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24th International seminar on Intelligent Technology and Its Applications, IsITIA 2023

作者： Widarma, Gita Novita, Rice Mustakim Rozanda, Nesdi Evrilyan Universitas Islam Negeri Sultan Syarif Kasim Riau Graduate Program Information Systems Department Pekanbaru Indonesia Postgraduate Programs Universitas Negeri Padang Information Systems Pekanbaru Indonesia Postgraduate Programs Institut Pertanian Bogor Master of Informatics Engineering Pekanbaru Indonesia Postgraduate Programs Universiti Teknologi Malaysia Master of Information Systems Management Pekanbaru Indonesia

ISBN: (纸本)9798350313956

Twitter is currently used as one of the applications that is often used to convey opinions or unrest by the public such as the current issue that is currently rife, namely regarding the increase in fuel oil prices (BBM) in Indonesia. With so much data scattered in a tweet that only consists of a few fragments of words, there must be valuable information or meaning that Twitter users want to convey. Text mining is a very appropriate method to do the analysis because in text mining there is a method that focuses on an opinion which will be concluded into positive and negative opinions. There are several algorithms that are most often used in data mining, namely K-Nearest Neighbor, Decision Tree, and Naive Bayes. The three algorithms used in this study were also compared with the use of feature selection Particle swarm Optimization (PsO) which is well known for improving the performance of algorithms. There are 2165 negative opinion data and 2835 positive sentiment data. The data will be tried based on distinctive sums of data namely 1000 data, 3000 data, and 5000 data. The classic K-Nearest Neighbor algorithm obtained an accuracy result of 75.60% and made it the best when tested with 1000 data. The use of Particle swarm Optimization (PsO) is proven to improve accuracy as when tested using 3000 data with an accuracy value of 78.21% on the K-Nearest Neighbor algorithm. In the mean time, in testing 5000 Data, the classic K-Nearest Neighbor once more gotten the most elevated exactness with a esteem of 74.02%. © 2023 IEEE.

关键词： Decision trees

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Minimizing the Number of stunting Prevalence Using the Euclid Algorithm Clustering Approach 7

Minimizing the Number of Stunting Prevalence Using the Eucli...

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7th IEEE International Conference of Computer science and information Technology, ICOsNIKOM 2023

作者： Zarlis, Muhammad Oktavia, Tanty Buaton, Relita Ernawan, Ferda Andrian, Kevin Bina Nusantara University Information System Management Department BINUS Graduate Program - Master of Information System Management Jakarta11480 Indonesia STMIK Kaputama Informatics Engineering Binjai20714 Indonesia Universiti Malaysia Pahang Al-Sultan Abdullah Faculty of Computing Pekan26600 Malaysia BINUS Graduate Program-Master of Information Systems Management Bina Nusantara University Department of Information Systems Management Jakarta Indonesia

ISBN: (纸本)9798350360752

superior and quality human resources are based on healthy human resources with indicators of adequate nutritional intake according to age development. However, the world still faces the problem of hunger and malnutrition today. According to a UNICEF report, the number of people suffering from malnutrition in the world will reach 767.9 million people in 2021. The World Health Organization (WHO) said that malnutrition is a dangerous threat to the health of the world's population. stunting also has an impact in Indonesia, the prevalence of toddlers experiencing stunting in Indonesia is 24.4% in 2021. The solution created is to classify and cluster the prevalence of stunting to produce a pattern that can be used as best practice to be transmitted to other affected areas. The algorithm used is Euclid. The Euclid algorithm can cluster stunting prevalence data into 4 clusters with the very little category at 79%, the little category at 67%, the many categories at 51%, and the very much category at 21%. The results of the classification and clustering of the best stunting prevalence in cluster one with a very small number, can be used as a source of accurate and updated information that can be used by the government in its efforts to optimize stunting handling in each district/city based on artificial intelligence which can provide handling and optimization patterns. stunting in every district/city. © 2023 IEEE.

关键词： Classification (of information)

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Application of factor analysis method to support the users acceptance model of ERP systems implementation 4

Application of factor analysis method to support the users a...

