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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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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.

Page 44 of 44 | Total Record : 436

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