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9th IEEE Information Technology International Seminar, ITIS 2023

作者： Lestari, Sri Yulmaini Aswin Sulyono Nul Fikri, Ruki Rizal Informatics Engineering Study Program Faculty of Computer Science Bandar Lampung Indonesia Management Study Program Faculty of Economics Business Bandar Lampung Indonesia

ISBN: (纸本)9798350306835

Artificial intelligence was recently implemented in various fields. One of these applied artificial intelligence systems was the Performance Monitoring System "SiMoniK"for MSMEs. This system was designed through the C4.5 algorithm and the Porat-Rank method. The C4.5 algorithm was applied to classify the MSMEs into three classifications, e.g. the micro enterprise classification, the small enterprise classification, and the medium enterprise classification. Moreover, the Porat-Rank method was also applied to distribute the solving-problem recommendations and to improve the independence of MSMEs. The result of this study was that the C4.5 algorithm could classify the MSME data correctly in the form of an accuracy value of 99.2%. Besides, the Porat-Rank method could also provide recommendations in the form of outstanding quality with an NDCG mean value of 0.87. © 2023 IEEE.

关键词： Artificial intelligence

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Implementation of Multi Attribute Utility Theory Method for Government Assistance Recipient Recommendation System in Dadapayu Village, Semanu District, Gunung Kidul Regency 15

Implementation of Multi Attribute Utility Theory Method for ...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Yumarlin, M.Z. Rahayu, Sri Bororing, Jemmy Edwin Kurniawan, Safar Dwi Fitriastuti, Fatsyahrina Rozak, Muhamad Department of Informatics Faculty of Engineering Universitas Janabadra Indonesia Diploma Program in Computer Engineering Politeknik Harapan Bersama Indonesia

ISBN: (纸本)9798331300579

The Indonesian government is furnishing help in the form of" Bantuan Langsung Tunai(BLT)" or the Direct Cash backing program in order to make up for the recent increase in the price of energy oil painting(BBM), which has easily had an impact on the lives of the destitute and the general public. The BLT program is enforced throughcross-sectoral collaboration, where institutions unite grounded on their separate functions and duties. Aid distribution opinions are grounded on multiple applicable rudiments and aren't primarily determined by one criterion when the MAUT fashion is applied. A system's recommended feasibility score of 78 is attained from the evaluation findings of system testing, which were carried out using a questionnaire exercising the System Usability Scale(SUS) approach. With a score in the "Acceptable" range, the system is supposed good since it's adequately satisfactory.. © Grenze Scientific Society, 2024.

关键词： Electric grounding

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Designing a skilled soccer team for RoboCup: exploring skill-set-primitives through reinforcement learning

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Neural Computing and Applications 2025年 1-36页

作者： Abreu, Miguel Reis, Luís Paulo Lau, Nuno LIACC/LASI/FEUP Artificial Intelligence and Computer Science Laboratory Faculty of Engineering University of Porto Porto Portugal IEETA/LASI/DETI Institute of Electronics and Informatics Engineering of Aveiro Department of Electronics Telecommunications and Informatics University of Aveiro Aveiro Portugal

The RoboCup 3D soccer simulation league serves as a competitive platform for showcasing innovation in autonomous humanoid robot agents through simulated soccer matches. Our team, FC Portugal, developed a new codebase from scratch in Python after RoboCup 2021. The team’s performance relies on a set of skills centered around novel unifying primitives and a custom, symmetry-extended version of the proximal policy optimization algorithm. Our methods have been thoroughly tested in official RoboCup matches, where FC Portugal has won the last two main competitions, in 2022 and 2023. This paper presents our training framework, as well as a timeline of skills developed using our skill-set-primitives, which considerably improve the sample efficiency and stability of skills, and motivate seamless transitions. We start with a significantly fast Sprint-Kick developed in 2021 and progress to the most recent skill set, including a multi-purpose omnidirectional walk, a dribble with unprecedented ball control, a solid kick, and a push skill. The push addresses low-level collision scenarios and high-level strategies to increase ball possession. We address the resource-intensive nature of this task through an innovative multi-agent learning approach. Finally, we release the team’s codebase to the RoboCup community, providing other teams with a robust and modern foundation upon which they can build new features. © The Author(s) 2025.

