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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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Deep Learning Approaches for Automatic Drum Transcription Cahyaningtyas, Zakiya Azizah; Purwitasari, Diana; Fatichah, Chastine
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.v11i1.764

Abstract

Drum transcription is the task of transcribing audio or music into drum notation. Drum notation is helpful to help drummers as instruction in playing drums and could also be useful for students to learn about drum music theories. Unfortunately, transcribing music is not an easy task. A good transcription can usually be obtained only by an experienced musician. On the other side, musical notation is beneficial not only for professionals but also for amateurs. This study develops an Automatic Drum Transcription (ADT) application using the segment and classify method with Deep Learning as the classification method. The segment and classify method is divided into two steps. First, the segmentation step achieved a score of 76.14% in macro F1 after doing a grid search to tune the parameters. Second, the spectrogram feature is extracted on the detected onsets as the input for the classification models. The models are evaluated using the multi-objective optimization (MOO) of macro F1 score and time consumption for prediction. The result shows that the LSTM model outperformed the other models with MOO scores of 77.42%, 86.97%, and 82.87% on MDB Drums, IDMT-SMT Drums, and combined datasets, respectively. The model is then used in the ADT application. The application is built using the FastAPI framework, which delivers the transcription result as a drum tab.
Performance Analysis of MIMO-OFDM System Using Predistortion Neural Network with Convolutional Coding Addition to Reduce SDR-Based HPA Nonlinearity Gulo, Melki Mario; Astawa, I Gede Puja; Sudarsono, Amang
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.v11i1.791

Abstract

In recent years, the development of communication technology has advanced at an accelerated rate. Communication technologies such as 4G, 5G, Wi-Fi 5 (802.11ac), and Wi-Fi 6 (802.11ax) are extensively used today due to their excellent system quality and extremely high data transfer rates. Some of these technologies incorporate MIMO-OFDM into their protocol. MIMO-OFDM is widely used in modern communication systems due to its benefits, which include high data rates, spectral efficiency, and fading resistance. Despite these benefits, MIMO-OFDM has disadvantages, with the use of a nonlinear HPA being one of them. Nonlinear HPA causes in-band and out-of-band distortions in MIMO-OFDM signals. Utilizing predistortion (PD) is one way of solving this issue. PD is a technique that uses the inverse distortion of the HPA to compensate for the nonlinear characteristics of the HPA. To enhance the quality of MIMO-OFDM systems that the use of HPA has degraded, the convolutional coding (CC) method can be combined with the help of PD. Convolutional coding is a type of channel coding that can be used for error detection and correction. This study will evaluate a combined technique of PD neural networks (PDNN) and CC on the MIMO-OFDM system using Software Defined Radio (SDR) devices. The evaluation of this system led to the use of a technique that combines PDNN and CC to improve SNR and minimise BER on MIMO-OFDM systems that HPA on SDR devices has degraded. In addition, at code rates 1/2, 2/3, and 3/4, using PDNN reduces the SNR value required to achieve BER equal to 0 by 12.037%, 37.8%, and 4.10% when compared to Digital Predistortion (DPD).
Planar Microwave Sensor with High Sensitivity for Material Characterization Based on Square Split Ring Resonator (SSRR) for Solid and Liquid Maizatul Alice Meor Said; Zahriladha Zakaria; Mohamad Harris Misran; Mohd Azlishah bin Othman; Redzuan Abdul Manap; Abd Shukur bin Jaafar; Shadia Suhaimi; Nurmala Irdawaty Hassan
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.v11i1.758

Abstract

Microwave resonator sensors are the most extensively used sensors in the food industries, quality assurance, medical, and manufacturing. Planar resonant technique is chosen as the medium for characterizing dielectric properties of material due to its compact in size, low cost and easy to fabricate. But these techniques have a low Q-factor and little sensitivity. This work uses the perturbation approach to overcome this technique's flaw, which is that Q-factor and resonant frequency are affected by the resonator's dielectric properties. This suggested sensor operated at 2.5GHz between 1GHz and 4GHz for material characterisation of solid and liquid samples. These sensors were constructed on a substrate made of RT/Duroid Roger 5880, which has a copper layer that is 0.0175 mm thick and has a dielectric constant of 2.2. This square split ring resonator (SSRR) sensor thus generates narrower resonant, low insertion loss, and a high Q-factor value of 430 at 2.5GHz. The SSRR sensor's sensitivity is 98.59%, which is higher than that of past studies. The application of the suggested sensor as a tool for material characterisation, particularly for identifying material attributes, is supported by this findings.
An Implementation of blood Glucose and cholesterol monitoring device using non-invasive technique Shubha B; Anuradha M G; Poornima N; Suprada H S; Prathiksha R V
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.v11i1.766

