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8th International Conference on electrical, Electronics and Information engineering, ICEEIE 2023

作者： Akbar, Muhammad Iqbal Smaragdina, Azhar Ahmad Ningrum, Gres Dyah Kusuma Fritama, Muhammad Anandha Pratama, Wahyu Setyo Universitas Negeri Malang Electrical and Informatics Engineering Department Malang Indonesia

ISBN: (纸本)9798350340501

In this study, Tech Zone Plaza conducted a testing phase using the system usability scale method, which is used with the aim of measuring how good a user experience is from software. The results of this test is used as a reference for improving and adjusting the user interface based on the result and input from the test. Testing Tech Zone Plaza e-commerce uses System Usability Scale (SUS) Methods. There are several steps that must be followed in conducting a system usability scale. The steps are divided into 3 parts, Preparation, Execution, and Analysis The overall average SUS score is 74, on a scale of 0 to 100, indicating a moderately positive perception of the system's usability. According to the findings of this study, SUS ratings and individual responder feedback provide essential information for evaluating the system's usability. They serve as critical indications of strengths and shortcomings, highlighting areas for improvement and informing decision-making to improve the user experience. A full knowledge of the system's usability may be obtained by supplementing quantitative data from SUS scores with qualitative input from users, resulting in a system that better matches user expectations and requirements. © 2023 IEEE.

关键词： User interfaces

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Measuring the Level of e-Commerce Usability in the electrical and informatics engineering department Using the System Usability Scale

Measuring the Level of e-Commerce Usability in the Electrica...

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International Conference on electrical, Electronics and Information engineering (ICEEIE)

作者： Muhammad Iqbal Akbar Azhar Ahmad Smaragdina Gres Dyah Kusuma Ningrum Muhammad Anandha Fritama Wahyu Setyo Pratama Electrical and Informatics Engineering Department Universitas Negeri Malang Malang Indonesia

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A Comparative Study of Machine Learning Algorithms for Graduation Prediction in electrical engineering and informatics department

A Comparative Study of Machine Learning Algorithms for Gradu...

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International Conference on informatics and Computing (ICIC)

作者： Azhar Ahmad Smaragdina Didik Dwi Prasetya Wahyu Nur Hidayat Gulsun Kurubacak Cakir Utomo Pujianto Putrinda Inayatul Maula Department of Electrical Engineering and Informatics State University of Malang Malang Indonesia Distance Education Department Anadolu University Eskisehir Turkiye Department of Electrical Engineering and Informatics Universitas Negeri Malang Malang Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Accurate prediction of students’ graduation time is a significant challenge for academic institutions, especially in the context of optimizing educational outcomes and resource allocation. However, there is a research gap in identifying which machine learning algorithms are best suited for this task, particularly in the electrical and informatics engineering department. This study addresses this gap by evaluating the performance of various machine learning algorithms in predicting students’ graduation time. Several algorithms, including Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machines (SVM), and Naive Bayes, were applied to a dataset consisting of academic and demographic records of students from a university in Indonesia. The evaluation used performance metrics such as accuracy, precision, recall, and F1-score. The results demonstrate that LR, KNN, DT, RF, and SVM exhibited comparable accuracy rates of 74%, with a weighted average F1-score of 0.85, indicating these algorithms are effective in classifying data. In contrast, Naive Bayes, while showing superior speed with an execution time of 0.018322 seconds, achieved lower performance with an accuracy of only 39% and a weighted average F1-score of 0.44. These findings suggest that selecting an algorithm should balance the trade-off between accuracy and time efficiency. For scenarios where both are important, LR and DT are optimal choices, while Naive Bayes may be suitable for faster processing at the expense of accuracy.

