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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 445 Documents
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Reliability improvement of distribution networks: A case study of Duhok distribution network Sadiq, Emad; Antar, Rakan
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.v13i1.908

Abstract

Power system is considered one of the most complicated infrastructures. The main components of the system are generation, transmission and distribution. The main function of the system is to supply consumers with electricity as economically and reliably as possible. In order to provide uninterrupted power supply to the consumers, the reliability of distribution system needs to be improved. Several strategies are in place in order to enhance the reliability of the distribution networks. The distribution system could encounter the challenges of aging infrastructure, environmental factors, and the rising in demand power which can cause frequent power interruptions. This paper aims to enhance the reliability of distribution networks by utilizing network reconfiguration techniques to improve voltage profiles, reduce power losses, and restore power to interrupt sections as quickly as possible in the event of a failure. Additionally, the study incorporates the use of fault passage indicator devices installed along the lines. These devices are intended to swiftly identify fault locations, thereby minimizing outage durations and further improving network reliability. An investment in these measures, can obtain significant reliability improvements in the network which at the end lead to consumer satisfaction and huge economic advantages for the system operator.
Factors impacting adoption of electronic HRM in public sector organizations: Case study of Hudury mobile attendance application in Ministry of Education in the Saudi Arabia Alduraywish, Yousef
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.v13i1.927

Abstract

This study investigates the factors influencing the adoption of the Hudury electronic attendance system among employees of the Ministry of Education (MOE) in Saudi Arabia. Using the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB), this research examines the impact of perceived ease of use (PEOU), perceived usefulness (PU), trust, security, attitude, and behavioral intentions on actual system usage. A non-probability sampling technique was employed to collect 225 responses from employees across three MOE departments through an online survey. Statistical analysis revealed that PEOU, PU, security, and attitude significantly and positively influence the adoption of Hudury. However, while trust and behavioral intention also have a positive impact, their effects on system adoption were found to be statistically insignificant. These findings highlight the importance of addressing trust deficits by conducting training sessions on Hudury’s efficacy to enhance employees' behavioral intentions toward its use. The study is limited by its non-probability sampling method, which may affect the generalizability of the findings to the broader MOE workforce.
Optimization of Gray Level Co-occurrence Matrix (GLCM) Texture Feature Parameters in Determining Rice Seed Quality Aji Setiawan; Arif Budiman, Adam
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.v13i1.928

Abstract

Rice seed quality assessment is a critical measure in promoting agricultural productivity, as high-quality seeds directly influence crop yield and resilience. One of method for evaluating seed quality is texture analysis, which leverages the Gray Level Co-occurrence Matrix (GLCM) to extract meaningful features from seed images, providing insights into their condition and potential performance. This research aims to determine the optimal performance of GLCM parameters in identifying the texture characteristics of rice seed quality. The experiments were conducted using four angles (0°, 45°, 90°, and 135°) and three-pixel distances (1, 2, and 3), evaluating features such as homogeneity, contrast, dissimilarity, and energy. The results indicate that certain parameter configurations significantly affect the discriminative power of the extracted features, with the Support Vector Machine (SVM) classifier achieving the highest performance at a pixel distance of 1, with an accuracy of 0.73, precision of 0.79, recall of 0.73, and F1-score of 0.72. These findings demonstrate that optimizing GLCM parameter settings directly contributes to improved classification performance, highlighting the method's potential for enhancing rice seed quality assessment.
Utilizing Evolutionary Mating Algorithm Optimized Deep Learning to Assess Cardiovascular Diseases Risk Alsarori, Ahmed; Sulaiman, Mohd Herwan
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.v13i1.936

Abstract

Cardiovascular Diseases (CVD) continue to be a primary cause of death worldwide, underscoring the critical importance of early and accurate risk prediction. However, traditional predictive models struggle with the complexity and interdependencies in medical data. This study addresses this gap by proposing a deep learning-based risk assessment model optimized with the Evolutionary Mating Algorithm (EMA) to enhance prediction accuracy and efficiency. Our contributions include developing a dedicated risk variable for machine learning applications and benchmarking the EMA-optimized model against ADAM and Particle Swarm Optimization (PSO). The proposed method was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Coefficient of Determination (R²), and Standard Deviation (STD). Experimental results demonstrate that the EMA-optimized model outperforms traditional optimization methods, achieving an MAE of 0.037, RMSE of 0.0464, and an R² of approximately 0.91. These results highlight the effectiveness of EMA in enhancing cardiovascular risk assessment models, providing a more reliable tool for early diagnosis and clinical decision-making.
A Detailed Set of Ideas for Designing a Quantum Computing Framework Based on Smart Contracts, Configured Using Foundry and Qiskit Tudorache, Alexandru-Gabriel
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.v13i1.949

