cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota surabaya,
Jawa timur
INDONESIA
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
 37   38   39   40   41 
Text Mining for Employee Candidates Automatic Profiling Based on Application Documents Adhi Dharma Wibawa; Arni Muarifah Amri; Arbintoro Mas; Syahrul Iman
EMITTER International Journal of Engineering Technology Vol 10 No 1 (2022)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v10i1.679

Abstract

Opening job vacancies using the Internet will receive many applications quickly. Manually filtering resumes takes a lot of time and incurs huge costs. In addition, this manual screening process tends to be inaccurate due to fatigue conditions and fails in obtaining the right candidate for the job. This paper proposed a solution to automatically generate the most suitable candidate from the application document. In this study, 126 application documents from a private company were used for the experiment. The documents consist of 41 documents for Human Resource and Development (HRD) staff, 42 documents for IT (Data Developer), and 43 documents for the Marketing position. Text Processing is implemented to extract relevant information such as skills, education, experiences from the unstructured resumes and summarize each application. A specific dictionary for each vacancy is generated based on terms used in each profession. Two methods are implemented and compared to match and score the application document, namely Document Vector and N-gram analysis. The highest the score obtained by one document, the highest the possibility of application to be accepted. The two methods’ results are then validated by the real selection process by the company. The highest accuracy was achieved by the N-Gram method in IT vacancy with 87,5%, while the Document Vector showed 75% accuracy. For Marketing staff vacancy, both methods achieved the same accuracy as 78%. In HRD staff vacancy, the N-Gram method showed 68%, while Document Vector showed 74%. In conclusion, overall the N-gram method showed slightly better accuracy compared to the Document Vector method.
Rapid Control Prototyping of Five-Level MMC based Induction Motor Drive with different Switching Frequencies VENU MADHAV GOPALA; T. Anil Kumar; D. Krishna; Ch. Srinivasa Rao; Shashank Kumar; Sudipto Poddar
EMITTER International Journal of Engineering Technology Vol 10 No 1 (2022)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v10i1.637

Abstract

In this paper, Rapid Control Prototyping (RCP) of five-level Modular Multilevel Converter (MMC) based Induction Motor (IM) drive performance is observed with different switching frequencies. The Semikron based MMC Stacks with two half-bridge each are tested with the switching logic generated by phase and level shifted based Sinusoidal Pulse Width Modulation (SPWM) technique. The switching logic is generated by the Typhoon Hardware in Loop (HIL) 402. The disadvantages of Multilevel Converter like not so good output quality, less modularity, not scalable and high voltage and current rating demand for the power semiconductor switches can be overcome by using MMC. In this work, the IM drive is fed by MMC and the experimentally the performance is observed. The performance of the Induction Motor in terms of speed is observed with different switching frequencies of 2.5kHz, 5kHz, 7.5kHz, 10kHz, 12.5kHz and the results are tabulated in terms of Total Harmonic Distortion (THD) of input voltage and current to the Induction Motor Drive. The complete model is developed using Typhoon HIL 2021.2 Version Real-Time Simulation Software.
Low Power, Area Efficient Architecture for Successive Cancellation Decoder Sujanth Roy J; G Lakshminarayanan
EMITTER International Journal of Engineering Technology Vol 10 No 1 (2022)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v10i1.650

Abstract

Polar codes have recently emerged as an error-correcting code and have become popular owing to their capacity-achieving nature. Polar code based communication system primarily consists of two parts, including Polar Encoder and Decoder. Successive Cancellation Decoder is one of the methods used in the decoding process. The Successive Cancellation Decoder is a recursive structure built with the building block called Processing Element. This article proposes a low power, area-efficient architecture for the Successive Cancellation Decoder for polar codes. Successive Cancellation Decoder with code length 1024 and code rate 0.5 was designed in Verilog HDL and implemented using 45-nm CMOS technology. The proposed work focuses on developing an area-efficient Successive Cancellation Decoder architecture by presenting a new Processing Element architecture. The proposed architecture has produced about 35% lesser area with a 12% reduced gate count. Moreover, power is also reduced by 50%. A substantial reduction in the latency and improvement in the Technology Scaled Normalized Throughput value was observed.
Dynamically Energy-Efficient Resource Allocation in 5G CRAN Using Intelligence Algorithm Prasanth Rao Adiraju; Voore Subba Rao
EMITTER International Journal of Engineering Technology Vol 10 No 1 (2022)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v10i1.661

