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EMITTER International Journal of Engineering Technology

EMITTER
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Published by Politeknik Elektronika Negeri Surabaya

ISSN : 2355391x EISSN : - DOI : -

Core Subject : Science,

Computer Science & IT

EMITTER International Journal of Engineering Technology is a BI-ANNUAL journal published by Politeknik Elektronika Negeri Surabaya (PENS). It aims to encourage initiatives, to share new ideas, and to publish high-quality articles in the field of engineering technology and available to everybody at no cost. It stimulates researchers to explore their ideas and enhance their innovations in the scientific publication on engineering technology. EMITTER International Journal of Engineering Technology primarily focuses on analyzing, applying, implementing and improving existing and emerging technologies and is aimed to the application of engineering principles and the implementation of technological advances for the benefit of humanity.

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Articles 11 Documents

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Search results for , issue "Vol 6 No 1 (2018)" : 11 Documents clear

Botnet Detection Using On-line Clustering with Pursuit Reinforcement Competitive Learning (PRCL) Yesta Medya Mahardhika; Amang Sudarsono; Ali Ridho Barakbah
EMITTER International Journal of Engineering Technology Vol 6 No 1 (2018)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Botnet is a malicious software that often occurs at this time, and can perform malicious activities, such as DDoS, spamming, phishing, keylogging, clickfraud, steal personal information and important data. Botnets can replicate themselves without user consent. Several systems of botnet detection has been done by using classification methods. Classification methods have high precision, but it needs more effort to determine appropiate classification model. In this paper, we propose reinforcedÂ approach to detect botnet with On-line Clustering using Reinforcement Learning. Reinforcement Learning involving interaction with the environment and became new paradigm in machine learning. The reinforcement learning will be implemented with some rule detection, because botnet ISCX dataset is categorized as unbalanced dataset which have high range of each number of class. Therefore we implemented Reinforcement Learning to Detect Botnet using Pursuit Reinforcement Competitive Learning (PRCL) with additional rule detection which has reward and punisment rules to achieve the solution. Based on the experimental result, PRCL can detect botnet in real time with highÂ accuracy (100% for Neris, 99.9% for Rbot, 78% for SMTP_Spam, 80.9% for Nsis, 80.7% for Virut, and 96.0% for Zeus) and fast processing time up to 176 ms. Meanwhile the step of CPU and memory usage which are 78 % and 4.3 GBÂ for pre-processing, 34% and 3.18 GB for online clustering with PRCL, and Â 23% and 3.11 GB evaluation. The proposed method is one solution for network administrators to detect botnet which has unpredictable behavior in network traffic.

Automatic Abstractive Summarization Task for New Article Afrida Helen
EMITTER International Journal of Engineering Technology Vol 6 No 1 (2018)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Understanding the contents of numerous documents requires strenuous effort. While manually reading the summary or abstract is one way, automatic summarization offers more efficient way in doing so. The current research in automatic summarization focuses on the statistical method and the Natural Processing Language (NLP) method. Statistical method produce Extractive summary that the summaries consist of independent sentences considered important content of document. Unfortunately, the coherence of the summary is poor. Besides that, the Natural Processing Language expected can produces summary where sentences in summary should not be taken from sentences in the document, but come from the person making the summary. So, the summaries closed to human-summary, coherent and well structured. This study discusses the tasks of generating summary. The conclusion is we can find that there are still opportunities to develop better outcomes that are better coherence and better accuracy.

Walking Trajectory Optimization Algorithm For Robot Humanoid on Synthetic Grass Dimas Pristovani Riananda; Ardik Wijayanto; Ali Husein Alasiry; A. Subhan Khalilullah
EMITTER International Journal of Engineering Technology Vol 6 No 1 (2018)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Synthetic grass surface is a new rule in international robot soccer competition (RoboCup). The main issue in the development of the RoboCup competition today is about how to make a humanoid robot walk above the field of synthetic grass. Because of that, the humanoid robot needs a system that can be implemented into the walking algorithm. This paper describes how to maintain the stability of humanoid robot called EROS by using walking trajectory algorithm without a control system. The establishment of the walking trajectory system is combined with a process of landing optimization using deceleration and heel-strikes gait optimization. This system has been implemented into a humanoid robot with 52 cm of height and walking on synthetic grass with different speeds. By adding optimization, the robot walks more stable from 32% to 80% of stability. In the next research, the control system will be added to improve the stability.