Abstract

The purpose of this paper is to describe a new system design for integrating quantum computing algorithms (and their results) into a blockchain network. In this selected context, we can use, create and upload smart contracts (SCs) that allow users to perform various quantum computations, by using the corresponding circuits. We are therefore proposing a system that uses gas fees in the blockchain context, in order to offer access to certain circuits and their simulation results; the system also allows for the previously analyzed circuits to become publicly available, through SCs – this can act like a quantum circuit encyclopedia. Most users in the first generation will have to pay, in addition to the normal transaction fees (gas) required to call the SC methods, a small development fee for the contract creation for most of the tasks; after a certain number of SCs, enough configurations and results will become accessible to everyone, and only custom, unprocessed circuits will require the development fee. Optionally, a dedicated blockchain network (similar to one of the existing test ones) can also be designed, with contracts that have access to real quantum hardware; its owners can decide (if necessary) the value of the virtual coin in connection to a real-world currency. For our experiments, we selected the Solidity language for the development of SCs, and Python for the development and simulation of quantum circuits, with the help of the Qiskit framework, an open-source library for quantum processing developed by IBM.
Sitting Posture Detection and Classification Using Machine Learning Algorithms on RapidMiner Sri-ngernyuang, Chawakorn; Prakrankiat Youngkong; Jinpitcha Mamom; Duangruedee Lasuka
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.v13i1.898

Abstract

Integrating pressure sensors into cushion pads presents a viable posture monitoring and classification solution in innovative health care and ergonomic design. In this study, a cushion pad with a pressure sensor implanted that can recognize and classify different postures using machine learning techniques is developed and evaluated. The principal objective is to augment postural awareness and avoid disorders of the muscles. The cushion pad system was created and used by combining software algorithms with hardware sensors. Using a variety of machine learning approaches, RapidMiner, a data science platform, was used to analyze the pressure data to classify postures. The following algorithms are tested using cross-validation for a robust evaluation: Decision Tree, Naive Bayes, Neural Network, Random Forest, and K-Nearest Neighbors (K-NN). The outcomes showed that the various algorithms' levels of accuracy varied. The Naive Bayes algorithm demonstrated a lesser accuracy of 55.83% compared to the Decision Tree algorithm's 84.49% accuracy. The Random Forest algorithm surpassed the others with an accuracy of 85.98%, while the Neural Network approach produced an accuracy of 82.26%. The k-NN algorithm also yielded promising results, with an accuracy of 82.01%. According to these results, the Random Forest algorithm outperforms the Decision Tree algorithm for posture categorization in this specific example. A workable approach for enhancing ergonomic health and avoiding posture-related illnesses is to integrate such machine learning models into a cushion pad with pressure sensor integration that can significantly help proactive posture management.
Visual Similarity Detection for Intellectual Property using Deep Transfer Learning Alnafjan, Abeer; Aldayel, Mashael
EMITTER International Journal of Engineering Technology Vol 13 No 2 (2025)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v13i2.852

Abstract

Trademarks examination can benefit from deep transfer learning. Utilizing pretrained models to extract image features can significantly improve the trademarks registration process. This approach can facilitate and accelerate image detection. This study aims to enhance the trademark similarity examination process by detecting marks’ visual similarities using deep transfer learning. Deep transfer learning has the potential to develop the registration process of trademarks through the implementation of an automated image detection system, which can enhance detection accuracy. To the best of our knowledge, no automated approach has been used locally to determine the similarities between local trademarks. This study proposes an image similarity detection system to make the trademark examination process more efficient and assist examiners in their decision-making. The proposed system was validated using a dataset provided by the Saudi authority for intellectual property (SAIP). To extract the features, we employed a residual network-based convolutional neural network model (ResNet-50). Then principal component analysis (PCA) was used to reduce the number of extracted features. The proposed system reached a mean average precision (MAP) of 0.774, which indicates a promising result in distinguishing the similarity of trademarks. The findings of this research suggested that an image similarity detection system can support decision-making in trademark examination contexts. Trademark examiners, legal professionals, and intellectual property offices can use the results of this research to enhance their evaluation processes and improve the accuracy and efficiency of trademark registration.
Performance Analysis of Decision Tree Ensemble Models and Feature Importance Analysis in Prediction of Particulate Matter PM10 Babu, Sherin; Thomas, Binu
EMITTER International Journal of Engineering Technology Vol 13 No 2 (2025)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v13i2.933

