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EMITTER International Journal of Engineering Technology
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.
Arjuna Subject : -
Articles 436 Documents
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Web Application Security Education Platform Based on OWASP API Security Project Muhammad Idris; Iwan Syarif; Idris Winarno
EMITTER International Journal of Engineering Technology Vol 10 No 2 (2022)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v10i2.705

Abstract

The trend of API-based systems in web applications in the last few years keeps steadily growing. API allows web applications to interact with external systems to enable business-to-business or system-to-system integration which leads to multiple application innovations. However, this trend also comes with a different surface of security problems that can harm not only web applications, but also mobile and IoT applications. This research proposed a web application security education platform which is focused on the OWASP API security project. This platform provides different security risks such as excessive data exposure, lack of resources and rate-limiting, mass assignment, and improper asset management which cannot be found in monolithic security learning application like DVWA, WebGoat, and Multillidae II. The development also applies several methodologies such as Capture-The-Flag (CTF) learning model, vulnerability assessment, and container virtualization. Based on our experiment, we are successfully providing 10 API vulnerability challenges to the platform with 3 different levels of severity risk rating which can be exploited using tools like Burp Suite, SQLMap, and JWTCat. In the end, based on our performance experiment, all of the containers on the platform can be deployed in approximately 16 seconds with minimum storage resource and able to serve up to 1000 concurrent users with the average throughput of 50.58 requests per second, 96.35% successful requests, and 15.94s response time.
Numerical Study of a Wind Turbine Blade Modification Using 30° Angle Winglet on Clark Y Foil Nu Rhahida Arini; Gilang Muhammad; Joke Pratilastiarso; Setyo Nugroho
EMITTER International Journal of Engineering Technology Vol 10 No 2 (2022)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v10i2.680

Abstract

The depletion of fossil fuels and the worsening environment motivate engineers and researchers to explore renewable energy resources. One of the promising renewable energy is wind energy. The wind turbine extracts wind energy to generate electricity. This study aims to modify a wind turbine blade using Clark Y foil to improve the lift force. The modification is employed by forming a winglet profile with a 30° angle on the foils tip. The result shows that the 30° winglet enlarges the lift coefficient to 1.3253 from 1.2795 of the original blade lift coefficient.
Omnidirectional Stereo Vision Study from Vertical and Horizontal Stereo Configuration Husein Aji Pratama; Bima Sena Bayu Dewantara; Dadet Pramadihanto
EMITTER International Journal of Engineering Technology Vol 10 No 2 (2022)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v10i2.700

Abstract

In stereo vision, an omnidirectional camera has high distortion compared to a standard camera, so the camera calibration is very decisive in its stereo matching. In this study, we will perform stereo matching for an omnidirectional camera with vertical and horizontal configuration so that the result of the image's depth has a 360-degree field of view by transforming the image using a calibration-based method. The result is that by using a vertical camera configuration, the image can be stereo matched directly, but by configuring a horizontal image, it is necessary to carry out a different stereo-matching process in each direction. Stereo matching with the semi-global matching method has better image results than block matching with more image objects detectable by the semi-global block matching method with a maximum disparity value of 32 pixels and with a window size of 21 pixels.
Investigation in Gas-Oil Two-Phase Flow using a Differential Pressure Transducer and Wire Mesh Sensor in Vertical Pipes Veyan A. Musa; Raid A. Mahmood; Sherwan M. Simo; Abbas Kh. Ibrahim; Lokman A. Abdulkareem
EMITTER International Journal of Engineering Technology Vol 10 No 2 (2022)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v10i2.703