Abstract

Invasive testing of glucose and cholesterol levels in the blood is the most prevalent procedure, which is uncomfortable, expensive, and risky since it can spread infections and harm skin cells. Diabetes and cholesterol are two of the most common diseases in the world, and they require constant monitoring to avoid health issues and organ damage. As a result, a non-invasive approach will allow for more regular testing and painless monitoring. The blood glucose and cholesterol levels can be assessed using the principle of reflecting and refractive properties of NIR light source against blood components. The MAX30100 sensor circuit gives SPO2 (Saturated Peripheral Oxygen Level) and BPM (beats per minute, or heart rate) information to the regression model, which is used to forecast blood glucose and cholesterol levels. The polynomial regression model is trained using preset datasets, and the trained model yields regression co-efficient values. For the fresh sample inputs from the sensor, the co-efficient values are used to estimate the new needed parameter value. The projected blood glucose and cholesterol levels are displayed on the LCD Display and delivered through Bluetooth HC-05 module via Serial communication to the mobile application.
A Combination of Lexicon-based and Distributional Representations for Classification of Indonesian Vaccine Acceptance Rates Suwida, Katon; Kardawi, Muhammad Yusuf; Purwitasari, Diana; Mabahist, Fahril
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.v11i1.768

Abstract

When the COVID-19 pandemic hit, the use of vaccines was advertised as the end of the pandemic by the entire world. However, the chances of vaccination depended on the sentiments of society and individuals about the vaccine. People's acceptance of vaccines can change depending on conditions and events. Social media platforms such as Twitter can be used as a source of information to find out the conditions and attitudes of the community toward the program. By implementing a machine learning technique on the COVID-19 vaccine dataset, we hope to impact the classification result with text. This study suggests three distinct machine learning models for classifying texts of the COVID-19 vaccination, namely a model based on the first lexicon using the feature extraction method; second, using the word insertion technique to utilize distribution representation; and third, a combination model of distribution representation and feature extraction based on the lexicon. From the evaluation that has been carried out, we found that a combination of lexicon-based and distributional representation methods succeeded in giving the best results for classifying the level of acceptance of the COVID-19 vaccine in Indonesia with an accuracy score of 71.44% and an F1-score of 71.43%.
Numerical Analysis of Wave Load Characteristics on Jack-Up Production Platform Structure Using Modified k-ω SST Turbulence Model Gilang Muhammad; Arini, Nu Rhahida; Ilman, Eko Charnius; Ariwibowo, Teguh Hady
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.v11i1.806

Abstract

One of the important stages in the offshore structure design process is the evaluation of the marine hydrodynamic load in which the structure operates, this is to ensure an appropriate design and improve the safety of the structure. Therefore, accurate modeling of the marine environment is needed to produce good evaluation data, one of the methods that can accurately model the marine environment is through the Computational Fluid Dynamic (CFD) method. This research aims to analyze the ocean wave load of pressure and force characteristics on the jack-up production platform hull structure using the (CFD) method. The foam-extend 4.0 (the fork of the OpenFOAM) software with waveFoam solver is utilized to predict the free surface flow phenomena as its capability to predict with accurate results. The Reynold Averaged Navier Stokes (RANS) turbulence model of k-ω SST is applied to predict the turbulence effect in the flow field. Five variations of incident wave direction type are carried out to examine its effect on the pressure and force characteristics on the jack-up production platform hull. The wave model shows inaccurate results with the decrease in wave height caused by excessive turbulence in the water surface area. Excessive turbulence levels can be overcome by incorporating density variable and buoyancy terms based on the Standard Gradient Diffusion Hypothesis (SGDH) into the turbulent kinetic energy equation. The k-ω SST Buoyancy turbulence model shows accurate results when verified to predict wave run-up and horizontal force loads on monopile structures. Furthermore, test results of the wave load on the jack-up production platform hull structure shows that the most significant wave load is obtained in variations with the wave arrival direction relatively opposite to the platform wall. Especially in the direction of 90° because it also has the most expansive impact surface area. Meanwhile, the lower wave load is obtained in variations 45° and 135°, which have the relatively oblique direction of wave arrival to the surface.
The Network Slicing and Performance Analysis of 6G Networks using Machine Learning Mahesh H. B; Ali Ahammed G. F; Usha S. M
EMITTER International Journal of Engineering Technology Vol 11 No 2 (2023)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v11i2.772