关键词： Support vector machines Radio frequency Accuracy Machine learning algorithms Nearest neighbor methods Prediction algorithms Bayes methods Classification algorithms Resource management informatics

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Techno-economic modeling and analysis of a PV EV charged with battery energy storage system (BESS) on Kalimantan Island

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Global Energy Interconnection 2025年第2期8卷 225-239页

作者： Aripriharta Adiguna, Satria Afandi, Arif N. Bagaskoro, Muhammad Cahyo Department of Electrical Engineering and Informatics Faculty of Engineering State University of Malang Malang65145 Indonesia Indonesia

This research examines the optimal combination of solar panel and battery capacity in hybrid systems in 11 cities on the island of Borneo, utilizing the region's significant solar energy potential and high irradiation levels. This research analyses the optimal combination of solar panels and battery capacity in 11 cities in Kalimantan using particle swarm optimization (PSO) and grey wolf optimization (GWO) algorithms to maximize energy output, reduce levelised energy costs, and maximally reduce carbon emissions. Results show Tarakan as the most optimal location, generating 215,804.88 kWh for IDR 916.9/kWh and lowering emissions by 435,884.29 kgCO2e, while Samarinda is the least optimal location. Economically, electricity tariffs of IDR 2,466.78/kWh and IDR 2,000/kWh generate a positive Net Present Value (NPV) with a payback period (PP) of 9–12 years, while a tariff of IDR 1,500/kWh is considered unfavorable. The findings demonstrate the effectiveness of PSO and GWO in optimizing the renewable energy system and confirm the project's financial viability, with a positive NPV and reasonable PP. Implementing renewable energy systems in Kalimantan Island can improve energy efficiency and significantly reduce carbon emissions, supporting environmental sustainability goals. © 2025 Global Energy Interconnection Group Co. Ltd.

关键词： Zero-carbon

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Digital-Twin-Enhanced Deep Reinforcement Learning for Intelligent Omni-Surface Configurations in MU-MIMO Systems

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IEEE Internet of Things Journal 2025年第9期12卷 13005-13020页

作者： Ye, Xiaowen Yu, Xianghao Fu, Liqun Xiamen University School of Informatics Department of Communication Engineering Xiamen361005 China City University of Hong Kong Department of Electrical Engineering Hong Kong

Intelligent omni-surface (IOS) is a promising technique to enhance the capacity of wireless networks, by reflecting and refracting the incident signal simultaneously. Traditional IOS configuration schemes, relying on all subchannels’ channel state information and user equipments’ mobility, are difficult to implement in complex realistic systems. Existing works attempt to address this issue employing deep reinforcement learning (DRL), but this method requires a lot of trial-and-error interactions with the external environment for efficient results and thus cannot satisfy the real-time decision making. To enable model-free and real-time IOS control, this article puts forth a new framework that integrates DRL and digital twins. As a first step, deep reinforcement learning IOS (DeepIOS), a DRL based IOS configuration scheme with the goal of maximizing the sum data rate, is developed to jointly optimize the phase-shift and amplitude of IOS in multiuser multiple-input-multiple-output (MU-MIMO) systems. Thereafter, in order to further reduce the computational complexity, DeepIOS introduces an action branch architecture, which decides two optimization variables in parallel in a separate fashion. Finally, a digital twin module is constructed through supervised learning as a preverification platform for DeepIOS, such that the decision making’s real-time can be guaranteed. The formulated framework is a closed-loop system, in which the physical space provides data to establish and calibrate the digital space, while the digital space generates a large number of experience samples for DeepIOS training and sends the trained parameters to the IOS controller for configurations. Numerical results show that compared with random and MAB schemes, the proposed framework attains a higher data rate and is more robust to different settings. Furthermore, the action branch architecture reduces DeepIOS’s computational complexity, and the digital twin module improves DeepIOS’s convergence speed

关键词： Deep reinforcement learning

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ALCS-PP: Adaptive Latency Control Scheme for Packet Pacing in Self-Clocked Congestion Control

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IEEE Access 2025年 13卷 77080-77123页

作者： Dhia Zubaydi, Haider Samir Jagmagji, Ahmed Molnár, Sandor Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Telecommunications and Artificial Intelligence Budapest1111 Hungary