Abstract

The current study is performed to identify the flow regimes of oil-gas two-phase flow experimentally in a vertical pipe has an internal diameter of 6.7 cm. It also aims to provide more details about the possibility of using Differential Pressure Transducers (DPT) for indicating flow patterns. A flow development of oil and gas has been investigated in a vertical pipe of 6 m in length and operated at atmospheric pressure. A series of experiments have been run to cover a range of inlet oil superficial velocities from 0.262 to 0.419 m/s, and inlet gas superficial velocities from 0.05 to 4.7 m/s. Wire Mesh Sensors (WMS) have been used to collect the obtained void fraction values of the flow. The Differential Pressure Transducer (DPT) is utilized to measure the pressure drop values of a one-meter along the pipe. The flow patterns are classified according to the analysis of void fractions, pressure gradients regarding time series, tomographic images, probability density functions of the void fractions, and pressure gradients. A bubbly flow is observed at low superficial velocities of gas and liquid, slug flow is observed at the lower flow rate of liquid and moderate flow rates of gas, while the churn flow pattern is recognized at the higher rates of liquid and gas. Also, the result has revealed the possibility of using Differential Pressure Transducers (DPT) to classify the gas-oil flow patterns in vertical pipes.
3D Visualization for Lung Surface Images of Covid-19 Patients based on U-Net CNN Segmentation FX Ferdinandus; Esther Irawati Setiawan; Eko Mulyanto Yuniarno; Mauridhi Hery Purnomo
EMITTER International Journal of Engineering Technology Vol 10 No 2 (2022)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v10i2.709

Abstract

The Covid-19 infection challenges medical staff to make rapid diagnoses of patients. In just a few days, the Covid-19 virus infection could affect the performance of the lungs. On the other hand, semantic segmentation using the Convolutional Neural Network (CNN) on Lung CT-scan images had attracted the attention of researchers for several years, even before the Covid-19 pandemic. Ground Glass Opacity (GGO), in the form of white patches caused by Covid-19 infection, is detected inside the patient’s lung area and occasionally at the edge of the lung, but no research has specifically paid attention to the edges of the lungs. This study proposes to display a 3D visualization of the lung surface of Covid-19 patients based on CT-scan image segmentation using U-Net architecture with a training dataset from typical lung images. Then the resulting CNN model is used to segment the lungs of Covid-19 patients. The segmentation results are selected as some slices to be reconstructed into a 3D lung shape and displayed in 3D animation. Visualizing the results of this segmentation can help medical staff diagnose the lungs of Covid-19 patients, especially on the surface of the lungs of patients with GGO at the edges. From the lung segmentation experiment results on ten patients in the Zenodo dataset, we have a Mean-IoU score = of 76.86%, while the visualization results show that 7 out of 10 patients (70%) have eroded lung surfaces. It can be seen clearly through 3D visualization.
Technical Analysis Based Automatic Trading Prediction System for Stock Exchange using Support Vector Machine I Made Akira Ivandio Agusta; Aliridho Barakbah; Arna Fariza
EMITTER International Journal of Engineering Technology Vol 10 No 2 (2022)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v10i2.740

Abstract

Stock exchange trading has been utilized to gain profit by constantly buying and selling best-performing stocks in a short term. Deep knowledge, time dedication, and experience are essential for optimizing profit if stock price fluctuations are analyzed manually. This research proposes a new trading prediction system that has the ability to automatically predict the accurate time for buying and selling stock using a combination of technical analysis and support vector machine (SVM). Technical analysis is used to analyze stock price fluctuation based on historical data by utilizing technical indicators such as moving average, Bollinger bands, relative strength index, stochastic oscillator, and Aroon oscillator. SVM maps inputs into higher dimensional spaces using non-linear kernel functions, making it suitable for various technical indicators implementation as inputs in stock trading prediction. Experimentation on five Indonesian stocks reveals that the combination of technical analysis and support vector machine is best suited for continuously fluctuated stocks, with the highest accuracy of 77.8%.
Estimation of Confidence in the Dialogue based on Eye Gaze and Head Movement Information Cui Dewen; Matsufuji Akihiro; Liu Yi; Eri Sato- Shimokawa; Toru Yamaguchi
EMITTER International Journal of Engineering Technology Vol 10 No 2 (2022)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v10i2.756

Abstract

In human-robot interaction, human mental states in dialogue have attracted attention to human-friendly robots that support educational use. Although estimating mental states using speech and visual information has been conducted, it is still challenging to estimate mental states more precisely in the educational scene. In this paper, we proposed a method to estimate human mental state based on participants’ eye gaze and head movement information. Estimated participants’ confidence levels in their answers to the miscellaneous knowledge question as a human mental state. The participants’ non-verbal information, such as eye gaze and head movements during dialog with a robot, were collected in our experiment using an eye-tracking device. Then we collect participants’ confidence levels and analyze the relationship between human mental state and non-verbal information. Furthermore, we also applied a machine learning technique to estimate participants’ confidence levels from extracted features of gaze and head movement information. As a result, the performance of a machine learning technique using gaze and head movements information achieved over 80 % accuracy in estimating confidence levels. Our research provides insight into developing a human-friendly robot considering human mental states in the dialogue.
Experimental Study of Hydroformed Al6061T4 Elliptical Tube Samples under Different Internal Pressures Md. Meraz; Santosh Kumar; Ravi Prakash Singh
EMITTER International Journal of Engineering Technology Vol 10 No 2 (2022)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v10i2.699