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4th International Conference on Combinatorics, Graph Theory, and Network Topology, ICCGANT 2020

作者： sardjono, W. sudirwan, J. Priatna, W. Putra, G.R. Information Systems Management Department BINUS Graduate Program Master of Information Systems Management Bina Nusantara University Jakarta11480 Indonesia School of Information Systems Department BINUS Undergraduate Program Bina Nusantara University Jakarta11480 Indonesia Informatics Engineering Engineering Bhayangkara University Jakarta Raya Indonesia Computer Science Study Program Faculty of Mathematics and Natural Sciences Pakuan University Bogor Indonesia

An e-Commerce company has been using an Enterprise Resource Planning (ERP) system for several years, but is still constrained in its implementation, this is reflected in the number of issue/change request tickets submitted by users who have not been able to reach ideal conditions. To overcome this problem, we need an appropriate reference model, the ERP system user acceptance model in companies. The study conducted factors that influence the level of acceptance of sAP ERP users in the XYZ group to then determine a model that is able to explain practically the level of ERP system user acceptance, which in turn can produce concrete proposals to increase ERP system user acceptance. Research is quantitative descriptive using factor analysis and regression analysis methods. The research method uses a questionnaire with a total of 107 valid respondents coming from 8 company names under the XYZ group in 6 southeast Asian countries. The conclusion of the study is that there are 24 indicators and 4 factors that influence user acceptance of the sAP ERP system. These four factors are the low contribution of the ERP system, the quality of ERP systems and data, the development of ERP systems, and also the low quality of ERP system supporters. It is hoped that by optimizing the value of the 4 factors, the XYZ group can increase user acceptance of the sAP ERP system so that the system can provide benefits expected by the company to be competitive advantage. © 2021 Published under licence by IOP Publishing Ltd.

关键词： Factor analysis

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On a New Rough Clustering Based on Optimization 13

On a New Rough Clustering Based on Optimization

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Joint 13th International Conference on soft Computing and Intelligent systems and 25th International symposium on Advanced Intelligent systems, sCIs and IsIs 2024

作者： Furuta, sakuya Endo, Yasunori Master's Program in Risk and Resilience Engineering Degree Programs in Systems and Information Engineering Graduate School of Science Annd Technology University of Tsukuba 1-1-1 Tennodai Tsukuba Ibaraki305-8573 Japan Institute of Systems and Information Engineering University of Tsukuba 1-1-1 Tennodai Tsukuba Ibaraki305-8573 Japan

ISBN: (纸本)9798350373332

The purpose of this study is to develop and investigate a new rough clustering based on optimization. We propose a restructured algorithm using only one variable, instead of the algorithm described using two variables in existing methods. We also perform numerical examples using artificial data and the Iris and Wine datasets. This research has the potential to be used as a method for determining false negatives and false positives in cancer diagnosis. © 2024 IEEE.

关键词： Clustering algorithms

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GEOMETRIC REAsONING WITH QUANTITATIVE PHYsICAL MODELs

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systems AND COMPUTERs IN JAPAN 1994年第1期25卷 47-56页

作者： OKAMOTA, Y Associate Member Faculty of Engineering The University of Tokyo Tokyo Japan 113 Yoshinori Okamoto graduated hi 1992 from the Dept. of Instrumental and Mathematical Engineering School of Engineering Tokyo University where he is presently in the Master's program. He is engaged in research on reasoning using visual images and is interested in man's common-sense reasoning and the brain's higher-order information processing.

This paper proposes a method of reasoning and of planning space and motion based on quantitative physical models. First, the particle-collection representation (PCR) is proposed as a method of representing objects, and the robust simulation method of rigid-body dynamics using PCR is discussed. Next, as a system which reasons and plans dynamic actions of rigid bodies based on this rigid-body simulation, a planner (PCP) using physical simulation is proposed. For the planning method of PsP, the action improvement method is proposed which carries out a plan by gradually improving an original plan. To solve the problem of adjusting parameters for action in the action improvement method, the general parameter adjuster (GPA) is proposed. Moreover, the motion of bodies and carriage plannings by PsP are simulated. Furthermore, the robustness and extensibility of the PsP are investigated.