Abstract

6G technology is designed to provide users with faster and more reliable data transfer as compared to the current 5G technology. 6G is rapidly evolving and provides a large bandwidth, even in underserved areas. This technology is extremely anticipated and is currently booming for its ability to deliver massive network capacity, low latency, and a highly improved user experience. Its scope is immense, and it’s designed to connect everyone and everything in the world. It includes new deployment models and services with extended user capacity. This study proposes a network slicing simulator that uses hardcoded base station coordinates to randomly distribute client locations to help analyse the performance of a particular base station architecture. When a client wants to locate the closest base station, it queries the simulator, which stores base station coordinates in a K-Dimensional tree. Throughout the simulation, the user follows a pattern that continues until the time limit is achieved. It gauges multiple statistics such as client connection ratio, client count per second, Client count per slice, latency, and the new location of the client. The K-D tree handover algorithm proposed here allows the user to connect to the nearest base stations after fulfilling the required criteria. This algorithm ensures the quality requirements and decides among the base stations the user connects to.
Analytical Analysis of Flexible Microfluidic Based Pressure Sensor Based on Triple-Channel Design Jim Lau Tze Ho; Mat Nawi, Mohd Norzaidi; Mohamad Faizal Abd Rahman
EMITTER International Journal of Engineering Technology Vol 11 No 2 (2023)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v11i2.798

Abstract

In designing a flexible microfluidic-based pressure sensor, the microchannel plays an important role in maximizing the sensor's performance. Similarly, the material used for the sensor's membrane is crucial in achieving optimal performance. This study presents an analytical analysis and FEA simulation of the membrane and microchannel of the flexible pressure sensor, aimed at optimizing it design and material selection. Different types of materials, including two commonly used polymers, Polyimide (PI) and Polydimethylsiloxane (PDMS) were evaluated. Moreover, different designs of the microchannel, including single-channel, double-channel, and triple-channel, were analyzed. The applied pressure, width of the microchannel, and length of the microchannel were varied to study the normalized resistance of the microchannel and maximize the performance of the pressure sensor. The results showed that the triple-channel design produced the highest normalized resistance. To achieve maximum performance, it is found that using a membrane with a large area facing the applied pressure was optimal in terms of dimensions. In conclusion, optimizing the microchannel and membrane design and material selection is crucial in improving the overall performance of flexible microfluidic-based pressure sensors.
KFREAIN: Design of A Kernel-Level Forensic Layer for Improving Real-Time Evidence Analysis Performance in IoT Networks Shukla, Seema; Mangesh, Sangeeta; Chhabra, Prachi
EMITTER International Journal of Engineering Technology Vol 11 No 2 (2023)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v11i2.804

Abstract

An exponential increase in number of attacks in IoT Networks makes it essential to formulate attack-level mitigation strategies. This paper proposes design of a scalable Kernel-level Forensic layer that assists in improving real-time evidence analysis performance to assist in efficient pattern analysis of the collected data samples. It has an inbuilt Temporal Blockchain Cache (TBC), which is refreshed after analysis of every set of evidences. The model uses a multidomain feature extraction engine that combines lightweight Fourier, Wavelet, Convolutional, Gabor, and Cosine feature sets that are selected by a stochastic Bacterial Foraging Optimizer (BFO) for identification of high variance features. The selected features are processed by an ensemble learning (EL) classifier that use low complexity classifiers reducing the energy consumption during analysis by 8.3% when compared with application-level forensic models. The model also showcased 3.5% higher accuracy, 4.9% higher precision, and 4.3% higher recall of attack-event identification when compared with standard forensic techniques. Due to kernel-level integration, the model is also able to reduce the delay needed for forensic analysis on different network types by 9.5%, thus making it useful for real-time & heterogenous network scenarios.
Development of a Mobile Application for Plant Disease Detection using Parameter Optimization Method in Convolutional Neural Networks Algorithm Alwan Fauzi; Iwan Syarif; Tessy Badriyah
EMITTER International Journal of Engineering Technology Vol 11 No 2 (2023)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v11i2.808

Abstract

Plant diseases are a serious problem in agriculture that affects both the quantity and quality of the harvest. To address this issue, authors developed a mobile software capable of detecting diseases in plants by analyzing their leaves using a smartphone camera. This research used the Convolutional Neural Networks (CNN) method for this purpose. In the initial experiments, authors compared the performance of four deep learning architectures: VGG-19, Xception, ResNet-50, and InceptionV3. Based on the results of the experiments, authors decided to use the CNN Xception as it yielded good performance. However, the CNN algorithm does not attain its maximum potential when using default parameters. Hence, authors goal is to enhance its performance by implementing parameter optimization using the grid search algorithm to determine the optimal combination of learning rate and epoch values. The experimental results demonstrated that the implementation of parameter optimization in CNN significantly improved accuracy in potato plants from 96.3% to 97.9% and in maize plants from 87.6% to 93.4%.

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