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

作者： Winata, Ardiansyah Jaya Surjawan, Daniel Jahja Mawardi, Viny Christanti Tarumanagara University Faculty of Informatics Engineering Jakarta Indonesia Maranatha Christian University Faculty of Smart Engineering and Technology Bandung Indonesia

ISBN: (纸本)9798331513320

The increasing workload of educators, particularly in manual question creation, poses a significant challenge in modern education. Manual question creation demands time, effort, and a deep understanding of the material to ensure contextual and curriculum-aligned questions. To address this, an Automatic Question Generation (AQG) system was developed using extractive summarization combined with the PEGASUS and TextRank methods. The system leverages Natural Language Processing (NLP) and transformer-based large language models (LLMs) to efficiently generate relevant questions. The primary data source for this system was digital social studies (IPS) books from the Indonesian Ministry of Education. The evaluation was conducted using ROUGE Score metrics and human assessments. ROUGE analysis yielded an average F1 score of 0.87 (ROUGE-1), 0.83 (ROUGE-2), and 0.84 (ROUGE-L), demonstrating the system's effectiveness in capturing essential information. Human evaluations involving educators and students highlighted the relevance and contextual accuracy of generated questions, particularly for structured materials. The system generated questions within 2 to 6 minutes, showcasing its efficiency in reducing educators' workload. However, challenges remain in handling materials with implicit semantic relationships or nonlinear narratives, as PEGASUS struggles to maintain contextual relevance. This limitation may lead to irrelevant questions and answers, indicating a need for improved semantic understanding. This study concludes that the PEGASUS+TextRank AQG system is a promising tool to streamline question generation. Future improvements in semantic algorithms and broader training data are crucial to enhancing the system's reliability and adaptability to diverse educational contexts. © 2025 IEEE.

关键词： Semantics

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Development of a Micro Counselling Educational Platform Based on AI and Face Recognition to Prevent Students Anxiety Disorder

Development of a Micro Counselling Educational Platform Base...

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2024 International Conference on Electrical and Information Technology, IEIT 2024

作者： Putri Syarifa, Dhea Fanny Krisna Moeljono, Azarya Aditya Hidayat, Wahyu Nur Shafelbilyunazra, Alvalen Abednego, Victory Kuriakos Prasetya, Didik Dwi Universitas Negeri Malang Faculty of Engineering Department of Electrical Engineering and Informatics Malang Indonesia

ISBN: (纸本)9798331516888

The rapid development of Information and Communication Technology (ICT) has brought broad consequences for example in the fields of education, health but mental health is rarely considered especially among Indonesian teenagers. Traditional approaches, such as in-person consultations with mental health professionals, often fall short due to accessibility and affordability issues. This research developed MoodNow, an E-learning system with personalized mental support using face recognition and artificial intelligence for students in grades 10 to 11 of SMK Negeri 10 Malang. Built with Design Thinking, the platform is dedicated to offering real-time mental health checks and customized virtual counseling. The system architecture incorporates user-friendly interfaces, robust backend processing, and AI-driven mood detection via the Gemini API Usability tests were also conducted, which presented a very high detection accuracy on emotions of 90%. This exchange rate gave the platform an excellent usability score, scoring 85.9% thus characterizing the project as highly effective. MoodNow's integration of AI and face recognition technologies demonstrates its potential to transform mental health support by offering real-time, personalized interventions. Despite challenges such as privacy concerns and the need for improved emotion detection accuracy, MoodNow represents a significant advancement in providing accessible and effective mental health care. This research contributes to the Sustainable Development Goals (SDG 3) by promoting mental well-being and fostering a supportive educational environment. © 2024 IEEE.