Abstract

5G network is the next generation for cellular networks to overcome the challenges and limitations of the 4G network. Cloud Radio Access Network(C-RAN) is providing solutions for cost-efficient and power-efficient solutions for the 5G network. The aim of this paper proposed an energy-efficient C-RAN to minimize the cost of the network by dynamically allocating BBU resources to RRHs as per facing traffic, and also minimize the energy consumption of centralized BBU resources that affect dynamically allocate of RRHs. Particle Swarm Optimization (PSO) algorithm is a Swarm Intelligence algorithm for optimization of mapping between BBU-RRH for resource allocation in C-RAN. The main objective of the paper is as per resource usage in C-RAN the BBU is put in the active or in-active mode to minimize energy consumption in C-RAN of 5G technology. As per our proposed C-RANapplication, the proposed PSO algorithm 90% minimizes energy consumption and maximizes energy efficiency compared with existing work.
A Machine learning Classification approach for detection of Covid 19 using CT images Suguna G C; Veerabhadrappa S T; Tejas A; Vaishnavi P; Raghunandan Gowda; Panchami Udupa; Spoorthy; Smitha Reddy; Sudarshan E
EMITTER International Journal of Engineering Technology Vol 10 No 1 (2022)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v10i1.672

Abstract

Coronavirus disease 2019 popularly known as COVID 19 was first found in Wuhan, China in December 2019. World Health Organization declared Covid 19 as a transmission disease. The symptoms were cough, loss of taste, fever, tiredness, respiratory problem. These symptoms were likely to show within 11 –14 days. The RT-PCR and rapid antigen biochemical tests were done for the detection of COVID 19. In addition to biochemical tests, X-Ray and Computed Tomography (CT) images are used for the minute details of the severity of the disease. To enhance efficiency and accuracy of analysis/detection of COVID images and to reduce of doctors' time for analysis could be addressed through Artificial Intelligence. The dataset from Kaggle was utilized to analyze. The statistical and GLCM features were extracted from CT images for the classification of COVID and NON-COVID instances in this study. CT images were used to extract statistical and GLCM features for categorization. In the proposed/prototype model, we achieved the classification accuracy of 91%, and 94.5% using SVM and Random Forest respectively.
Secure Real-time Data Transmission for Drone Delivery Services using Forward Prediction Scheduling SCTP Febby Ronaldo; Amang Sudarsono; Dadet Pramadihanto
EMITTER International Journal of Engineering Technology Vol 10 No 1 (2022)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v10i1.690

Abstract

Drone technology is considered the most effective solution for the improvement of various industrial fields. As a delivery service, drones need a secure communication system that is also able to manage all of the information data in real-time. However, because the data transmission process occurs in a wireless network, data will be sent over a channel that is more unstable and vulnerable to attack. Thus, this research, purposes a Forward Prediction Scheduling-based Stream Control Transmission Protocol (FPS-SCTP) scheme that is implemented on drone data transmission system. This scheme supports piggybacking, multi-streaming, and Late Messages Filter (LMF) which will improve the real-time transmission process in IEEE 802.11 wireless network. Meanwhile, on the cybersecurity aspect, this scheme provides the embedded option feature to enable the encryption mechanism using AES and the digital signatures mechanism using ECDSA. The results show that the FPS-SCTP scheme has better network performance than the default SCTP, and provides full security services with low computation time. This research contributes to providing a communication protocol scheme that is suitable for use on the internet of drones’ environment, both in real-time and reliable security levels.
Classification of Ischemic Stroke with Convolutional Neural Network (CNN) approach on b-1000 Diffusion-Weighted (DW) MRI Andi Kurniawan Nugroho; Dinar Mutiara Kusumo Nugraheni; Terawan Agus Putranto; I Ketut Eddy Purnama; Mauridhi Hery Purnomo
EMITTER International Journal of Engineering Technology Vol 10 No 1 (2022)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v10i1.694

Abstract

When the blood flow to the arteries in brain is blocked, its known as Ischemic stroke or blockage stroke. Ischemic stroke can occur due to the formation of blood clots in other parts of the body. Plaque buildup in arteries, on the other hand, can cause blockages because if it ruptures, it can form blood clots. The b-1000 Diffusion Weighted (DW) Magnetic Resonance Imaging (MRI) image was used in a general examination to obtain an image of the part of the brain that had a stroke. In this study, classifications used several variations of layer convolution to obtain high accuracy and high computational consumption using b-1000 Diffusion Weighted (DW) MR in ischemic stroke types: acute, sub-acute and chronic. Ischemic stroke was classified using five variants of the Convolutional Neural Network (CNN) architectural design, i.e., CNN1–CNN5. The test results show that the CNN5 architectural design provides the best ischemic stroke classification compared to other architectural designs tested, with an accuracy of 99.861%, precision 99.862%, recall 99.861, and F1-score 99.861%.
An Image Processing Framework for Breast Cancer Detection Using Multi-View Mammographic Images Nada Fitrieyatul Hikmah; Tri Arief Sardjono; Windy Deftia Mertiana; Nabila Puspita Firdi; Diana Purwitasari
EMITTER International Journal of Engineering Technology Vol 10 No 1 (2022)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v10i1.695

