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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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From Waste to Power: Fly Ash-Based Silicone Anode Lithium-Ion Batteries Enhancing PV Systems Amalia, Kania Yusriani; Dewi, Tresna; Rusdianasari, Rusdianasari
EMITTER International Journal of Engineering Technology Vol 12 No 2 (2024)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v12i2.885

Abstract

Indonesia's high solar irradiance, averaging 4.8 kWh/m²/day, presents a significant opportunity to harness solar power to meet growing energy demands. Fly ash, abundant in Indonesia and rich in silicon dioxide (40-60% SiO2), can be repurposed into high-value silicon anodes. The successful extraction of silicon from fly ash, increasing SiO2 content from 49.21% to 93.52%, demonstrates the potential for converting industrial waste into valuable battery components. Combining these advanced batteries with PV systems improves overall efficiency and reliability. Energy charge and discharge experiments reveal high energy efficiency for silicon-anode batteries, peaking at 80.53% and declining to 67.67% after ten cycles. Impedance spectroscopy tests indicate that the S120 sample, with the lowest impedance values, is most suitable for high-efficiency applications. Photovoltaic (PV) system integration experiments show that while increased irradiance generally boosts power output, other factors like PV cell characteristics and load conditions also play crucial roles. In summary, leveraging Indonesia's solar potential with fly ash-based silicon anode batteries and advanced predictive analytics addresses energy and environmental challenges. This innovative approach enhances battery performance and promotes the circular economy by converting waste into high-value products, paving the way for a sustainable and efficient energy future.
Early Detection of Ball Bearing Faults Using the Decision Tree Method Istanto, Iwan; Sulaiman , Robi; Rio Natanael Wijaya; Budi Suhendro; Rokhmat Arifianto; Slamet
EMITTER International Journal of Engineering Technology Vol 12 No 2 (2024)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v12i2.920

Abstract

Bearings are one of the important components in the machine that functions as a holder and positions the shaft alignment radially when rotating. Statistics show that about 50% of failures in electric motors are related to bearings. Therefore, monitoring bearing performance and efficiency before damage occurs is necessary to avoid more serious damage and save repair costs. This research aims to build a classification model that can identify bearings in normal condition and 6 types of damage (inner crack, outer crack, ball crack, and a combination of both) using the HUST dataset. The model building process begins with collecting datasets, processing and extracting dataset features, building classification models and evaluating the models that have been made. A decision tree is a type of supervised machine learning used to categorize or make predictions based on how a previous set of questions were answered. The model is a form of supervised learning, meaning that the model is trained and tested on a set of data that contains the desired categorization. The results of the decision tree model that has been built are able to identify bearing damage with an accuracy of 94.47%.
A Deep Dive into a Groundbreaking Approach to Machine Learning-Powered E-Learning Sengupta, Subhabrata; Das, Rupayan; Chakrabarti, Satyajit
EMITTER International Journal of Engineering Technology Vol 12 No 2 (2024)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v12i2.855

Abstract

Information retrieval aims to find the most important data for specific queries. The challenge is retrieving relevant data efficiently due to the large search area. Existing solutions lead to unnecessary processing costs. Additionally, identifying the main focus of the query is crucial for targeted retrieval. Current methods struggle to address these issues effectively. To overcome these challenges, we have proposed a goal-question-indicator (GQI) approach for personalized learning inquiry (PLA). This approach allows for efficient retrieval of variable-sized data with reduced processing requirements. We have also presented the open learning analytics platform's (Open-LAP) pointer motor segment, which helps end users specify goals, generates discussion topics, and provides self-characterizing pointers.
Evaluating YOLOv5s and YOLOv8s for Kitchen Fire Detection: A Comparative Analysis Ismail, Norisza Dalila; Ramli, Rizauddin; Ab Rahman, Mohd Nizam
EMITTER International Journal of Engineering Technology Vol 12 No 2 (2024)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v12i2.882

