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7th International Conference of computer and Informatics Engineering, IC2IE 2024

作者： Wandy, Wandy Adi, Kusworo Ayu, Media Anugerah Diponegoro University Doctoral Program of Information Systems School of Postgraduate Studies Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Physics Semarang Indonesia Sampoerna University Faculty of Engineering and Technology Department of Computer Science South Jakarta Indonesia

ISBN: (纸本)9798331505530

Full-marathon and Half-marathon distances are categorized as road running. Full-marathon running is becoming increasingly popular, and Half-marathon is increasing worldwide in both sexes and all age groups. Some aspects might relate to Full-marathon and Half-marathon running performance during training and races. Technology also plays an essential role in supporting runners and running races. Technology like artificial intelligence (AI) now supports the running athlete, not only predicting performance and results. It can also be used later to help the coach generate training programs for the athlete. This research aimed to find many aspects of marathons and performance and analyze them to see if artificial intelligence could later support them. It used secondary data and a systematic literature review proposed by Kitchenham. Out of the 58 articles, 21 of them (36.21%) received a score of 1 from Q1. Additionally, 19 articles (32.76%) received a score of 1 from both Q2 and Q3. Among the 58 articles, 9 (15.52%) received a total score of 3, with all three Q1, Q2, and Q3 scores being 1. This indicates that artificial intelligence will likely support the content of these nine articles. Several factors were also discovered to be connected to marathons and athletic performance. These findings suggested that additional investigation into marathons and performance, later backed by artificial intelligence, remained pertinent and essential. © 2024 IEEE.

关键词： artificial intelligence aspects marathon performance

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Teachers and Mobile Learning Perception: Towards a Conceptual Model of Mobile Learning for Training

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Procedia - Social and Behavioral sciences 2015年 176卷 425-430页

作者： Iin Karmila Yusri Robert Goodwin Carl Mooney School of Computer science Engineering and Mathematics Flinders University Australia Computer and Networking Engineering Study Program Ujung Pandang State of Polytechnic Indonesia

Before designing and implementing a mobile learning system, it is important to ascertain the perception of future users towards mobile learning since their perception will influence their willingness and readiness to use the system. This research investigated the perception of teachers in Indonesia of mobile learning to ascertain their readiness to engage in mobile learning for training. This study conducted a survey with 308 high school teacher respondents. The results show that teachers in Indonesia had positive perception of mobile learning and were looking forward to engage in mobile learning. Financial and device issues were not obstacles for the teachers to participate in a mobile learning environment. Interestingly, the knowledge and readiness for mobile learning of teachers who taught ICT subject was lower than teachers with mathematics, science, and other subjects. Findings obtained from this study are used as a base for designing and developing a mobile learning system for ICT training for teachers in Indonesia.

关键词： teachers mobile learning perception training system

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Air Particulate Matters Auto-Rule-Based Labeling to Support Long-Distance Run Environment Data Classification 13

Air Particulate Matters Auto-Rule-Based Labeling to Support ...

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13th IEEE Symposium on computer Applications and Industrial Electronics, ISCAIE 2023

ISBN: (纸本)9798350347319

Sports science is an interdisciplinary and multidisciplinary science that strives to increase athletic performance and endurance. Sport science recognizes and prevents injuries. Sensors and statistics formalize Sports science. Runners need coaches and teams to support them before, during, and after the run race. Coaches generate running training plans to boost performance. Running race performances may be impacted by air pollution exposure while training, so coaches should consider limiting air pollution exposure when training. One of the external factors is Particulate Matter (PM2.5 and PM10). Sensors connecting to the Internet can record external factors and produce csv data. The foundation of supervised machine learning is the labeling process. Labeling a set of data is one of the laborious and time-consuming phases in every machine-learning application because it requires verifying the accuracy of the labels and making any necessary revisions. This research aimed to find a solution to automatically label numerous air particulate matter raw data using a rule based on parameters to reduce manual work, human errors and faster processes. This labeled data will later be used for supervised machine learning classification to support the coach in generating training programs for the runners in a Sports Information System. Based on Indonesia Air Quality index rule-based approach, labeled text data in csv has been generated and tested with PM2.5 and PM10 parameters in three scenarios with a 100% success rate. It was possible to automate the labeling process, and it explained how automation results in fast and accurate results. © 2023 IEEE.

关键词： Classification (of information)

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Caira - A Creative Artificially-Intuitive and Reasoning Agent as conductor of telematic music improvisations

Caira - A Creative Artificially-Intuitive and Reasoning Agen...