关键词： Reinforcement learning

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AdaptVR: An Adaptive Learning VR System for Dental Students Training

AdaptVR: An Adaptive Learning VR System for Dental Students ...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Sherif, Meriam Barakat, Nahla Hamdy, Abeer The British University in Egypt Faculty of Informatics and Computer Science Department of Software Engineering Cairo Egypt The British University in Egypt Faculty of Informatics and Computer Science Department of Artificial Intelligence Cairo Egypt

ISBN: (纸本)9798350367560

The paper proposes 'AdaptVR' a virtual reality (VR) system designed to enhance dental training through realistic real tile simulations and adaptive learning environment, to overcome traditional training challenges in dental education. The system aims to provide personalized learning experiences, facilitating skill development and comprehensive assessments for students. The proposed system was tested by 17 dentistry students and 5 experts. AdaptVR framework is done for operative dentistry which is removing decay especially Class I and Class II of classification of caries. Results from the experiment revealed that 77.3% of participants exceed their expectations, 59.1% saw a positive effect on their dental skills, 90.9% reported higher levels of engagement, and 65% indicated that vibration and force feedback provided students with a genuine sensation. AdpatVR's experimental findings show an average accuracy of 50.85% for Class I, 51.7% for Class II, and 82.09% for the Class II box technique. The error percentage averages 49% for Class I, 48% for Class II, and 18% for Class II box technique. © 2024 IEEE.

关键词： Virtual environments

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Knowledge augmented significant language model-based chatbot for explainable diabetes mellitus prediction 19

Knowledge augmented significant language model-based chatbot...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Elfayoumi, Mazen AbouElazm, Mohamed Mohamed, Omar Abuhmed, Tamer El-Sappagh, Shaker Faculty of Computer Science and Engineering Galala University Egypt Faculty of Computer Science Goldsmith UOL United Kingdom College of Computing and Informatics Sungkyunkwan University Korea Republic of

ISBN: (纸本)9798331507817

This study proposes an innovative diabetes prediction chatbot that utilizes large language models (LLMs) to determine the likelihood of diabetes based on specific patient inputs. Unlike conventional machine learning models and in addition to providing precise, individualized, robust prediction of diabetes augmented by the percentage of its confidence, this chatbot provides detailed text-based explanations for its individual predictions and enhances user’s understanding by retrieving and presenting similar cases using case-based reasoning techniques. Utilizing key health indicators such as hypertension status, heart disease presence, smoking history, BMI, HbA1c level, and blood glucose levels, the chatbot not only predicts the presence of diabetes but also educates users about the underlying reasons for each prediction. The system's explanation module promotes transparency and trust in the predictive process. The proposed architecture integrates the retrieval augmented generation (RAG) technique with prompt engineering across multiple LLMs, including Llama 3.1, GPT-3.5, and Gemma2. This approach augments the chatbot’s responses with contextually relevant data from similar past cases, thereby enhancing both relevance and accuracy. Furthermore, RAG minimizes LLM hallucinations while enhancing them with up-to-date, precise, personalized medical information. The objective of this chatbot is to offer healthcare professionals and patients a dependable, educational instrument for early detection of diabetes supported by transparent AI principles. This will have a positive effect on preventative healthcare measures. © 2025 IEEE.

关键词： Prediction models

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Comparison of Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) Models on the Poverty Levels in Central Java in 2023

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IAENG International Journal of Applied Mathematics 2024年第12期54卷 2746-2757页

作者： Alya, Najma Attaqiya Almaulidiyah, Qothrotunnidha Farouk, Bailey Reshad Rantini, Dwi Ramadan, Arip Othman, Fazidah Engineering Department Data Science Technology Study Program Universitas Airlangga Indonesia Engineering Department Data Science Technology Study Program Faculty of Advanced Technology and Multi-discipline Universitas Airlangga Indonesia Information System Study Program Department of Industrial and System Engineering Telkom University Surabaya Campus Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia

Poverty is considered a serious global issue that must be immediately eradicated by Sustainable Development Goals (SDGs) 1, namely ending poverty anywhere and in any form. As a developing country, poverty is a complex issue faced by Indonesia. Tackling poverty in Indonesia has become a key focus of the government’s strategic priorities. This issue of poverty is experienced by all provinces in Indonesia, including Central Java. The poverty rate in Central Java stands at 10.77% or 3.79 million people. The causes of poverty are explained by several factors. Using these various factors, a mapping model can be conducted to determine the number of poor populations in Central Java. This research employs Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) approaches to compare the effectiveness of these models in analyzing the number of poor populations in Central Java in 2023. The kernel function weighting used in this study is Fixed Gaussian. The results showed that the MGWR model outperformed both the OLS regression and GWR models, achieving an Akaike Information Criterion (AIC) of 62.766, an R2 of 82.3%, and a Mean Squared Error (MSE) of 0.177. Consequently, it can be inferred that the MGWR model was more suitable for explaining the poverty levels in Central Java. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Logistic regression

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EXPLORING INFLUENTIAL FACTORS IN KNOWLEDGE SHARING WITHIN INDONESIAN PRISONS 22

EXPLORING INFLUENTIAL FACTORS IN KNOWLEDGE SHARING WITHIN IN...