Abstract

Breast cancer is the leading cause of cancer death in women. The early phase of breast cancer is asymptomatic, without any signs or symptoms. The earlier breast cancer can be detected, the greater chance of cure. Early detection using screening mammography is a common step for detecting the presence of breast cancer. Many studies of computer-based using breast cancer detection have been done previously. However, the detection process for craniocaudal (CC) view and mediolateral oblique (MLO) view angles were done separately. This study aims to improve the detection performance for breast cancer diagnosis with CC and MLO view analysis. An image processing framework for multi-view screening was used to improve the diagnostic results rather than single-view. Image enhancement, segmentation, and feature extraction are all part of the framework provided in this study. The stages of image quality improvement are very important because the contrast of mammographic images is relatively low, so it often overlaps between cancer tissue and normal tissue. Texture-based segmentation utilizing the first-order local entropy approach was used to segment the images. The value of the radius and the region of probable cancer were calculated using the findings of feature extraction. The results of this study show the accuracy of breast cancer detection using CC and MLO views were 88.0% and 80.5% respectively. The proposed framework was useful in the diagnosis of breast cancer, that the detection results and features help clinicians in making treatment.
Density-based Clustering for 3D Stacked Pipe Object Recognition using Directly-given Point Cloud Data on Convolutional Neural Network Alfan Rizaldy Pratama Pratama; Bima Sena Bayu Dewantara; Dewi Mutiara Sari; Dadet Pramadihanto
EMITTER International Journal of Engineering Technology Vol 10 No 1 (2022)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v10i1.704

Abstract

One of the most commonly faced tasks in industrial robots is bin picking. Much work has been done in this related topic is about grasping and picking an object from the piled bin but ignoring the recognition step in their pipeline. In this paper, a recognition pipeline for industrial bin picking is proposed. Begin with obtaining point cloud data from different manner of stacking objects there are well separated, well piled, and arbitrary piled. Then followed by segmentation using Density-based Spatial Clustering Application with Noise (DBSCAN) to obtain individual object data. The systems then use Convolutional Neural Network (CNN) that consume raw point cloud data. Performance of the segmentation reaches an impressive result in separating objects and network is evaluated under the varying style of stacking objects and give the result with average Accuracy, Recall, Precision, and F1-Score on 98.72%, 95.45%, 99.39%, and 97.33% respectively. Then the obtained model can be used for multiple objects recognition in one scene.
Classification Method in Fault Diagnosis of Oil-Immersed Power Transformers by Considering Dissolved Gas Analysis Rosmaliati; Bernandus Anggo Seno Aji; Isa Hafidz; Ardyono Priyadi; 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.v10i2.702

Abstract

Fault detection in the incipient stage is necessary to avoid hazardous operating conditions and reduce outage rates in transformers. Fault-detected dissolved gas analysis is widely used to detect incipient faults in oil-immersed transformers. This paper proposes fault diagnosis transformers using an artificial neural network based on classification techniques. Data on the condition of transformer oil is assessed for dissolved gas analysis to measure the dissolved gas concentration in the transformer oil. This type of disturbance can affect the gas concentration in the transformer oil. Fault diagnosis is implemented, and fault reference is provided. The result of the NN method is more accurate than the Tree and Random Forest method, with CA and AUC values 0.800 and 0.913. This classification approach is expected to help fault diagnostics in power transformers.

Page 39 of 44 | Total Record : 436

 37   38   39   40   41 

Filter by Year

2013 2025


Reset
Filter By Issues
All Issue Vol 13 No 1 (2025) Vol 12 No 2 (2024) Vol 12 No 1 (2024) Vol 11 No 2 (2023) Vol 11 No 1 (2023) Vol 10 No 2 (2022) Vol 10 No 1 (2022) Vol 9 No 2 (2021) Vol 9 No 1 (2021) Vol 8 No 2 (2020) Vol 8 No 1 (2020) Vol 7 No 2 (2019) Vol 7 No 1 (2019) Vol 7, No 1 (2019) Vol 6 No 2 (2018) Vol 6, No 2 (2018) Vol 6, No 1 (2018) Vol 6 No 1 (2018) Vol 5, No 2 (2017) Vol 5 No 2 (2017) Vol 5 No 1 (2017) Vol 5, No 1 (2017) Vol 4 No 2 (2016) Vol 4, No 2 (2016) Vol 4 No 1 (2016) Vol 4, No 1 (2016) Vol 3, No 2 (2015) Vol 3 No 2 (2015) Vol 3 No 1 (2015) Vol 3, No 1 (2015) Vol 2 No 2 (2014) Vol 2, No 2 (2014) Vol 2, No 1 (2014) Vol 2 No 1 (2014) Vol 1 No 1 (2013) Vol 1, No 1 (2013) More Issue