Abstract

In order to achieve crack free elliptical shape under controlled conditions, an experimental set-up was designed and fabricated. This setup consists of three hydraulic cylinders, an intensifier, a hydraulic power pack, storage tanks, forming die, and all parts are controlled by a Programmable Logic Controller (PLC) system. The elliptical samples can be achieved through proper control of internal pressure and axial force with proper sealing. Experimental work has been carried out with different magnitudes of internal pressure and constrained conditions of axial force. Initially die of elliptical shape has been designed and modeled in Abaqus to successfully achieve the particular shape of the Al6061T4 tube under different internal pressure. The fabricated tube hydroforming machine set-up is highly effective for forming 0.5 mm-2 mm thick Al6061T4 alloy tube samples. The Experimental test has been carried out at 12.7 mm outer diameter, 175 mm length and 0.5 mm thick Al6061T4 samples. Bulge height parameters measured at different points of regular distance gap on the axial direction of the tube length and corner radius found at different pressures range of the samples are plotted under different internal pressures. Samples having an 18.7 mm major elliptical bulge were achieved during the experiment. The experimental data was validated by simulation results.
Hardware Trojan Detection and Mitigation in NoC using Key authentication and Obfuscation Techniques Thejaswini P; Vivekananda G; Anu H; Priya R; Krishna Prasad B S; Nischay M
EMITTER International Journal of Engineering Technology Vol 10 No 2 (2022)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v10i2.716

Abstract

Today's Multiprocessor System-on-Chip (MPSoC) contains many cores and integrated circuits. Due to the current requirements of communication, we make use of Network-on-Chip (NoC) to obtain high throughput and low latency. NoC is a communication architecture used in the processor cores to transfer data from source to destination through several nodes. Since NoC deals with on-chip interconnection for data transmission, it will be a good prey for data leakage and other security attacks. One such way of attacking is done by a third-party vendor introducing Hardware Trojans (HTs) into routers of NoC architecture. This can cause packets to traverse in wrong paths, leak/extract information and cause Denial-of-Service (DoS) degrading the system performance. In this paper, a novel HT detection and mitigation approach using obfuscation and key-based authentication technique is proposed. The proposed technique prevents any illegal transitions between routers thereby protecting data from malicious activities, such as packet misrouting and information leakage. The proposed technique is evaluated on a 4x4 NoC architecture under synthetic traffic pattern and benchmarks, the hardware model is synthesized in Cadence Tool with 90nm technology. The introduced Hardware Trojan affects 8% of packets passing through infected router. Experimental results demonstrate that the proposed technique prevents those 10-15% of packets infected from the HT effect. Our proposed work has negligible power and area overhead of 8.6% and 2% respectively.
Federated Learning Framework for IID and Non-IID datasets of Medical Images Srinivasan, Kavitha; Prasanna, Sainath; Midha, Rohit; Mohan, Shraddhaa
EMITTER International Journal of Engineering Technology Vol 11 No 1 (2023)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v11i1.742

Abstract

Advances have been made in the field of Machine Learning showing that it is an effective tool that can be used for solving real world problems. This success is hugely attributed to the availability of accessible data which is not the case for many fields such as healthcare, a primary reason being the issue of privacy. Federated Learning (FL) is a technique that can be used to overcome the limitation of availability of data at a central location and allows for training machine learning models on private data or data that cannot be directly accessed. It allows the use of data to be decoupled from the governance (or control) over data. In this paper, we present an easy-to-use framework that provides a complete pipeline to let researchers and end users train any model on image data from various sources in a federated manner. We also show a comparison in results between models trained in a federated fashion and models trained in a centralized fashion for Independent and Identically Distributed (IID) and non IID datasets. The Intracranial Brain Hemorrhage dataset and the Pneumonia Detection dataset provided by the Radiological Society of North America (RSNA) are used for validating the FL framework and comparative analysis.

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