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22nd International Conference on e-Society 2024, ES 2024 and 20th International Conference on Mobile Learning 2024, ML 2024

作者： Imandeka, Ejo Hidayanto, Achmad Nizar Suhartanto, Heru Pidanic, Jan Department of Computer Science Faculty of Computer Science University of Indonesia Depok Indonesia Department of Electrical Engineering Faculty of Electrical Engineering and Informatics Pardubice University Pardubice Czech Republic

The exchange of knowledge is widely recognized as a crucial aspect of effective knowledge management. When it comes to sharing knowledge within Prison settings, things get complicated due to various challenges such as limited resources, social stigma, isolation, communication barriers, and security concerns. Additionally, there's a scarcity of resources dedicated to promoting knowledge sharing in secure environments. This study aims to dig deep into the key factors that influence how knowledge is shared in Indonesian prisons. To navigate this intricate terrain, the study uses questionnaires distributed among Prison officers to understand the complex relationship between the identified critical factors and knowledge sharing in Prison. Leadership support, organizational culture, organizational structure, incentives, and individual beliefs emerge as pivotal elements in knowledge sharing. It's also noted that the connection between these critical factors and the intention to share knowledge is significantly influenced by organizational structure. Recognizing and addressing critical factors in knowledge sharing within prisons not only improves rehabilitation within prison walls but also transforms the broader criminal justice system. This approach fosters evidence-based practices, better communication, and a comprehensive understanding of factors leading to successful rehabilitation and reduced recidivism, ultimately creating a more just and effective system. © 2024 International Conferences e-Society 2024 and Mobile Learning 2024. All rights reserved.

关键词： Critical Factors Indonesia Knowledge Knowledge Sharing Prison

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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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Advancements in Multi-View Human Activity Recognition for Ambient Assisted Living

Advancements in Multi-View Human Activity Recognition for Am...

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2024 Multimedia University engineering Conference, MECON 2024

ISBN: (纸本)9798331530747

This Ambient Assisted Living (AAL) uses technology to improve the well-being, autonomy, and security of seniors and disabled individuals. AAL services depend on Human Activity Recognition to detect human behaviors from sensor data. Compared to single-view models, multi-view HAR, which aggregates data from multiple sensors, provides improved accuracy and reliable detection. In this review, we assess recent progress in multi-view HAR for AAL, focusing on the value of lightweight deep learning models. We cover the development of HAR systems, as well as the emergence of multi-view datasets and highly efficient deep learning models that are explicitly designed for AAL scenarios. Furthermore, we address problems such as data synchronization, data protection, and adaptability, as well as possible remedies like sensor fusion and transfer learning. As a result, we also demonstrate the potential of multi-view HAR to increase AAL services and outlining future research trends in providing adaptive, efficient, and privacy-aware systems. © 2024 IEEE.

关键词： Adversarial machine learning

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Energy Efficiency in Sustainable Software Development: Clean Code Approaches 4

Energy Efficiency in Sustainable Software Development: Clean...

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4th International Conference on Electrical engineering and computer science, ICECOS 2024

作者： Inayatullah Widhiarso, Wijang Stiawan, Deris Universitas Multi Data Palembang Faculty of Computer Science Department Program in Engineering Science Palembang Indonesia Universitas Sriwijaya Faculty Af Computer Science Department Program in Engineering Science Palembang Indonesia Universitas Multi Data Palembang Faculty of Computer Science Palembang Indonesia Universitas Sriwijaya Faculty Af Computer Science Palembang Indonesia

ISBN: (纸本)9798350368253

The use of technology and information devices contributes to global warming. This issue has also become a concern for UN institutions, as stated in international environmental agreements, which aim to stabilize greenhouse gas emissions in the atmosphere at a level that prevents anthropogenic and dangerous disturbances to the environmental system. This research uses a line of code derived from the tanjak dataset, a student project from 2003 to 2020, numbering 500. This research aims to apply energy consumption efficiency to reduce CO2 emissions produced using computer devices, especially in developing software. The approach used in this research is clean code, which can provide benefits in the form of a line of code that is easy to read, understand, test, and maintain so that it can create savings in energy use. In the test, the line of code used was 1,072,089, producing an energy of 82,152.18 Joules, minus the line of code comments, which was 76,750, producing an energy of 2,971.25 Joules. Initial research results show that the clean code approach can be used to save energy usage, with the result of the line of code used being 995,339 and producing 79,180 Joules of energy, so it can affect reducing CO2 emissions when implementing software development. © 2024 IEEE.

关键词： Greenhouse gas emissions

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