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检索条件"机构=Program of Informatics Engineering"

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Analyses of Photovoltaic Systems Performance with Fuzzy Inference Systems and Bayesian Networks

Analyses of Photovoltaic Systems Performance with Fuzzy Infe...

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Power Electronics and Control (SEPOC), Seminar on

作者： Nicole Polityto Cremasco Myriam Regattieri De Biase da Silva Delgado Jair Urbanetz Junior Luiz Ledo Mota Melo Jorge Assade Leludak Keiko Verônica Ono Fonseca Program of Post-Graduation of Electrical Engineering and Industrial Informatics(CPGEI) of Federal University of Technology of Paraná(UTFPR) Program of Post-Graduation of Electrical Engineering and Industrial Informatics(CPGEI) of the Federal University of Technology of Paraná(UTFPR) Program of Post-Graduation in Energy Systems of Federal University of Technology of Paraná(UTFPR Department of Electrical Engineering (DAELT)) Federal University of Technology of Paraná(UTFPR) Curitiba Brazil

Renewable energy sources have been increasingly targeted as an alternative for reducing fossil fuel emissions. This scenario requires a better understanding of energy systems' performance and limitations, notably using photovoltaic setups under diverse meteorological conditions and determining device measurement uncertainties. Accordingly, this study conducts a performance evaluation of a photovoltaic system of copper, indium, and gallium (di)selenide technology. It compares the results provided by a reference normalized equation with two interpretable artificial intelligence techniques, Fuzzy Inference System and Bayesian Networks, when temperature is also considered. In the experiments, the study found significant percentage differences between the normalization procedure and the fuzzy system on days with lower irradiation values. On the other hand, the results provided by Bayesian networks presented lower difference percentages in relation to the normalization equation values.

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Sensing Architecture in Smart City: A Review Paper 10

Sensing Architecture in Smart City: A Review Paper

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Eriyadi, Mindit Supangkat, Suhono Harso Hidayat, Fadhil Doctoral Program of Instiut Teknologi Bandung Bandung Indonesia School of Applied Science Universitas Telkom Bandung Indonesia School of Electrical Engineering and Informatics Instiut Teknologi Bandung Bandung Indonesia Institut Teknologi Bandung Smart City and Community Innovation Center Bandung Indonesia

ISBN: (纸本)9798350339543

Relevant smart city research involves many highly futuristic applications, such as smart transportation, in which smart roads warn drivers of impending traffic jams, smart parking, which communicates the location of available parking spaces to drivers, and smart environments, which allow homes and workplaces to fully automatically adjust their temperature to save energy. Complexity in the city sensing section is caused by the complexity of urban systems. The enormous number of sensor types and technologies utilized for municipal data collecting provide a problem. Some of the issues in city sensing are node deployment, sensing management, sensor network types, and their derivative variables, necessitating the development of a city sensing architecture capable of contributing to advances in efficiency, accuracy, and real-timeness. © 2023 IEEE.

关键词： Internet of things

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Finding the Important Elements Linked to Integration Problems in the Use of Enterprise Resource Planning Systems 9

Finding the Important Elements Linked to Integration Problem...

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9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Sardjono, Wahyu Kristin, Desi Maya Pranata, Dewi Sagita Bina Nusantara University BINUS Graduate Program Master of Information Systems Management Information Systems Management Department Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia King's College London Cyber Security Studi Program Faculty of Natural Mathematical & Engineering Sciences Department of Informatics WC2R 2LS United Kingdom

ISBN: (纸本)9798350390025

Organizations face an urgent need to become faster, more efficient, and more successful in their commercial operations, given the current state of technological evolution and the arrival of Industry 4.0. Many businesses have adopted the idea of enterprise resource planning (ERP) systems to achieve this goal. The ERP system is expected to increase worker productivity, improve business performance to maximize profitability and profits, and provide accurate and up-to-date data and information to management and stakeholders. This research aims to find new variables related to integration problems in the use of ERP systems. Using the SPSS statistical program, the data collected will be examined using quantitative exploratory factor analysis (EFA). Data processing then includes collecting demographic information from respondents, reliability testing, conducting the Kaiser-Meyer-Olkin (KMO) test, testing using an anti-image matrix, and deriving new factors using a rotation component matrix. This research seeks to identify new elements underlying ongoing integration problems in ERP system implementation. The research results show several reasons for underlying integration problems and offer in-depth information to management that can be used to analyze and evaluate the ERP system in their company appropriately in the future so that the company's performance becomes superior to the competition. © 2024 IEEE.

关键词： Profitability

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Sentiment Analysis of Cooking Oil Price Policy in Indonesia using Word2Vec Feature Extraction and Bidirectional Long Short Term Memory

Sentiment Analysis of Cooking Oil Price Policy in Indonesia ...