关键词： Students

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Flow turbulence presented by different vegetation spacing sizes within a submerged vegetation patch

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Journal of Hydrodynamics 2023年第6期35卷 1131-1145页

作者： Chukwuemeka Kingsley John Jaan H.Pu Yakun Guo Prashanth R.Hanmaiahgari Manish Pandey Faculty of Engineering and Informatics University of BradfordBradfordUK Department of Civil Engineering Indian Institute of TechnologyKharagpurIndia National Institute of Technology(NIT) WarangalIndia

This study presents results from a vegetation-induced flow experimental study which investigates 3-D turbulence structure profiles,including Reynolds stress,turbulence intensity and bursting analysis of open channel *** vegetation densities have been built between the adjacent vegetations,and the flow measurements are taken using acoustic Doppler velocimeter(ADV)at the locations within and downstream of the vegetation *** different tests are conducted,where the first test has compact vegetations,while the second and the third tests have open spaces created by one and two empty vegetation slots within the vegetated *** reveals that over 10%of eddies size is generated within the vegetated zone of compact vegetations as compared with the fewer *** turbulence structures variation is also observed at the points in the non-vegetated *** findings from burst-cycle analysis show that the sweep and outward interaction events are dominant,where they further increase away from the *** effect of vegetation on the turbulent burst cycle is mostly obvious up to approximately two-third of vegetation height where this phenomenon is also observed for most other turbulent structure.

关键词： Velocity profiles acoustic Doppler velocimeter(ADV) turbulent intensities Reynolds stress turbulent bursting vegetated flow vegetation spacing sizes

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Double-crossed Bowtie Antenna-coupled Carbon Nanotube Microbolometer with Meander Structures at 1THz

Double-crossed Bowtie Antenna-coupled Carbon Nanotube Microb...

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2024 International Symposium on Antennas and Propagation, ISAP 2024

作者： Hasiholan, Matthew Gabriel Pangesti Aji, Arie Apriono, Catur Rahardjo, Eko Tjipto Department of Electrical Engineering Faculty of Engineering Universitas Indonesia Indonesia Department of Electrical Engineering Faculty of Engineering and Informatics Universitas Dian Nusantara Indonesia

ISBN: (纸本)9798350364774

A high reception of THz waves is important in the design of an antenna-coupled microbolometer, which is indicated by the ability to detect THz waves effectively from all directions. In this study, we implement the meander line structure in a carbon nanotube (CNT) based bolometer coupled with a double-crossed bowtie antenna. The structure is designed on a SiO2 substrate with an additional metal reflector at the bottom. The simulation results reveal the potential high responsivity of 281.5 kV/W and could effectively minimize the NEP down to 0.0145 pW/Hz1/2. These results demonstrate the prospect of a meander line bolometer structure to enhance THz wave absorption with great potential in the application of real-time imaging. © 2024 IEEE.

关键词： Terahertz waves

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Optimizing Feature Selection for Stock Price Prediction Using a Two-Stage Feature Selection Framework

Optimizing Feature Selection for Stock Price Prediction Usin...

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2024 Beyond Technology Summit on informatics International Conference, BTS-I2C 2024

作者： Priyatno, Arif Mudi Prasetya, Didik Dwi Widiyaningtyas, Triyanna Faculty of Engineering Universitas Negeri Malang Department of Electrical Engineering and Informatics Malang Indonesia

ISBN: (纸本)9798331508579

Stock price prediction with stock price history and technical indicators has challenges in high feature dimensions. Recursive feature elimination feature selection is one solution to overcome this. However, there are several disadvantages to recursive feature elimination. Depending on the machine learning model used, the highest feature ranking may not always be the most relevant, and it also requires lengthy computation time. This study proposes feature selection optimization for stock price prediction using a two-stage feature selection framework. Stage 1 feature selection uses a combination of minimum and maximum that are relevant to the concept of quotient and important random forest features. Stage 2 feature selection uses recursive feature elimination. The stock data used are PT Bank Central Asia Tbk (BBCA), PT Bank Mandiri (Persero) Tbk (BMRI), and PT Bank Tabungan Negara (Persero) Tbk (BBTN). The prediction results show that the RMSE in the proposed method gets the best value under 20 features, and the MAPE value in the proposed method gets a value that is always smaller than other methods. This proves that the Two-Stage Feature Selection Framework successfully optimizes feature selection for stock price prediction, thereby reducing the error rate of stock price prediction. © 2024 IEEE.