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131st Audio Engineering Society Convention 2011

作者： Braasch, Jonas Bringsjord, Selmer Kuebler, Colin Oliveros, Pauline Parks, Anthony Van Nort, Doug Graduate Program in Architectural Acoustics Rensselaer Polytechnic Institute School of Architecture Troy NY 12180 United States Laboratory Department of Cognitive Science Rensselaer Polytechnic Institute Troy NY 12180 United States Laboratory Department of Computer Science Rensselaer Polytechnic Institute Troy NY 12180 United States Arts Department Rensselaer Polytechnic Institute Troy NY 12180 United States

ISBN: (纸本)9781618393968

This paper reports on the architecture and performance of the Creative Artificially-Intuitive and Reasoning Agent Caira as a conductor for improvised avant-garde music. Caira's listening skills are based on a music recognition system that simulates the human auditory periphery to perform an Auditory Scene Analysis (ASA). Its simulation of cognitive processes includes a comprehensive cognitive calculus for reasoning and decision-making using logic-based reasoning. Caira is used as conductor for live music performances with distributed ensembles, where the musicians are connected via the internet. Caira uses a visual score and directs the ensemble members based on tension arc estimations for the individual music performers.

关键词： Decision making

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BIM Software Plug-ins： An Alternative to OptimizeDesign Processes from the Perspective of Performance and Sustainability

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Journal of Civil Engineering and Architecture 2017年第3期11卷 249-264页

作者： Juliano Lima da Silva Andrea Quadrado Mussi Lauro Andre Ribeiro Thaisa Leal da Silva Postgraduate Program in Architecture and Urbanism (PPGARQ) of the Polytechnic School at Faculdade Meridional (IMED) Passo Fundo/RS. CEP: 99070-220 Brazil INESC Coimbra Coimbra CEP 3030-030 Portugal School of Computer Science of the Polytechnic School at Faculdade Meridional (IMED) Passo Fundo/RS. CEP: 99070-220 Brazil

With the increased use of technology in design processes, BIM （building information modeling） tools user＇s community has started to develop solutions through external applications such as plug-ins to automate specific and repetitive tasks, amplify interoperability and add functionality versatility to BIM platforms. This paper seeks to evaluate Autodesk Revit platform plug-ins＇ state of the art, by means of a quantitative study of current repositories and the proposition of a categorization system of the purposes to which plug-ins are developed. Then a comparison between use and necessity of Brazil＇s AEC （architecture, engineering and construction） industry was done through a questionnaire with experienced professionals of the area. An analysis was carried out based on the professional＇s difficulties in meeting the requirements of Brazilian Performance Standard and compliance with Environmental Certification provisions, underpinning the possibility of using plug-ins to optimize the design processes under the perspective of performance and sustainability.

关键词： Building information modeling plug-ins performance standard environmental certifications.

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Report on the AAAI 2010 robot exhibition

Report on the AAAI 2010 robot exhibition

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作者： Anderson, Monica D. Chernova, Sonia Dodds, Zachary Thomaz, Andrea L. Touretzky, David S. Computer Science Department University of Alabama United States Robotics Engineering Program Worcester Polytechnic Institute United States Department of Computer Science Harvey Mudd College United States School of Interactive Computing Georgia Institute of Technology United States Center for the Neural Basis of Cognition Carnegie Mellon University United States

The 19th robotics program at the annual AAAI conference was held in Atlanta, Georgia, in July 2010. In this article we give a summary of three components of the exhibition: the Small-Scale Manipulation Challenge: Robotic Chess;the Learning by Demonstration Challenge;and the Education Track. In each section we detail the challenge task. We also describe the participating teams, highlight the research questions they tackled, and briefly describe the systems they demonstrated. Copyright © 2011, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Exhibitions

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Features Reduction with PCA Technique for Malware Detection Using Machine Learning

Features Reduction with PCA Technique for Malware Detection ...

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International Conference on Computing, Engineering, and Design (ICCED)

作者： Teddy Mantoro Agung Ginanjar Media Anugerah Ayu Umar Aditiawarman Department of Computer Science Faculty of Engineering and Technology Sampoerna University Jakarta Indonesia Postgraduate Program - School of Computer Science Nusa Putra University Sukabumi Indonesia

Malware had been a problem for quite some times since it spreads easily and can cause various problems. Currently, malware is also one of the big threats for internet users. With a huge number of internet users today, techniques that can automatically detect malware before it infects the system is required. This study aims to develop malware detection using machine learning approach with Principal Component Analysis (PCA) as feature reduction. PCA (Principal Component Analysis) is expected to be able to reduce the number of features which then could also reduce the learning time but do not reduce its accuracy significantly. There were four machine learning classifiers used in this study, i.e. K-Nearest Neighbor, Decision Tree, Naïve bayes, and Random Forest. The n-components used in this study were 20 and 34 and the ratio of test and train in the dataset was 35% for test and 65% for training. The results have shown that the best performance come from the detection using random forest with 34 n-component and 100 n-estimator with the average accuracy was 0.991688.