Abstract

Accurate and timely detection of kitchen fires is crucial for enhancing safety and reducing potential damage. This paper discusses comparative analysis of two cutting-edge object detection models, YOLOv5s and YOLOv8s, focusing on each performance in the critical application of kitchen fire detection. The performance of these models is evaluated using five main key metrics including precision, F1 score, recall, mean Average Precision across various thresholds (mAP50-95) and mean Average Precision at 50 percent threshold (mAP50). Results indicate that YOLOv8s significantly outperforms YOLOv5s in several metrics. YOLOv8s achieves a recall of 0.814 and an mAP50 of 0.897, compared to YOLOv5s' recall of 0.704 and mAP50 of 0.783. Additionally, YOLOv8s attains an F1 score of 0.861 and an mAP50-95 of 0.465, whereas YOLOv5s records an F1 score of 0.826 and mAP50-95 of 0.342. However, YOLOv5s shows a higher precision of 0.952 compared to YOLOv8s' 0.914. This detailed evaluation underscores YOLOv8s as a more effective model for precise fire detection in kitchen settings, highlighting its potential for enhancing real-time fire safety systems. Additionally, by offering the future work of integration of sensors with latest YOLO involvement can further optimize efficiency and fast detection rate.
Development of DOAS System for Hazardous Methane Detection in the Near-Infrared Region Khandaker, Sayma; Hasan, Md Mahmudul; Shaipuzaman, Nurulain; Aspar, Mohd Amir Shahlan Mohd; Manap, Hadi
EMITTER International Journal of Engineering Technology Vol 12 No 2 (2024)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v12i2.890

Abstract

Methane (CH4) is a powerful greenhouse gas that greatly contributes to global warming. It is also very combustible, which means it has a large danger of causing explosions. It is crucial to tackle methane emissions, especially those arising from oil and gas extraction processes like transit pipes. An area of great potential is the advancement of dependable sensors for the detection and reduction of methane leaks, with the aim of averting dangerous consequences. An open-path differential optical absorption spectroscopy (DOAS) system was described in this paper for the purpose of detecting CH4 gas emission at a moderate temperature. An in-depth examination of the absorption lines was conducted to determine the optimal wavelength for measurement. The Near Infrared (NIR) region was identified as the most suitable wavelength for detecting methane. Multiple measurements were conducted at different integration times (1 second, 2 seconds, and 3 seconds) to ensure reliability and determine the optimal integration time for the CH4 detection system. The DOAS system has the capability of precisely detecting methane concentrations at 1M ppm in the NIR region with a quick integration time of 2 seconds.
Implementation of Portable Ultrasound for Heart Disease Detection Using Cloud Computing-Based Machine Learning Sigit, Riyanto; Rika Rokhana; Setiawardhana; Taufiq Hidayat; Anwar; Jovan Josafat Jaenputra
EMITTER International Journal of Engineering Technology Vol 12 No 2 (2024)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v12i2.904

Abstract

Heart disease remains one of the leading causes of death globally, including in Indonesia. Cardiovascular disease is the leading cause of death worldwide, resulting in a significant number of fatalities. In Indonesia, access to specialized heart examination services is limited, requiring patients to visit large hospitals equipped with specialized facilities. Echocardiographic examinations using ultrasound can measure various heart parameters, such as hemodynamics, heart mass, and myocardial deformation. Portable ultrasound devices have emerged, enabling flexible and effective heart examinations. These devices capture video data of the patient's heart condition. The data undergoes image preprocessing involving median filtering, high-boost filtering, morphological operations, thresholding, and Canny filtering. Segmentation is performed using region filters, collinear filters, and triangle equations. Tracking utilizes the Optical Flow Lucas-Kanade method, and feature extraction employs Euclidean distance and trigonometric equations. The classification stage uses Support Vector Machine (SVM). Video data is transmitted via a mobile application to the cloud, where all stages from preprocessing to classification are conducted on cloud servers. The classification results are then sent back to the mobile application. The proposed model achieved an accuracy rate of 86% with a standard deviation of 0.09, indicating that the detection system performs effectively.
An Exploring the Power of Feature Representations: An Empirical Study on Product Reviews for Sentiment Analysis Lian Ben, Thian; R N, Ravikumar; Kumar Singh, Sushil; Bharatbhai Chauhan, Pratikkumar; N, Sivakumar; V, Manoj Praveen
EMITTER International Journal of Engineering Technology Vol 13 No 1 (2025)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v13i1.821

Abstract

With the rise of e-commerce and online shopping, customer reviews have become a crucial factor in determining the quality and reputation of a product. Online shoppers rely heavily on customer reviews to make informed purchasing decisions, as they don't have the opportunity to physically examine the product before buying. As a result, companies are also investing in sentiment analysis to understand and respond to customer feedback, as well as to enhance the quality of their products and services. Using natural language processing (NLP) and machine learning techniques, sentiment analysis classifies the tone of a customer review as positive, negative, or neutral. It involves analysing text data to determine the overall tone, emotion, and opinion expressed in a review. In this work, we study sentiment analysis of client reviews using machine learning algorithms with different vectorization techniques. The strategy outlined here consists of three distinct phases. The initial step involves some pre-processing to get rid of irrelevant information and find the useful terms. Then, feature extraction was accomplished utilizing numerous vectorization strategies as Bag-Of-Words (BoW), Term Frequency Inverse Document Frequency (TF-IDF), and N-grams. After extracting the features from text data, the final stage is classification and predictions based on machine learning approaches. We evaluated the proposed models on Yelp reviews dataset. The experimental results are evaluated using metrics such as precision, recall, and f1-score, and K-fold cross-validation.
Improving 3D Human Pose Orientation Recognition Through Weight-Voxel Features And 3D CNNs Riansyah, Moch. Iskandar; Putra, Oddy Virgantara; Rahmanti, Farah Zakiyah; Priyadi, Ardyono; Wulandari, Diah Puspito; Sardjono, Tri Arief; Yuniarno, Eko Mulyanto; Hery Purnomo, Mauridhi
EMITTER International Journal of Engineering Technology Vol 13 No 1 (2025)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v13i1.847

