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4th IEEE International Conference of Computer Science and information technology, ICOSNIKOM 2022

作者： Nababan, Adli A Sutarman Zarlis, Muhammad Nababan, Erna B Universitas Sumatera Utara Faculty of Computer Science and Information Technology Department of Computer Science Medan Indonesia Universitas Sumatera Utara Faculty of Mathematics and Natural Sciences Department of Mathematics Medan Indonesia Binus Graduate Program-Master of Information Systems Management Bina Nusantara University Department of Information Systems Management Jakarta Indonesia

ISBN: (纸本)9798350399073

Air pollution makes it worse in populated regions. Indonesia's major cities are also impacted by air pollution. Due to rising traffic, material consumption by vehicles, industrial growth, land burning, and garbage collection, air quality has altered significantly. Accurate measurement and classification of air quality are required. Results of accurate classification aid in forming governmental regulations. To reach the criteria for livable air quality, we intend to manage controls. Some of the characteristics of air pollution include particulate (PM10) or PM25, sulfides (in the form of SO2), carbon monoxide (CO), ozone (O3), and nitrites (NO2). Using the XGBoost algorithm and synthetic minority oversampling (SMOTE) methods based on the Air Pollution Standard Index (ISPU) category, this study seeks to uncover factors that affect air quality. XGBoost, an ensemble machine learning approach built on a decision tree and applying a gradient reinforcement framework, is the classification algorithm in use. Air quality categorization has been tested and shown to work with the XGBoost algorithm. The dataset used in this study is air quality in 2021 which was collected by the Jakarta Environmental Ministry The proposed classification model will be evaluated using the Repeated k-fold cross-validation method for objective results. The results of the study prove that SMOTE and XGBoost have better performance than using only the XGBoost method in predicting air quality, where the overall accuracy value of SMOTE with XGBoost is 99.60%, The overall precision value is 99.60%, the overall recall value is 99.60% and the overall f1-score is 99.60% and the overall ROC AUC value is 99.96%. The overall SMOTE method with the proposed XGBoost has given better performance in air pollution prediction. © 2022 IEEE.

关键词： Air quality

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Towards Trustworthy Aircraft Safety: Explainable AI for Accurate Incident and Accident Predictions 20th

Towards Trustworthy Aircraft Safety: Explainable AI for Accu...

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20th International Conference on the Economics of Grids, Clouds, Systems, and Services, GECON 2024

作者： Amin, Maryam Noor, Umara Fatima, Manahil Rashid, Zahid Altmann, Jörn Department of Software Engineering Faculty of Computing and Information Technology International Islamic University Islamabad44000 Pakistan Technology Management Economics and Policy Program College of Engineering Seoul National University Seoul Korea Republic of College of Engineering Seoul National University Seoul Korea Republic of Integrated Major in Smart City Global Convergence Seoul National University Seoul Korea Republic of

ISBN: (纸本)9783031812255

Despite technological advancements, ensuring aircraft safety remains a challenge, however, Machine learning (ML)-based approaches for predicting future incidents play a crucial role in addressing flight safety. As ML models increase in complexity, their decision-making process becomes less transparent, posing significant challenges to trustworthiness. While simpler models demonstrate lower accuracy, more intricate models such as deep neural networks achieve higher accuracy but sacrifice interpretability. In this study, we enhance trustworthiness in aircraft safety prediction by leveraging a dataset of past accidents and incidents to prevent similar accidents from occurring in the future. To achieve this, we apply Random Forest and Extreme Gradient Boosting models to classify different categories of aircraft incidents. Additionally, we apply two powerful explainable artificial intelligence (XAI) techniques: Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive exPlanations (SHAP) to provide insights into both local and global predictions made by the models. Notably, our results reveal high accuracy in these predictions while maintaining trustworthiness. This research contributes to the advancement of XAI and offers valuable insights for safety-critical applications and decision support systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Aircraft accidents

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Teacher development in robot and IoT knowledge, skills, and attitudes with the use of the TPACK-based Support-Stimulate-Seek approach

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Interactive Learning Environments 2023年第9期31卷 5811-5830页

作者： Hu, Chih-Chien Yeh, Hui-Chin Chen, Nian-Shing Department of Information Management Tatung University Taipei City Taiwan Department of Applied Foreign Languages National Yunlin University of Science and Technology Yunlin Douliou Taiwan Institute for Research Excellence in Learning Sciences Program of Learning Sciences National Taiwan Normal University Taipei City Taiwan