关键词： GEOMETRIC REAsONING RIGID BODY DYNAMICs sIMULATION PATH PLANNING

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Real-time recognition of traffic signs using opponent color filters

Real-time recognition of traffic signs using opponent color ...

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14th World Congress on Intelligent Transport systems, ITs 2007

作者： Baba, Kyoko Hirai, Yuzo Master's Program in Graduate School of Systems and Information Engineering University of Tsukuba 1-1-1 Ten-nodai Tsukuba Ibaraki 305-8573 Japan Department of Computer Science Graduate School of Systems and Information Engineering University of Tsukuba 1-1-1 Ten-nodai Tsukuba Ibaraki 305-8573 Japan

ISBN: (纸本)9781617387777

In order to assist driver's vision, a real-time recognition system for traffic signs is proposed. After detecting sign candidates, biologically inspired opponent-color filters are used to extract symbol parts of signs. After normalizing the size of symbol, structural features are calculated to identify the sign. 5572 segmented images are used to design the algorithm. In a real-time system, the same sign in a sequence of frames is tracked, and a majority vote is used to integrate the recognition results. For test data, 93.8% recall rate and 99.3% precision rate could be attained. In-vehicle experiment also showed high recall and precision rates.

关键词： Traffic signs

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Action Robust Reinforcement Learning with Highly Expressive Policy

Action Robust Reinforcement Learning with Highly Expressive ...

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2024 IEEE International Conference on systems, Man, and Cybernetics, sMC 2024

作者： Kim, seongIn shibuya, Takeshi University of Tsukuba Doctor's Program in Intelligent and Mechanical Interaction Systems Ibaraki Japan University of Tsukuba Faculty of Engineering Information and Systems Ibaraki Japan

ISBN: (纸本)9781665410205

In traditional reinforcement learning, there can be a degradation in the control performance of the policy when the environmental parameters differ between the training and application phase. The policy that minimizes this degradation is referred to as a robust policy. A framework called Noisy action Robust Markov Decision Process (NR-MDP) was proposed for training robust policies, and the Action Robust Deep Determin-istic Policy Gradient (AR-DDPG) algorithm was introduced as a method for solving NR-MDP. The optimal policy in NR-MDP includes policies following various probability distributions, whereas AR-DDPG is restricted to deterministic policies. We propose a new robust reinforcement learning method called Action Robust Q-Learning (AR-QL) that enables the training of optimal policies in NR-MDP by leveraging various sampling techniques to extend the representational capacity of policies, targeting an improvement in policy robustness. To validate this, we confirmed that AR-QL can acquire the optimal policy for a simple NR-MDP problem, for which AR-DDPG fails to obtain the optimal policy. Furthermore, we confirmed that the robust performance of policy trained by AR-QL in the OpenAI's InvertedPendulum environment surpasses that of policy trained by AR-DDPG. © 2024 IEEE.

关键词： Deep reinforcement learning

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How different spatial resolutions of digital models affects the geospatial distribution evaluation of the Index of Connectivity

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Journal of south American Earth sciences 2025年 163卷

作者： Eberval Marchioro Geraldo André Rosseto Barreto Ialy Gomes Ferreira Adelsom soares Filho Diogo Costa Buarque Department of Geography (DGEO) Graduate Program in Geography (PPGG) Federal University of Espírito Santo/Brazil (UFES) Coordinator of the Laboratory for Monitoring and Modeling Environmental Systems (Lamosa) Brazil Graduate Program in Management and Regulation of Water Resources (ProfÁgua/UFES) Brazil Graduate Program in Geography at UFES Brazil Graduate Program in Civil Engineering at UFV Brazil Department of Geography Graduate Program in Geography Federal University of Grande Dourados (UFGD) Brazil Department of Environmental Engineering Graduate Program in Environmental Engineering (PPGEA) Professional Master's in Water Resources Management and Regulation (ProfÁgua/UFES) Federal University of Espírito Santo/Brazil (UFES) Brazil