Abstract

Preprocessing is a widely used process in deep learning applications, and it has been applied in both 2D and 3D computer vision applications. In this research, we propose a preprocessing technique involving weighting to enhance classification performance, incorporated with a 3D CNN architecture. Unlike regular voxel preprocessing, which uses a zero-one (binary) approach, adding weighting incorporates stronger structural information into the voxels. This method is tested with 3D data represented in the form of voxels, followed by weighting preprocessing before entering the core 3D CNN architecture. We evaluate our approach using both public datasets, such as the KITTI dataset, and self-collected 3D human orientation data with four classes. Subsequently, we tested it with five 3D CNN architectures, including VGG16, ResNet50, ResNet50v2, DenseNet121, and VoxNet. Based on experiments conducted with this data, preprocessing with the 3D VGG16 architecture, among the five architectures tested, demonstrates an improvement in accuracy and a reduction in errors in 3D human orientation classification compared to using no preprocessing or other preprocessing methods on the 3D voxel data. The results show that the accuracy and loss in 3D object classification exhibit superior performance compared to specific preprocessing methods, such as binary processing within each voxel.
The Next Generation Wireless Network Deployment Using Machine Learning Based Multi-Objective Genetic Algorithm Mahesh H. B; Ali Ahammed G. F; Usha S. M
EMITTER International Journal of Engineering Technology Vol 13 No 1 (2025)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v13i1.875

Abstract

6G networks provides ubiquitous connectivity, reduced delay and high-speed gigabit connection. The Introduction of AI to the planning process of 5G beyond networks is crucial to ensure the efficient deployment of cells and the minimization of SINR (signal to interference plus noise ratio). The Multi-Objective Genetic Algorithm (MOGA) to take care of the planning issue in 5G and beyond network organizations. This is accomplished by expanding the already existing 4G and 5G infrastructure. The MOGA endeavors to limit the deployment cost, the interference between the cells and maximize the percentage of the clients being served. This work is the solution for deployment problem in next generation networks. The randomly deployment of the cells decreases the network performance, increases the interference and not effective in terms of deployment cost and leads to Dense Multi-Objective Deployment problem. An optimised deployment strategy is employed in the proposed work to address this issue. This work based on optimized utilization of the network through planning. This decreases the cost of deployment, interference and redundancy. It enhances the coverage capacity and quality of service. This excellent coverage of users which is close to 85% is obtained over existing 4G and 5G infrastructure, thereby reducing the total cost of deployment. The work is compared with the meta-heuristic algorithms. The comparison results shows that the proposed work achieves higher SINR, improved coverage capacity than the meta-heuristic algorithms.
Synergistic Integration of LQR Control and PSO Optimization for Advanced Active Suspension Systems Utilizing Electro-Hydraulic Actuators and Electro-Servo Valves Trong Tu
EMITTER International Journal of Engineering Technology Vol 13 No 1 (2025)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v13i1.892

Abstract

This paper investigates the design and optimization of Linear Quadratic Regulator (LQR) controllers for vehicle active suspension systems, incorporating an electro-hydraulic actuator with an electro-servo valve. To enhance both vehicle comfort and road-holding stability, we employ Particle Swarm Optimization (PSO) to optimize the LQR controller parameters. The active suspension system model includes the dynamics of the electro-hydraulic actuator and the electro-servo valve, providing a realistic and practical framework for heavy vehicles. By leveraging PSO, the LQR controller parameters are fine-tuned to minimize a cost function that integrates both comfort and stability up to 76.91%. The results demonstrate substantial improvements in ride comfort and road-holding stability compared to traditional passive suspension systems. This research remarks the fundamentals of the experimental validation and further refinement of these control algorithms to adapt to various driving conditions and vehicle models, ultimately aiming to transition these optimized controllers from theoretical frameworks to practical, real-world applications.

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