In-service teachers’ professional development regarding technology adoption in education is often focused on only one specific technology. To maximize the utilization of technologies, teachers need to be able to leverage different technologies seamlessly. Technological Pedagogical Content Knowledge (TPACK) is one of the most important frameworks depicting teachers’ competences for effectively teaching with technology. The TPACK Support-Stimulate-Seek model (3S-TPACK) was used to design training programs with learning activities using robots, IoT-based sensors, actuators, tablets, and computer programming tools. The results show that the training programs designed by adapting the 3S-TPACK model not only can improve the teachers’ learning outcomes but also can support positive attitudes toward adopting robots and IoT technologies in the teaching and learning process. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： 21st century abilities interdisciplinary projects Teacher education teacher professional development technological pedagogical content knowledge technology in education

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Quality Analysis the E-PASTI System to Determine the Condition of the System and information Quality

Quality Analysis the E-PASTI System to Determine the Conditi...

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2019 International Conference of Science and information technology in Smart Administration, ICSINTeSA 2019

作者： Nugraha, Bayu Marleny, Finki Dona Sfenrianto, Sfenrianto Edwina, Cinthya Information System Program Faculty of Science and Technology Sari Mulia University South Kalimantan Banjarmasin Indonesia Information Systems Management Department BINUS Graduate Program-Master of Information Systems Management Bina Nusantara University Jakarta11480 Indonesia

The purpose of this study is to evaluate the quality of secretarial, financial and staffing information systems at the Directorate General of AHU, known as the E-PASTI system. There are two variables analysed, namely system quality and information quality. Data collection using questionnaires that distributed to 123 employees who used the E-PASTI system but returned 93 questionnaires. Data quality analyst uses descriptive quantitative method. The results of the study are several conditions related to the system and information quality of E-PASTI. The quality can be a reference for the AHU Directorate General to improve the quality of E-PASTI that suits the needs of employees. © Published under licence by IOP Publishing Ltd.

关键词： Personnel

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Mapping the Scientific Landscape of Integrated Scheduling Towards Industry 5.0 with A Bibliometric Analysis 6

Mapping the Scientific Landscape of Integrated Scheduling To...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Putri Ardyanti, Anak Agung Ayu Xie, Zhiqiang Karnyoto, Andrea Stevens Pardamean, Bens Estiyanti, Ni Made School of Computer Science and Technology Harbin University of Science and Technology Harbin150000 China Faculty of Information Technology and Design Primakara University Bali 80226 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia School of Management and Business University of Debrecen Debrecen Hungary

The industry is rapidly transitioning from the 4.0 era to the 5.0 era, prompting renewed interest among scholars in scheduling problems. They allow operations to process and assemble various components simultaneously. This bibliometric analysis provides a comprehensive understanding of the diverse research perspectives on the potential challenges of integrated scheduling within the framework. We identified 357 Scopus articles and analysed co-occurrence keyword (CNK) by VOSViewer tools and Global Citation Score (GCS) by Scopus tools. The research data obtained from 1993 to 2023 mainly in the subject areas of Engineering, Computer Science, Maths, and others. CNK identified six cluster keywords, and nine most cited articles based on normalised GCS. These results contribute to our understanding of the development of scheduling algorithms, shifting from an emphasis on manufacturing transfers to contemporary demand-centric research directions. Furthermore, this research provides a valuable perspective for policymakers, plant personnel, and manufacturing managers to make informed decisions. © 2025 The Authors. Published by Elsevier B.V.

关键词： Job shop scheduling

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A model of service-oriented architecture of e-certification system to support boat registration and site visit inspection to support maritime safety and crew health inspection

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Marine Systems and Ocean technology 2023年第1-2期18卷 45-63页

作者： Saputra, Diaz Gaol, Ford Lumban Abdurachman, Edi Sensuse, Dana Indra Matsuo, Tokuro Department of Computer Science BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Computer Science Binus Graduate Program - Master in Information System Bina Nusantara University Jakarta11480 Indonesia Department of Research in Management Bina Nusantara University Jakarta Indonesia Faculty of Computer Science University of Indonesia Jakarta Indonesia Advanced Institute of Industrial Technology Tokyo Japan City University of Macau China Asia University Taichung Taiwan

