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INDONESIA
Proceeding of the Electrical Engineering Computer Science and Informatics
Published by IAES Indonesia Section and Partners
ISSN : -     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 719 Documents
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Aggressive driving behaviour classification using smartphone's accelerometer sensor Sonbhadra, Sanjay Kumar; Agarwal, Sonali; Syafrullah, Muhammad; Adiyarta, Krisna
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.v7.2091

Abstract

Aggressive driving is the most common factor of road accidents, and millions of lives are compromised every year. Early detection of aggressive driving behaviour can reduce the risks of accidents by taking preventive measures. The smartphone's accelerometer sensor data is mostly used for driving behavioural detection. In recent years, many research works have been published concerning to behavioural analysis, but the state of the art shows that still, there is a need for a more reliable prediction system because individually, each method has it's own limitations like accuracy, complexity etc. To overcome these problems, this paper proposes a heterogeneous ensemble technique that uses random forest, artificial neural network and dynamic time wrapping techniques along with weighted voting scheme to obtain the final result. The experimental results show that the weighted voting ensemble technique outperforms to all the individual classifiers with average marginal gain of 20%.
Design of Regenerative Damper for Energy Harvester in Playground Seesaw Ramadhan, Reyhan; Haq, Muhammad Ul; Ekawati, Estiyanti; Prakosa, Tri
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.v7.2092

Abstract

Increasing demand for electricity, coupled with a greater understanding of the environmental impact of conventional power generation, has led to growing research interest on alternative energy sources. Energy harvesters based on playground equipment, such as the seesaw, has been proposed as an alternative method to generate electrical power. In this study, a new harvesting mechanism based on the electromagnetic regenerative damper is proposed as an alternative method to harness energy from a playground seesaw. The proposed design is intended for higher power output and efficiency, smaller dimensions, and ease of installation on a seesaw. Lab tests have been carried out to characterize the proposed design experimentally. The energy harvesting (stroke velocity-to-voltage) coefficient for the proposed seesaw-based energy harvester is obtained as 73.18 V/(ms -1 ). The regenerative damper is capable of producing up to 110 mW of power at 9.34% efficiency.
SCADA Solution by Installing DTM6000 and Trunking Tier Three Siregar, Marsul; Mustikaswara, Ikar; Wahyudi, Dhian; Nur, Tajuddin
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.v7.2093

Abstract

The aim of this study is to provide the new solution for the SCADA system by using the DTM6000 with Tier three and the multipoint node topology for the application offshore mining industry. The application of the proposed design can provide the data as wind speed, machine temperature, sonar, vessel position. Applying the SCADA Software, NMS (network management system), and DWS (Dispatch Work Station), data can be monitored through the control Center. The SCADA system with radiofrequency can cover a wide coverage area (ie up to 60 km). The trunking base station, carry out the data, and able to transmit voice communication with good quality. The technical design and the installation as well as the trial for validation of the proposed system in the remote area offshore of the mining industry is provided. It is might be stated that the system enables the development and addition of more other SCADA equipment in the future
Optimal Sizing of Micro Hydropower to Improve Hybrid Renewable Power System Syafii, Syafii; Laksono, Heru Dibyo; Novizon, Novizon; Fahreza, Rahmad
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.v7.2094

Abstract

This paper presents an analysis of optimal micro hydropower (MH) capacity of hybrid systems to improve renewable energy based power systems. The electricity system was designed by considering river water flow data and solar radiation data at the research location, Andalas University. Optimal results obtained for the configuration of the Grid, MH, and PV with a head height of 30 m and a flow rate of 800 L/s with the lowest COE value of $ 0.065. As an optimal sizing system has been able to increase the composition of renewable energy generation in the Unand electrical network. The renewable energy fraction has increased from 26.4% to 36.5%. Therefore, determining the optimal capacity will increase the use of renewable energy generation. Conversely, an increase in electricity supply from renewable energy plants will reduce electricity consumption from the PLN grid. The latest excess power generation at a low load can be sold to the PLN grid
Potential for Reducing CO2 Emissions in the Operation of Subcritical Power Plants into Supercritical Sunaryo, Sunaryo; Putra, Arinan; Marwanto, Arief; Haddin, Muhamad
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.v7.2095