An Embedding Technique for Language-Independent Lecturer-Oriented Program Visualization Lisan Sulistiani; Oscar Karnalim
EMITTER International Journal of Engineering Technology Vol 6 No 1 (2018)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Nowadays, programming is a promising skill to be learned; the demand of programmer is increased. To align with such trend, several Program Visualization (PV) tools have been developed. Using such tool, user can learn how a particular program works through interactive and descriptive visualization. However, most of the tools are language-dependent: they use either language-dependent debugger or code to generate visualization. Such dependency may become a problem when a program written in new programming language is incorporated. Therefore, this paper proposes an embedding technique to handle given issue. To incorporate new programming language, it only needs five language-dependent features to be set. In general, our proposed technique works in threefold: embedding some statements to target program, generating visualization states by running the program with console commands, and visualizing given program based on generated visualization states. According to our evaluation, proposed technique is able to incorporate program written in any programming languages as long as those languages provide required language-dependent features. Further, it is practical to be used since it still have the benefits of conventional PV even though it is designed as a language-independent PV.

Improve of Water Flow Acceleration in Darrieus Turbine Using Diffuser NACA 11414 2,5R Setyo Nugroho; Arrad Ghani Safitra; Teguh Hady Aribowo; Mochammad Arief Julianto
EMITTER International Journal of Engineering Technology Vol 6 No 1 (2018)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Indonesia has potential hydro energy around 70000 MW which has been used around 6% (3529 MW). One of the development constraint is the stream velocity in Indonesian rivers is relative low. It causes bigger turbine dimension needed to achieve power which is desired. Â An alternative is to utilize adiffuser, which is a device that could accelerate the fluid flow in order to give more energy to the turbine. Based on contiunity equation, diffuser can increase velocity by ratio of cross-section area. It can be Â used to achieve expected power as long as it is not too much reduce the pressure. This research is conducted in 0.566 m/s of water velocity with Darrieus turbine with hydrofoil NACA 0018, height 0.74 m, radius 0.17 m, chord 0.11 m and 3 number of blades. The performance (Cp) was determined by numerical and experimental without and with diffuser NACA 11414 2.5R for variation of angle 8o, 16o, and 20o. Both of those result showed that the best performance of NACA 11414 2,5R is on angle 16o which numerically has stream velocity 0,91 m/s of water and 7 times of Cp, while experimentally has 0,891 m/s of water velocity and 3,16 times of Cp. This diffuser could improve the power generated by the turbine and increase the turbine efficiency.

Underwater Acoustic Channel Characterization of Shallow Water Environment Tri Budi Santoso; Endang Widjiati; Wirawan Wirawan; Gamantyo Hendrantoro
EMITTER International Journal of Engineering Technology Vol 6 No 1 (2018)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Understanding of channel propagation characteristics is a key to the optimal design of underwater acoustic communication. Generally, modelling of underwater acoustic channel is performed based on measurement result in certain site at certain times. Different sites might have different characteristics, each of which can generally be described by a model obtained by averaging measurement results at multiple points in the same environment. This paper describes a characterization of the underwater acoustic channel of tropical shallow water in a Mangrove estuary, which has sediment up to 60 cm at the bottom. Such a channel model is beneficial for the design of communication system in an autonomous underwater vehicle, for instance. The measurement result of delay spread parameter from three different points with the distance of 14 ~ 52 m, has various values. The root mean square (RMS) of delay spread ranges between 0.0621 ~ 0.264 ms, and the maximum delay spread varies with the value of 0.187 ~ 1.0 ms. The pdf fitting shows that Rayleigh distribution describes the fading variation more accurately than Nakagami and Ricean.

The Enhancement of 3 MHz Ultrasonic Echo Signal for Conversion Curve Development for Acoustic Impedance Estimation by Using Wavelet Transform Edo Bagus Prastika; Agus Indra Gunawan; Bima Sena Bayu Dewantara; Naohiro Hozumi; Chandra Edy Prianto
EMITTER International Journal of Engineering Technology Vol 6 No 1 (2018)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Ultrasonic technology has already been used for many applications. Most of them are mainly used for object measurement. Some techniques have been widely applied to particular measurement by utilizing a very specific component. In this research, the previous technique to develop a conversion curve to obtain the acoustic impedance of the target is adopted. Then, we propose a 3 MHz concave shaped ultrasonic transducer for measuring liquids and a confirmation is needed to confirm if the system used is correct. Therefore, several saline solutions which property has been known are used. A low voltage of 10 Volt pulse is used to trigger the transducer. The ultrasonic wave is then transmitted through the multilayered mediums, which is pure water, clear acrylic, and the target. The echo from the interface between the acrylic and the target is then received by the same transducer. Some parameters such as peak and RMS are used to develop the conversion curve. A peak detection and comparison between the original echo and the processed one by using Wavelet transform (UWT and DWT) is then performed. Some analysis of the echo signal by using multiresolution and time-frequency analysis is also proposed. The result obtained from the measurement is then compared to that from the theoretical calculation. Based on the result, in terms of developing the calibration graph, only the RMS value (UWT) which has the closest trend to the result of the calculation, with the mean percentage error of 0.65512%, which is the smallest value among all parameters.