This article describes the architecture of software services that provide registration of small boat data to build maritime safety. The proposed system architecture which so-called SIMKAPEL aims to support small boat registration for maritime safety. It is developed from a conventional service-oriented architecture (SOA) with an open standard for data interoperability between elements. The goal of this research is to implement and evaluate the proposed architecture to support small boat registration and e-certification. Several parts of the architecture have been implemented as a platform that provides core services and data sources to be used by Ministry of Transportation, Directorate General of Sea Transportation, and other government agencies. Finally, the proposed SIMKAPEL platform with the SOA architecture is evaluated for its benefits to users through the expert opinion survey to measure its efficiency. The analysis results showed that SOA-based SIMKAPEL is feasible to use as a boat e-certification system. Furthermore, experiment results provide three parameters (e.g., maritime safety, namely, cargo/passengers, boat safety facilities, and maritime safety systems) with efficiency values of 42%, 46%, and 42%, respectively. The parameter with the highest efficiency shows a big effect on the level of safety of the boat and passengers. Thus, the parameters that need to be prioritized and need boat owners’ attention are cargo/passenger, boat safety equipment, and maritime safety system. © 2023, The Author(s).

关键词： Boat e-certification Boat online certification Cloud-based service Integrated system SOA architecture

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Disease Data Modeling by Using Machine Learning Approach

Disease Data Modeling by Using Machine Learning Approach

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International Conference on information technology, Computer and Electrical Engineering (ICITACEE)

作者： Gunawan Wang Rakha Gusti Wardhana Farida Sibuea Anderes Gui Information Systems Management Department BINUS Graduate Program-Master of Information Systems Management Bina Nusantara University Jakarta Indonesia Head of Data Analytics department Data and Information Technology Centre (Pusdatin) Ministry of Health Jakarta Indonesia School of Information Systems Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350389302

ISBN: (纸本)9798350389319

Since Covid outbreak in 2020, Indonesian Ministry of Health (MoH) has intensively embraced digital transformation initiatives in healthcare management and decision-making process. The latest healthcare digital transformation roadmap 2024, has emphasized the use of AI technology in data analytics and support policy making. The article examines the use of common Machine Learning (AI) algorithms, such as: Logistic Regression, Random Forest, Support Vector Machine (SVM), Decision Tree, and XGBoost. The article applies these algorithms to analyse the common diseases faced by Indonesian such as Measles, Malaria, Leprosy, and Diphtheria. Those diseases are classical diseases, however, still infecting many people in large areas. The article takes a case study of national health profile within period 2017–2022, to study the data model of those diseases. The outcome of the article is expected to be used for further policy making.

关键词： Support vector machines Logistic regression Machine learning algorithms Malaria Measles Medical services Predictive models Prediction algorithms Decision trees Random forests

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NNSplit-SØREN: Supporting the model implementation of large neural networks in a programmable data plane

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Computer Networks 2023年 222卷

作者： Li, David Chunhu Maulana, Muhamad Rizka Chou, Li-Der Information Technology and Management Program Ming Chuan University Taoyuan City333321 Taiwan Department of Computer Science and Information Engineering National Central University Taoyuan City320317 Taiwan

Embedding neural network (NN) models in the data plane is one of the very promising and attractive ways to leverage the computational power of computer network switches. This method became possible with the advent of the P4 language controlling the programmable data plane. However, most data planes today have some constraints, such as a limited set of operations and limited memory size. The computational cost of training large-scale NNs is high. In addition, while complex large-scale NN architectures are often used to improve prediction accuracy, they affect the functional performance of the data plane because of factors such as numerous input parameters and complex model design. Therefore, determining how to reduce the performance cost incurred by implementing large NN models in the data plane is a critical issue that needs to be addressed. This research proposes a technique called Neural Network Split (NNSplit) to solve the performance problems of embedding a large NN in a data plane by splitting the NN layers across multiple data planes. To support layer splitting, a new protocol called SuppORting ComplEx Computation in the Network (SØREN) is also proposed. The SØREN protocol header carries the activation value and bridges the NN layers in all switches. A multi-class classification use case of network traffic is used as the context for the experimental analysis. Experimental results show that compared to non-splitting NN architectures, NNSplit can reduce memory usage by nearly 50% and increase network traffic throughput with the cost of a 14% increase in round-trip time. In addition, when the SØREN protocol is encapsulated into data packets, the average processing time of the switch is 773 µs, which has very little impact on the processing time of the packets. Experimental results also show that the proposed NNSplit–SØREN can support large NN models on the data plane with a small performance cost. © 2022 Elsevier B.V.