Abstract

The consumption of electricity that increase anytime, also increases CO2 emissions in the air as a result of coal combustion flue gas at the power plant. The operation of supercritical boilers on the power plant will lead to higher thermal efficiency compared to subcritical boilers. Higher steam pressure boiler will increase the thermal efficiency and automatically reduce CO2 emissions due to a reduction in fuel consumption at the same boiler efficiency and heating value of coal. At 166.9 bar subcritical steam boiler thermal efficiency was 45.47 % and CO2 emissions were 602.2 tons while at supercritical pressure 240 bar, efficiency increased to 47.12 % with a reduction in CO2 emissions of 20.9 tons to 581.3 tons.
Steering System of Electric Vehicle using Extreme Learning Machine Ahmadi, Sofyan; Anam, Khairul; Saleh, Azmi
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.v7.2096

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)
Analysis of Autopsy Mobile Forensic Tools against Unsent Messages on WhatsApp Messaging Application Alief, Fahdiaz; Suryanto, Yohan; Rosselina, Linda; Hermawan, Tofan
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.v7.2097

Abstract

This paper discusses the new feature that is implemented in most social media messaging applications: the unsent feature, where the sender can delete the message he sent both in the sender and the recipient devices. This new feature poses a new challenge in mobile forensic, as it could potentially delete sent messages that can be used as evidence without the means to retrieves it. This paper aims to analyze how well the Autopsy open-source mobile forensics tools in extracting and identifying the deleted messages, both that are sent or received. The device used in this paper is a Redmi Xiaomi Note 4, which has its userdata block extracted using linux command, and the application we're using is WhatsApp. Autopsy will analyze the extracted image and see what information can be extracted from the unsent messages. From the result of our experiment, Autopsy is capable of obtaining substantial information, but due to how each vendor and mobile OS store files and databases differently, only WhatsApp data can be extracted from the device. And based on the WhatsApp data analysis, Autopsy is not capable of retrieving the deleted messages. However it can detect the deleted data that is sent from the device. And using sqlite3 database browser, the author can find remnants of received deleted messages from the extracted files by Autopsy.
Features Extraction on IoT Intrusion Detection System Using Principal Components Analysis (PCA) Sharipuddin, Sharipuddin; Purnama, Benni; Kurniabudi, Kurniabudi; Winanto, Eko Arip; Stiawan, Deris; Hanapi, Darmawijoyo; Idris, Mohd. Yazid; Budiarto, Rahmat
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.v7.2098

Abstract

There are several ways to increase detection accuracy result on the intrusion detection systems (IDS), one way is feature extraction. The existing original features are filtered and then converted into features with lower dimension. This paper uses the Principal Components Analysis (PCA) for features extraction on intrusion detection system with the aim to improve the accuracy and precision of the detection. The impact of features extraction to attack detection was examined. Experiments on a network traffic dataset created from an Internet of Thing (IoT) testbed network topology were conducted and the results show that the accuracy of the detection reaches 100 percent.
Improving the Anomaly Detection by Combining PSO Search Methods and J48 Algorithm Kurniabudi, Kurniabudi; Harris, Abdul; Mintaria, Albertus Edward; Hanapi, Darmawijoyo; Stiawan, Deris; Idris, Mohd. Yazid; Budiarto, Rahmat
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.v7.2099

Abstract

The feature selection techniques are used to find the most important and relevant features in a dataset. Therefore, in this study feature selection technique was used to improve the performance of Anomaly Detection. Many feature selection techniques have been developed and implemented on the NSL-KDD dataset. However, with the rapid growth of traffic on a network where more applications, devices, and protocols participate, the traffic data is complex and heterogeneous contribute to security issues. This makes the NSL-KDD dataset no longer reliable for it. The detection model must also be able to recognize the type of novel attack on complex network datasets. So, a robust analysis technique for a more complex and larger dataset is required, to overcome the increase of security issues in a big data network. This study proposes particle swarm optimization (PSO) Search methods as a feature selection method. As contribute to feature analysis knowledge, In the experiment a combination of particle swarm optimization (PSO) Search methods with other search methods are examined. To overcome the limitation NSL-KDD dataset, in the experiments the CICIDS2017 dataset used. To validate the selected features from the proposed technique J48 classification algorithm used in this study. The detection performance of the combination PSO Search method with J48 examined and compare with other feature selection and previous study. The proposed technique successfully finds the important features of the dataset, which improve detection performance with 99.89% accuracy. Compared with the previous study the proposed technique has better accuracy, TPR, and FPR.

Page 72 of 72 | Total Record : 719

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