Effective congestion control is crucial for maintaining network stability, especially in dynamic environments. Self-Clocked Rate Adaptation for Multimedia (SCReAM) is a hybrid loss and delay-based algorithm for real-time multimedia transmission. It encounters challenges with static pacing and low latency under fluctuating round-trip time (RTT) and jitter, resulting in ineffective bandwidth utilization. To address these issues, this paper proposes an Adaptive Latency Control Scheme for Packet Pacing (ALCS-PP), a novel congestion control scheme featuring dynamic rate adjustment and adaptive packet pacing. The evaluation process is divided into three phases. The first phase involves initial performance evaluation using default parameters to establish a baseline comparison. The next phase explores parameter-specific experiments, where individual parameters such as target latency, jitter thresholds, and rate scaling factors are tuned to assess their impact on performance. The last phase investigates multi-parameter combinations to optimize the overall system configuration under various network conditions. The proposed scheme effectively reduced network queue delay (NQD) by up to 21.91% and smoothed RTT (sRTT) by 13.44%, ensuring better responsiveness in real-time multimedia transmission. These improvements are achieved through adaptive tuning using dynamic soft clamping and gradual corrections, minimizing performance oscillations. Additionally, the scheme maintains metrics such as network throughput (NThput), congestion window (CWND), and bytes in flight (BiF) close to SCReAM’s baseline values, ensuring stable performance. The experimental evaluation highlights the algorithm’s capability to balance delay reduction and network stability, making it suitable for delay-sensitive applications such as 5G, Internet of Things (IoT), and real-time multimedia streaming. © 2013 IEEE.

关键词： Adaptive control systems

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Integrating Gen3 Blockchain into a Transactive Energy Market for DERs Orchestration

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IEEE Transactions on Industry Applications 2025年第3期61卷 5103-5115页

作者： Ali, L. Azim, M. Imran Peters, J. Ojha, Nabin B. Simoes, M.G. Muyeen, S.M. Curtin University Department of Electrical Engineering Bentley Australia School of Engineering RMIT University Melbourne Australia aculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Berlin Melbourne Germany Department of Energy Environment and Climate Change Asian Institute of Technology Pathum Thani Thailand Department of Electrical Engineering University of Vaasa Vaasa Finland Department of Electrical Engineering Qatar University Doha Qatar

This paper presents the integration of third generation (Gen3) blockchain technology into a transactive energy market (TEM) for enabling secure and efficient peer-to-peer (P2P) energy trading among diverse participants, including prosumers with photovoltaic (PV) systems and battery energy storage systems (BESS), as well as electric vehicle (EV) owners. The primary motivation behind this work is to streamline transactive energy markets by reducing reliance on third-party intermediaries and enhancing energy self-sufficiency. The TEM utilizes blockchain's decentralized ledger for transparent transactions, with an advanced trading engine that matches participants' energy and price bids based on forecasted profiles and optimizes local energy use while minimizing costs. The major contributions of this study include: (1) the development of a scalable and efficient P2P trading framework leveraging Proof of Stake (POS) and Proof of History (POH) consensus mechanisms based on Gen3 blockchain, and (2) a comprehensive performance analysis resulting in considerable transaction time, throughput and cost reductions compared to traditional business-as-usual models. Numerical simulations reveal a 24% and 23% reduction in peak grid imports and exports, translating into financial and environmental benefits for all stakeholders, including prosumers, network operators, and retailers. © 2025 IEEE.

关键词： Cost reduction

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Harnessing YOLO for Loose Fruits Detection: Boosting Productivity in Palm Oil Plantations

Harnessing YOLO for Loose Fruits Detection: Boosting Product...

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2025 International Conference on Advancement in Data Science, E-learning and Information System, ICADEIS 2025

作者： Warni, Elly Indrabayu Achmad, Andani Syahsir, Aldilah Rezki Rhamadani Universitas Hasanuddin Doctoral Program of Electrical Engineering Department Informatics Department Makassar Indonesia Universitas Hasanuddin Electrical Engineering Department Makassar Indonesia Universitas Hasanuddin Informatics Department Makassar Indonesia