关键词： Cost reduction

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Preliminary Plan for Data Collection on Stakeholder information Interdependence in Area-Business Continuity management: A Case Study of Flood management in Thai Industrial Zones 23

Preliminary Plan for Data Collection on Stakeholder Informat...

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23rd International Symposium on Communications and information Technologies, ISCIT 2024

作者： Arayachookiat, Penpitcha Saengtabtim, Kumpol Kodaka, Akira Leelawat, Natt Tang, Jing Watanabe, Kenji Risk and Disaster Management Program Graduate School Chulalongkorn University Bangkok Thailand Chulalongkorn University Faculty of Engineering Department of Industrial Engineering Bangkok Thailand Chulalongkorn University Disaster and Risk Management Information Systems Research Unit Bangkok Thailand Graduate School of System Design and Management Keio University Kanagawa Japan International School of Engineering Chulalongkorn University Faculty of Engineering Bangkok Thailand Graduate School of Social Engineering Nagoya Institute of Technology Nagoya Japan

ISBN: (纸本)9798350353952

The 2011 flood in Thailand exposed significant vulnerabilities in industrial areas, highlighting the necessity for enhanced disaster risk management through Area-Business Continuity management (Area-BCM). This preliminary study focuses on identifying how stakeholders rely on information from each other to improve disaster preparedness and response. The research involves systematically identifying Area-BCM stakeholders and designing interview questions, which are evaluated by experts using the Index of Item-Objective Congruence (IOC) to ensure relevance and clarity. All interview questions surpassed the IOC threshold of 0.5, confirming their effectiveness in capturing information interdependencies. Expert feedback led to refinements in the questions, underscoring the importance of tailored data collection. This initial plan provides a critical foundation for understanding information interdependence and highlights the importance of well-designed data collection methods. The findings have significant implications for developing more resilient disaster management strategies in industrial areas, emphasizing the need for precise and relevant stakeholder communication to enhance Area-BCM effectiveness. © 2024 IEEE.

关键词： Area-Business Continuity management Flood Index of Item-Objective Congruence information Interdependence

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LayOut Loud: An AI-Powered Augmented Reality and Mobile Application for Room Interior Design and Layout Optimization

LayOut Loud: An AI-Powered Augmented Reality and Mobile Appl...

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Intelligent Cybernetics technology & Applications (ICICyTA), International Conference on

作者： John Raphael G. Cuevas John Kenneth R. Rili Anny Neolicia G. Santillan Vivien A. Agustin Mary Grace N. Gonzales Criselle J. Centeno Richard C. Regala Department of Information Technology College of Information Systems and Technology Management Pamantasan ng Lungsod ng Maynila Manila Philippines Department of Computer Science Associate Dean of College of Information Systems and Technology Management Pamantasan ng Lungsod ng Maynila Manila Philippines Department of Information Technology Faculty of College of Engineering and Technology Pamantasan ng Lungsod ng Maynila Manila Philippines Department of Information Technology Director of Graduate Program of College of Information Systems and Technology Management Pamantasan ng Lungsod ng Maynila Manila Philippines Department of Computer Science Computer Lab Administrator of College of Engineering and Technology Pamantasan ng Lungsod ng Maynila Manila Philippines

ISBN: (数字)9798331506490

ISBN: (纸本)9798331506506

LayOut Loud is an AI-powered augmented reality (AR) and mobile application designed to revolutionize room interior design by offering tailored, real-time solutions for layout optimization. The primary objective of this tool is to design AI-powered features that analyze user-provided room dimensions, furniture types, and style preferences. By leveraging advanced algorithms, LayOut Loud generates personalized design suggestions that cater to the individual needs and aesthetic tastes of users, ensuring that each interior design experience is customized and unique. A key goal of the application is to enhance user experience and decision-making through a clustering algorithm. This algorithm categorizes furniture pieces based on their suitability and appearance in living spaces, streamlining the selection process for users. By enabling the visualization of these items in a 3D model, users can make informed decisions about their interior design choices, ensuring that each piece contributes to the overall aesthetic and functional harmony of the space. Additionally, LayOut Loud focuses on improving its AR capabilities by allowing users to capture their room layouts and view virtual furniture arrangements in real time. This feature provides practical, interactive design solutions, empowering users to experiment with different configurations and instantly see how potential changes would appear in their actual living environments. Ultimately, LayOut Loud offers a user-friendly, immersive platform that transforms the way individuals approach interior design.

关键词： Solid modeling Three-dimensional displays Layout Decision making Clustering algorithms Real-time systems User experience Mobile applications Augmented reality Optimization

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