关键词： Feature Selection

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Case-Based Reasoning Method Implementation for Well Water Feasibility Recommendation System with Nearest Neighbor Algorithm 1st

Case-Based Reasoning Method Implementation for Well Water Fe...

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1st International Conference on Emerging Smart Cities, ICESC 2022

作者： Bororing, Jemmy Edwin Yumarlin, Mz Rahayu, Sri Setyorini, Agustin Fitriastuti, Fatsyahrina Saputra, Jeffry Andhika Informatics Engineering Faculty of Engineering Janabadra University Daerah Istimewa Yogyakarta Yogyakarta Indonesia

ISBN: (纸本)9789819911103

Water is the most important natural resource used by humans to meet the needs of life. And water is one of the media that can be used by pathogenic microorganisms as a place to breed and also as a temporary residence before moving to humans so tolluted water can cause health problems. The community must be able to know the requirements for drinking water, especially the use of well water that will be used suitable for consumption by the body. In addition, the quality of water in clean water supply facilities, namely well water that does not meet the requirements is also a problem that needs attention and is often found in the community. The aim of this research is to apply the case based reasoning method in the recommendation system for the feasibility of well water using the nearest neighbor algorithm. Measurement of the feasibility of well water uses physical parameters consisting of 6 attributes, namely (1) turbidity, (2) color, (3) dissolved solids, (4) temperature, (5) taste and (6) smell. In testing the system on 5 aspects of usability testing based on the results of questionnaires to 20 respondents, namely the auxiliary aspect gave a value of 71%, the interface aspect gave a value of 80%, the navigation aspect was 84%, the pedagogical aspect was 78% and the robustness aspect was 78%. With a total average of 78% overall test results which identify the well water recommendation system suitable for use by users. © 2024, Institute of Technology PETRONAS Sdn Bhd.

关键词： Case based reasoning

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Lightweight Deep Learning for Human Activity Recognition in Ambient Assisted Living

Lightweight Deep Learning for Human Activity Recognition in ...

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2024 Multimedia University engineering Conference, MECON 2024

作者： Bari, Ahsanul Farid, Fahmid Al Sarker, Md. Tanjil Mansor, Sarina Karim, Hezerul Abdul Bhuiyan, Md Roman Bannah, Hasanul Multimedia University Faculty of Engineering Cyberjaya63100 Malaysia Berlin School of Business and Informatics Faculty of Computer Science and Informatics Berlin12043 Germany

ISBN: (纸本)9798331530747

This Lightweight Deep Learning (LDL) for Multi-View Human Activity Recognition in Ambient Assisted Living Systems can significantly improve the conditions of daily activities for people living with the elderly, disabled, or simply needing assistance. Human Activity Recognition (HAR) is essential for these systems;currently, the task of identifying the wide array of human activities remains complicated. This study introduces an improved deep learning solution for recognizing human activity from multiple viewpoints in AAL environments. By utilizing skeleton data from various perspectives, such as the RHM-HAR-SK dataset, we demonstrate useful improvements in accuracy for activity recognition when compared with single-view methods. Our LeNet and Vision Transformer models have been carefully designed to excel with skeletal data, delivering outstanding performance while keeping computational requirements to a minimum. In this research study also highlights the benefits of multi-view human activity recognition in ambient assisted living and emphasizes the effectiveness of efficient deep learning models in enabling assistive technologies that are real-time and resource friendly. These advancements support more independence and safety by supporting the development of AAL systems that are more sensitive and intelligent. © 2024 IEEE.