Digital Terrain Models (DTM) and Digital Elevation Models (DEM) are important for obtaining the Index of Connectivity, as they provide valuable information to assess the possibility of sediments reaching the riverbed in watershed basins. This is crucial for proactive management, as it allows the adoption of preventive and mitigating measures. Therefore, this study aims to analyze the effect of the spatial resolution of DEMs with resolutions of 5 m and 2 m, and between the DTM and DEM at 2 m, in determining the Index of Connectivity (IC). Additionally, it was investigated whether it is possible to use only one spatial resolution of digital models to assess the geospatial distribution of IC for all hydrogeomorphological landscapes of the Duas Bocas River watershed, located in the Metropolitan Region of Greater Vitória, in the state of Espírito santo, southeast Brazil. For this study, the Borselli Index of Connectivity model (et al., 2008) was used, originally developed for rural areas of Italy and widely applied in different regions worldwide. The data used, including Digital Elevation and Terrain Models and land use and cover information, were obtained from the Integrated Geospatial Base system of the state of Espírito santo (Brazil), which is publicly *** results indicate that a better geospatial representation of IC occurs when changing the spatial resolution of DEMs from a 5 m pixel to a 2 m pixel and when using the 2 m DTM instead of the 2 m DEM. When analyzing the relationship between IC and the hydrogeomorphological landscapes in the basin, it was observed that, in the absence of models with an even finer pixel resolution, the 2 m DTM is the most suitable option, as it presented IC values consistent within and between landscapes in the Escalated Terraces of southern Espírito santo, Coastal Hills and Massifs, and Coastal Plateaus sectors. The exception was the fluvio-lagoon plain, which requires an even finer pixel resolution, necessitating further st

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Mining frequent itemsets from noisy data 22

Mining frequent itemsets from noisy data

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22nd International Conference on Data engineering Workshops, ICDEW 2006

作者： Narita, Kasuyo Kitagawa, Hiroyuki Master's Programs in Science and Engineering University of Tsukuba Tennodai Tsukuba Japan Graduate School of Systems and Information Engineering Center for Computational Sciences University of Tsukuba Tennodai Tsukuba Japan

ISBN: (纸本)0769525717

As we face huge amounts of varied information, data mining, which helps us discover hidden features or rules from voluminous data systematically, has become more important [3, 4, 6, 10]. However, real world data is often dirty, including noise such as missing or irrelevant values. The information mined from such noisy data may be incorrect. We model noisy data with probabilities, assuming that noise is mixed with data statistically. We also propose a way to find frequent itemsets [2] by estimating supports on noiseless data from noisy data. An algorithm using FP-Tree [6, 10] is also presented to mine frequent itemsets efficiently. © 2006 IEEE.

关键词： Data mining

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Explainable Reinforcement Learning via Causal Model Considering Agent's Intention

Explainable Reinforcement Learning via Causal Model Consider...

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2024 IEEE International Conference on systems, Man, and Cybernetics, sMC 2024

ISBN: (纸本)9781665410205

Explaining agent's decision can offer valuable insights for designers and end-users. One proposed method for explaining an agent's decision-making involves representing a causal relation between state components and action as a causal model and providing explanations for the decisions made using causal model. However, traditional causal model often faces structural limitation, restricting the range of representable control problems. Additionally, providing accurate explanation becomes challenging in control problems with various types of rewards because agent's intention of an action is unknown. In this study, we introduce a causal model capable of representing a broader range of control problems and a method to provide accurate explanations in control problems with various types of reward structures. Through redefining the relationships between nodes in the causal model, we have enabled a broader representation of control problems. Also, by incorporating agent's intention into the explanation, we have achieved to provide a more precise explanation. To validate the effectiveness of our proposed method, we conducted experiments using OpenAI's LunarLander environment. Using a proposed causal model, we defined the causal model of LunarLander, which could not be represented by conventional causal models. Furthermore, by incorporating the intentions of an agent into the explanation, novel interpretations previously inaccessible have become feasible. © 2024 IEEE.

关键词： Reinforcement learning

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