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A Framework for Simulation Analysis of Delay Tolerant Routing Protocols

A Framework for Simulation Analysis of Delay Tolerant Routin...

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IEEE Conference on Vehicular Technology (VTC)

作者： Sathya Narayanan Eric McDonald Geoffrey G. Xie Computer Science and Information Technology Program California State University Seaside CA USA Department of Computer Science Naval Postgraduate School Monterey CA USA

A variety of network deployments in disaster recovery, fire fighting and military scenarios create networks that do not form a connected network all the time. In such scenarios, paths between a source and destination may be created over time when nodes encounter each other due to node mobility. Many routing algorithms that can route messages in such delay-tolerant networking (DTN) settings have been proposed. Each of these proposals presents simulation and/or analytical results to demonstrate improved performance in comparison with other known protocols. This paper proposes a common framework that defines multiple important components through which performance of these protocols can be compared using simulation experiments. This framework includes performance metrics that can be used for comparison, simulation environment, new mobility models and results in organizing the various known protocols based on their ability to predict a path. In a case study using the framework, we demonstrate how the predictive ability of PROPHET may or may not result in significant performance gains, depending on the mobility model.

关键词： Routing Routing protocols Delay Predictive models Analytical models

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Sentiment Analysis With N-Gram Preprocessing for Online-Shopping Reviews in Indonesian Language

Sentiment Analysis With N-Gram Preprocessing for Online-Shop...

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International Conference on Computing, Engineering, and Design (ICCED)

作者： Jelita Asian Oktavianus Tegar N.K Putra Media Anugerah Ayu Teddy Mantoro Postgraduate Program - School of Computer Science Nusa Putra University Sukabumi Indonesia Department of Computer Science Faculty of Engineering and Technology Sampoerna University Jakarta Indonesia

Currently, online Shopping platforms have grown significantly, especially during the COVID-19 pandemic. This condition motivates the need for analyzing how the users/customers’ opinions on using such platform. Sentiment analysis, as a process of detecting, extracting, and classifying users’ opinions and attitudes toward specific topics, is a good tool for the required analysis. This study aims to evaluate the performance of machine learning approach which combined with N-Gram technique in doing sentiment analysis. The dataset used in this study comes from scraping reviews in Bahasa Indonesia regarding the Shopee Apps. In this study, $\mathrm{N}=2$ for the N-Gram was employed in the preprocessing process. Our main goal is to investigate whether the performance of machine learning in doing sentiment analysis can be improved by adding the N-Gram technique in its preprocessing. This work applied the Naive Bayes Classifier and k-Nearest Neighbor with $K=11$ as the machine learning algorithms. The best accuracy in this study was achieved by Naive Bayes Classifier after applying N-Gram Terms $(N=2)$ with Split Validation (8:2), which is $\mathbf{97.26\%}$.

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Enhancing Security Information and Event Management (SIEM) by Incorporating Machine Learning for Cyber Attack Detection

Enhancing Security Information and Event Management (SIEM) b...

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International Conference on Computing, Engineering, and Design (ICCED)

作者： Media Anugerah Ayu Dimas Erlangga Teddy Mantoro Dini Handayani Department of Computer Science Faculty of Engineering and Technology Sampoerna University Jakarta Indonesia Postgraduate Program - School of Computer Science Nusa Putra University - Sukabumi Indonesia

Network security is a crucial component of Information Technology, yet organizations continue to grapple with meeting established security benchmarks. Given the rise in cyber-attacks and the continuous emergence of new attack types, it’s practically infeasible to persistently update attack patterns or signatures within security parameters. Key tools such as Intrusion Detection Systems (IDS) and Security Information and Event Management (SIEM) are instrumental in monitoring network traffic and identifying potential threats. However, these tools face limitations, such as the high volume of alerts produced by IDS and the use of rule-based method, also the inability of SIEM tools to analyze logs comprehensively to identify inappropriate activities. This research has conducted anomaly detection using machine learning process to classify cyber-attacks network flow collected from IDS that installed incident network infrastructure. The analysis of IDS using machine learning, integrated with SIEM. The algorithm used in this research was Random Forest Classifier using CSE-CID-IDS2018 dataset pre-processed with Principal Component Analysis (PCA). Results of the experiments show that Random Forest Classifier Model, when combined with Principal Component Analysis (PCA), yields the most commendable results when applied to a 70/30 training/testing data ratio with accuracy of 0.99953.

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