Abstract

Particulate Matter induced air pollution is known to have significant negative impacts on both the environment and human health. This research evaluates the effectiveness of various decision tree ensemble models in predicting daily PM10 concentrations in Thiruvananthapuram, Kerala, from July 2017 to December 2019. Seven decision tree ensemble models, namely Random Forest, Extra Trees, Gradient Boosting, AdaBoost, LightGBM, XGBoost, and Histogram-Based Gradient Boosting are employed here. To address missing data in the dataset, kNN imputation is utilized for a cohesive dataset suitable for model training. The models utilize both meteorological and air pollutant variables, with performance assessment using metrics such as the coefficient of determination (R²), root mean square error (RMSE) and mean absolute error (MAE). The findings indicate that the Extra Trees regression model provided the best prediction performance (R² = 0.9397, RMSE = 6.664 μg/m³, MAE = 4.950 μg/m³). Histogram-Based Gradient Boosting and Random Forest also demonstrate strong predictive capabilities. The explainability of the best prediction models is conducted by the feature importance analysis process. Feature importance analysis highlighted sulfur dioxide (SO2) as the most significant pollutant influencing PM10 levels, alongside meteorological factors like wind speed and rainfall, enhancing both prediction accuracy and interpretability of results. This research represents the first comprehensive effort to predict PM10 levels in Thiruvananthapuram using machine learning techniques, addressing a gap in regional air quality studies.
FloYO-Net: Enhancing Small Floating Waste Detection in Natural Waters Using Atrous YOLOv5s Badams, Badiu; Ullah Sheikh, Usman; Syed Abu Bakar, Syed Abd Rahman; Abdul Wahab, Norhaliza
EMITTER International Journal of Engineering Technology Vol 13 No 2 (2025)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v13i2.978

Abstract

Detecting small and partially hidden objects in rivers and water bodies remains a major challenge for real-time waste detection systems. These objects are often missed due to their small size, low contrast, and cluttered surroundings. Further complicating the task is the lack of dedicated datasets focused on small floating debris, limiting the development of more capable detection models. To bridge this gap, we developed D_six, a custom dataset of 495 high-resolution images capturing six classes of floating waste under real-world conditions. In this study, we improve the YOLOv5s object detection model by integrating atrous convolutions at three key backbone layers: P1/2, P3/8, and P5/32. These layers represent different scales of the feature pyramid, and the strategic placement of atrous convolution at each level plays a crucial role in helping the model recognize small and occluded objects more effectively. Using a dilation rate of 6, the model’s receptive field is expanded without increasing its size or slowing it down. When trained and evaluated on the D_six data set, the FloYO-Net (Floating Object YOLO Network) consistently outperformed the standard YOLOv5s, achieving a mean Average Precision (mAP@0.5) of 0.828 and mAP@0.5:0.95 of 0.509, compared to 0.787 and 0.498 respectively. Improvements were especially notable for hard-to-detect items like plastic bottles and plastic drink containers, with average precision gains of 6.6% and 7.1%, respectively. These results demonstrate that atrous convolution — when thoughtfully placed — can significantly improve detection accuracy, making it a powerful enhancement for real-time environmental cleanup systems.
Enhanced Wingsuit Flying Search (EWFS) Algorithm for Combinatorial T-way Test Suite Generation Che Rose, Nurol Husna; Othman, Rozmie Razif; Zakaria, Hasneeza Liza; Suali, Anjila J; Jamal Abdul Nasir, Husna; Altmemi, Jalal
EMITTER International Journal of Engineering Technology Vol 13 No 2 (2025)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v13i2.979

Abstract

The Wingsuit Flying Search (WFS) algorithm is a newly developed global meta-heuristic algorithm. It is efficient and easy to implement, requiring no parameter tuning apart from the population size and the maximum number of iterations. Recently, WFS has been developed based on applying t-way strategies, where t represents the interaction strength. Despite the encouraging results, WFS's search strategy leans more toward local optima due to the narrowing of the boundary search space and the increased value of the search sharpness. Hybridising two or more algorithms enhances search performance by effectively balancing the strengths and mitigating the weaknesses of each method. Thus, this paper proposes a new hybrid Lévy Flight with Wingsuit Flying Search (WFS) algorithm called Enhanced Wingsuit Flying Search Algorithm (EWFS). EWFS uses a control mechanism to identify the best dynamic solution during runtime. The Lévy Flight motion helps the solution escape from local optima and improves the searching process when it gets stuck. Comparison between EWFS and WFS uses the benchmarking configuration of CA(N; 2, 5⁷), while the comparison with other metaheuristic algorithms is based on the following covering array configurations: CA(N; t, 3p), CA(N; t, v7), CA(N; 2, 2p), and CA(N; t, 210). The experimental result shows that EWFS is statistically better regarding test suite size reduction than the recent t-way strategies. It also offers improved results of 65% over the original WFS and resolves the issues of excessive exploitation and getting stuck in local minima or maxima.

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