Pradanita; Siti Rochimah
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.v7.2079

Abstract

The growth of financial technology (fintech) has led to an increase in cashless transactions. One of the technology that is developing and widely used is digital wallets. Because of the frequent use of digital wallet services, an assessment to measure quality in use needs to be done. Quality in use relates to user interaction with software when the product is used. The assessment standard used to measure quality in use is ISO/IEC 25022. The criteria assessed are effectiveness, efficiency, satisfaction, and freedom from risk. To strengthen the results obtained, a correlation between the existing criteria and the quality in use of digital wallets is sought. From these results, it will be known which criteria have the highest correlation to the quality in use of digital wallets. This study does not focus on assessing the quality in use of each digital wallet, but on digital wallets globally (in this study the digital wallets used are OVO, Gopay, and Dana) because after the results of the questionnaire, almost all respondents use more than one digital wallet, even besides the mentioned digital wallets. The conclusion obtained in this study is that digital wallet product users are satisfied with the use of digital wallets although there are still some risks that may arise.
IoT Botnet Malware Classification Using Weka Tool and Scikit-learn Machine Learning Susanto Susanto; Deris Stiawan; M. Agus Syamsul Arifin; Mohd. Yazid Idris; 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.v7.2080

Abstract

Botnet is one of the threats to internet network security-Botmaster in carrying out attacks on the network by relying on communication on network traffic. Internet of Things (IoT) network infrastructure consists of devices that are inexpensive, low-power, always-on, always connected to the network, and are inconspicuous and have ubiquity and inconspicuousness characteristics so that these characteristics make IoT devices an attractive target for botnet malware attacks. In identifying whether packet traffic is a malware attack or not, one can use machine learning classification methods. By using Weka and Scikit-learn analysis tools machine learning, this paper implements four machine learning algorithms, i.e.: AdaBoost, Decision Tree, Random Forest, and Naïve Bayes. Then experiments are conducted to measure the performance of the four algorithms in terms of accuracy, execution time, and false positive rate (FPR). Experiment results show that the Weka tool provides more accurate and efficient classification methods. However, in false positive rate, the use of Scikit-learn provides better results.

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