Influence of Logistic Regression Models For Prediction and Analysis of Diabetes Risk Factors Yufri Isnaini Rochmat Maulana; Tessy Badriyah; Iwan Syarif
EMITTER International Journal of Engineering Technology Vol 6 No 1 (2018)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Diabetes is a very serious chronic. Diabetes can occurs when the pancreas doesn't produce enough insulin (a hormone used to regulate blood sugar), cause glucose in the blood to be high. The purpose of this study is to provide a different approach in dealing with cases of diabetes, that's with data mining techniques mengguanakan logistic regression algorithm to predict and analyze the risk of diabetes that is implemented in the mobile framework. The dataset used for data modeling using logistic regression algorithm was taken from Soewandhie Hospital on August 1 until September 30, 2017. Attributes obtained from the Hospital Laboratory have 11 attribute, with remove 1 attribute that is the medical record number so it becomes 10 attributes. In the data preparation dataset done preprocessing process using replace missing value, normalization, and feature extraction to produce a good accuracy. The result of this research is performance measure with ROC Curve, and also the attribute analysis that influence to diabetes using p-value. From these results it is known that by using modeling logistic regression algorithm and validation test using leave one out obtained accuracy of 94.77%. And for attributes that affect diabetes is 9 attributes, age, hemoglobin, sex, blood sugar pressure, creatin serum, white cell count, urea, total cholesterol, and bmi. And for attributes triglycerides have no effect on diabetes.

Stator Flux Estimator Using Feed-Forward Neural Network for Evaluating Hysteresis Loss Curve in Three Phase Induction Motor Bayu Praharsena; Era Purwanto; Arma Jaya; Muhammad Rizani Rusli; Handri Toar; Ridwan wk
EMITTER International Journal of Engineering Technology Vol 6 No 1 (2018)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

The operation of induction motors with high performance contributes significantly to the global energy savings but hysteresis loss is one of the factors causing decreased performance. Stator flux density (B) and magnetic field intensity (H) must be plotted to know hysteresis loss quantity. Unfortunately, since the rotor rotates in time series, the stator flux density is unmeasurable quantities, itâ€™s hard to direct sensored this properties because of limited airgap space and costly to install additional instrument. The purpose of this paper is to evaluate the hysteresis loss quantity in induction motor using a novel method of multilayer perceptron feed forward neural network as stator flux estimator and magnetizing current model as magnetic field intensity properties. This method is effective, because itâ€™s non-destructive method, without an additional instrument, low cost, and suitable for real-time motor drive systems. The FFNN estimator response is satisfying because accurately estimate stator flux density for evaluating hysteresis loss quantity including its magnitude and phase angle. By using the proposed model, the stator flux density and magnetizing current can be plotted become hysteresis loss curve. The performance of flux response, speed response, torque response and error deviation of stator flux estimator has been presented, investigated, compared and verified in Simulink Matlab.

Arrhythmia Classification Using Long Short-Term Memory with Adaptive Learning Rate Hilmy Assodiky; Iwan Syarif; Tessy Badriyah
EMITTER International Journal of Engineering Technology Vol 6 No 1 (2018)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Arrhythmia is a heartbeat abnormality that can be harmless or harmful. It depends on what kind of arrhythmia that the patient suffers. People with arrhythmia usually feel the same physical symptoms but every arrhythmia requires different treatments. For arrhythmia detection, the cardiologist uses electrocardiogram that represents the cardiac electrical activity. And it is a kind of sequential data with high complexity. So the high performance classification method to help the arrhythmia detection is needed. In this paper, Long Short-Term Memory (LSTM) method was used to classify the arrhythmia. The performance was boosted by using AdaDelta as the adaptive learning rate method. As a comparison, it was compared to LSTM without adaptive learning rate. And the best result that showed high accuracy was obtained by using LSTM with AdaDelta. The correct classification rate was 98% for train data and 97% for test data.

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