关键词： Active learning

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Improved QT ınterval estimation using conditional generative adversarial networks

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Neural Computing and Applications 2024年第18期36卷 10777-10789页

作者： Al−Zaben, Awad Al−Abed, Mohammad Biomedical Systems and Medical Informatics Department Hijjawi Faculty of Engineering Technology Yarmouk University Irbid Jordan Biomedical Engineering Department Faculty of Engineering the Hashemite University Zarqa Jordan College of Engineering and Technology American University of the Middle East Egaila54200 Kuwait

QT interval carries essential information about ventricular depolarization and repolarization. Therefore, in many investigations, it is vital to determine the QT interval accurately and monitor the corresponding QT interval variations. This paper presents a novel implementation of the Generative Adversarial Networks (GAN) to predict the QT waveform interval from the electrocardiogram. The network then estimates the Q and T locations to determine the QT interval. The accuracy of the proposed method in measuring the QT interval was 94.10%. The procedure is evaluated against ground truth performed by expert physicians, and the limits of agreement are used as the measure of performance. The bias in the estimate of the Q locations was 2.92 ms, with 11.48 ms as an upper limit of agreement and − 5.65 ms as the lower limit. The bias in estimating the T location was 1.14 ms with an upper limit of 15.04 ms and a lower limit of − 12.79 ms. Moreover, the bias in the estimate of the QT interval was − 7.10 ms with an upper limit of 50.17 ms and a lower limit of − 64.38 ms. These limits of agreements are clinically acceptable and show that the estimate of the QT interval agrees with the expert’s annotations. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Electrocardiograms

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Enhancing Cybersecurity: Two-Phase Detection Approach for Intrusion Network for Anomaly Data

Enhancing Cybersecurity: Two-Phase Detection Approach for In...

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2024 International Conference on Artificial Intelligence and Mechatronics System, AIMS 2024

作者： Java, Muhammad Iskandar Shabrina, Ulima Inas Wiliyanti Fahmi, Riza Nidhom Pratomo, Baskoro Adi Institut Teknologi Sepuluh Nopember Faculty of Informatics Engineering Surabaya Indonesia

ISBN: (纸本)9798350350524

This research explores the performance of two-phase classification methods in intrusion detection systems (IDS) with various algorithms, including SVM + Naive Bayes, Random Forest + KNN, and other combinations. This research validates the superiority of combination models over individual ones, highlighting RF+KNN as the most effective combination, followed by RF+SVM and SVM+NB. These findings offer valuable insights for cybersecurity practitioners seeking to enhance security measures by selecting balanced model combinations based on performance metrics and computing resources. The RF, KNN, and RF + KNN methods emerge as optimal choices, boasting an accuracy, F1 score, recall, and precision of 99.998%. This combination proves to be highly reliable in minimizing identification errors and detecting potential threats. While the Naive Bayes method demonstrates swift processing times, its detection efficacy lags significantly behind RF, KNN, and RF + KNN. This research underscores the critical importance of strategic model selection for optimal performance in intrusion detection systems. © 2024 IEEE.

关键词： Intrusion detection

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Accurate contactless measurement of triangular objects composed of 3D point clouds of low quality and unclear rotation 17

Accurate contactless measurement of triangular objects compo...

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17th IEEE International Scientific Conference on informatics, informatics 2024

作者： Matousek, Radek University of Pardubice Faculty of Electrical Engineering and Informatics Pardubice Czech Republic

ISBN: (纸本)9798350387674

Image processing has become an important tool in various fields of human activity. One of its important applications lies in the determination of the size and dimensions of an object. Thus, in the present paper, we deal with the contactless measurement of triangular components, which was found to have certain limitations once performed on a production line in real time. These are (i) low quality output with unclear rotation in the space of a 3D scanner (missing accurate Cartesian coordinate system rotation data) and (ii) fully autonomous measurement for the selected shape of the component (no manual data tweaking as possible in the laboratory). Besides measurement, the application program contains preprocessing, such as segmentation or removing outliers. But we focus on just the key problem of measurement, i.e., finding the best-fitting hyperplane through three preprocessed point clouds and measuring the sides of the triangle projected onto the hyperplane. The problem is solved by orthogonal linear regression utilizing the Principal Component Analysis approach. © 2024 IEEE.

关键词： Image segmentation

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