ISBN: (纸本)9798331513320

This study explores the use of the YOLOv5 algorithm to detect loose fruits palm oil in plantations, improve collection efficiency, and reduce reliance on manual labor. Loose fruits have high economic value due to their oil content, but they are often not collected, resulting in financial losses and the growth of weeds around oil palm trees. Conventional manual methods are often ineffective;thus, automatic detection is required. The dataset comprises 525 images of loose fruits, covering complex conditions on plantations, and secondary data from Roboflow, including nontarget objects, such as rocks and dry leaves, to train the model to accurately distinguish loose fruits. During training, several parameters were optimized: batch size, learning rate, optimizer type, weight decay, and momentum, which showed that small batch sizes provided higher mAP50 values, whereas large batches increased mAP50:95, indicating the ability to detect objects at higher levels of complexity. Testing in a scenario with loose fruits clearly visible and partially covered with leaves or grass showed that YOLOv5 could detect loose fruits well despite some constraints in the obstructed environment. The results of this study show that, with appropriate parameter settings, YOLOv5 can be a reliable basis for automated loose fruit collection systems, potentially improving operational efficiency in oil palm plantations and contributing to the development of automation in the agricultural sector. © 2025 IEEE.

关键词： Fruits

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A Self-adapting Pixel Antenna - Substrate Lens System for Infrared Frequencies

Periodica Polytechnica Electrical Engineering and Computer S...

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Periodica Polytechnica electrical engineering and Computer Science 2025年第1期69卷 14-25页

作者： Shubbar, Mustafa Rakos, Balázs Department of Automation and Applied Informatics Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Műegyetem rkp. 3. BudapestH-1111 Hungary

In this work, we propose a concentrator lens - antenna arrangement, potentially suitable for infrared sensing and energy harvesting rectenna systems. The structure consists of a pixel antenna, self-adapting for the direction of the incident infrared radiation, and a concentrator lens, both optimized for the mid-infrared spectrum. The silicon substrate lens is situated above the pixel antenna, enabling the concentration of the incident light on the antenna;silicon was chosen due to its transparency in the IR spectrum. We examine how various parameters of the lens, in conjunction with the different states of the pixel antenna, affects the performance of the system. The simulations show that the gain of the arrangement increases considerably in correlation with the radius of the lens. The results suggest that the energy conversion efficiency of infrared rectenna systems can be enhanced by several orders of magnitude with the utilization of the proposed arrangement. © 2025 Budapest University of Technology and Economics. All rights reserved.

关键词： Rectennas

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xCViT:Improved Vision Transformer Network with Fusion of CNN and Xception for Skin Disease Recognition with Explainable AI

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Computers, Materials & Continua 2025年第4期83卷 1367-1398页

作者： Armughan Ali Hooria Shahbaz Robertas Damaševicius Department of Electrical Engineering Wah Engineering CollegeUniversity of WahWah Cantt47040Pakistan Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Applied Informatics Vytautas Magnus UniversityKaunas44309Lithuania

Skin cancer is the most prevalent cancer globally,primarily due to extensive exposure to Ultraviolet(UV)*** identification of skin cancer enhances the likelihood of effective treatment,as delays may lead to severe tumor *** study proposes a novel hybrid deep learning strategy to address the complex issue of skin cancer diagnosis,with an architecture that integrates a Vision Transformer,a bespoke convolutional neural network(CNN),and an Xception *** were evaluated using two benchmark datasets,HAM10000 and Skin Cancer *** the HAM10000,the model achieves a precision of 95.46%,an accuracy of 96.74%,a recall of 96.27%,specificity of 96.00%and an F1-Score of 95.86%.It obtains an accuracy of 93.19%,a precision of 93.25%,a recall of 92.80%,a specificity of 92.89%and an F1-Score of 93.19%on the Skin Cancer ISIC *** findings demonstrate that the model that was proposed is robust and trustworthy when it comes to the classification of skin *** addition,the utilization of Explainable AI techniques,such as Grad-CAM visualizations,assists in highlighting the most significant lesion areas that have an impact on the decisions that are made by the model.

关键词： Skin lesions vision transformer CNN Xception deep learning network fusion explainable AI Grad-CAM skin cancer detection

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