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International Conference of Advanced informatics: Concept, Theory and Application (ICAICTA)

作者： Dedy Sugiarto Ema Utami Ainul Yaqin Suwanto Raharjo Faculty of Industrial Technology Universitas Trisakti DKI Jakarta Indonesia Master Program of Informatics Engineering Universitas Amikom Yogyakarta Yogyakarta Indonesia Informatics IST AKPRIND Yogyakarta Yogyakarta Indonesia

The policy of the Ministry of Trade of the Republic of Indonesia which sets the highest retail price for cooking oil and then cancels it in early 2022 due to people's complaints about the difficulty of getting cooking oil in the market has received many responses from the public via social media. This study conducts sentiment analysis using Word2Vec and Bidirectional Long Short Term Memory (BLSTM) with the aim of studying the public's response to the removal of the highest retail price policy and effect of both skip-gram (SG) and continuous bag-of-words (CBOW), window size and dimension to the word similarity and accuracy. Data taken via Twitter in March 2022. The data then goes through preprocessing stages, manual labeling by experts, and word embedding using Word2Vec to be further classified using BLSTM. Data labeling produces imbalanced data where the largest is neutral sentiment (53%) followed by negative (27%) and positive (20%). ome of the words such as ‘mafia’, ‘mahal’, ‘langka’, ‘tangkap’, ‘bongkar’, and ‘mendag’ could become the government's attention. The similarity value of the SG model tends to have a higher value than CBOW at a lower word vector dimension. The model that gives the best performance results is the Word2Vec model with 50 dimensions, SG architecture and window size 5. This is shown in the accuracy of 0.71 which is the highest value and only shows slight signs of overfitting. Symptoms of over fitting will be more visible with increasing dimensions.

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Deep STI: Deep Stochastic Time-series Imputation on Electronic Health Records 46

Deep STI: Deep Stochastic Time-series Imputation on Electron...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Cheng, Ming-Che Hsieh, Yi-Hsien Hsu, Te-Cheng Su, Tung-Hung Lin, Che Institute of Communications Engineering National Tsing Hua University Hsinchu30013 Taiwan Taipei10617 Taiwan Division of Gastroenterolgy and Hepatology Dept. of Internal Medicine National Taiwan University Hospital Taipei10048 Taiwan Department of Electrical Engineering Ntu Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine Ntu Taipei Taiwan Smart Medicine and Health Informatics Program Ntu Taipei Taiwan

ISBN: (纸本)9798350371499

Electronic Health Records (EHRs) are a cornerstone of modern healthcare analytics, offering rich datasets for various disease analyses through advanced deep learning algorithms. However, the pervasive issue of missing values in EHRs significantly hampers the development and performance of these models. Addressing this challenge is crucial for enhancing clinical decision-making and patient care. Existing methods for handling missing data, ranging from simple imputation to more sophisticated approaches, often fall short of capturing the temporal dynamics inherent in EHRs. To bridge this gap, we introduce the Deep Stochastic Time-series Imputation (Deep STI) algorithm, an innovative end-to-end deep learning model that seamlessly integrates a sequence-to-sequence generative network with a prediction network. Deep STI is designed to leverage the observed time-series data in EHRs, learning to infer missing values from the temporal context with high accuracy. We evaluated Deep STI on the liver cancer data from the National Taiwan University Hospital (NTUH), Taiwan. Our results showed that Deep STI achieved better 5-year hepatocellular carcinoma predictions (19.21% in the area under the precision-recall curve) than extreme gradient boosting (18.15%) and Transformer (18.09%). The ablation study also illustrates the efficacy of our generative architecture design compared to regular imputations. This approach not only promises to improve the reliability of disease analysis in the presence of incomplete data but also sets a new standard for utilizing EHRs in predictive healthcare. Our work aims to advance the field of healthcare analytics and open new avenues for research in deep learning applications to EHRs. © 2024 IEEE.

关键词： Electronic health record

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Decision Support System in Determining the Achievement of Widyaiswara Value Using WASPAS and TOPSIS Algorithm 1

Decision Support System in Determining the Achievement of Wi...

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1st International Conference on Technology Innovation and Its Applications, ICTIIA 2022

作者： Pratama, Yudhistira Adhitya Iswara, Bagus Ajie Nababan, Adli Abdillah Information System Studies Program Pelita Harapan University Medan Campus Faculty Of Computer Science Medan Indonesia Master's Study Program Informatics Engineering Universitas Sumatera Utara Faculty Of Computer Science And Information Technology Medan Indonesia Digital Business Study Program Stmik Pelita Nusantara Medan Indonesia

ISBN: (数字)9781665488266

ISBN: (纸本)9781665488266

Widyaiswara is required to show the best performance to fulfill his duties and obligations. Therefore, it is very important to measure the performance of the Widyaiswara, so that it can be used as evaluation material by the BPSDM PROVSU. However, the assessment process that occurs is still not precise and time-consuming. Therefore, to minimize this, a decision support system is needed that can minimize time errors and data processing in decision-making so that the assessment runs well and accurately. This system was built by applying the WASPAS method and the TOPSIS method. Based on the results of tests carried out five times, the results of the WASPAS method processing time resulted in an average processing time of 0.00029791 ms and an average memory usage value of 2.836.44 kb, while the results of the TOPSIS method processing time test resulted in an average processing time - an average of 0.000296428 ms and an average memory usage value of 2.862.34 kb. © 2022 IEEE.

关键词： Decision support systems

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Improvement of Confidential Messages Secure Routing over Paths with Intersection in Cyber Resilient Networks 28

Improvement of Confidential Messages Secure Routing over Pat...

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28th International Conference on Information, Communication and Automation Technologies, ICAT 2022

作者： Lemeshko, Oleksandr Yeremenko, Oleksandra Mersni, Amal Gazda, Juraj Kharkiv National University of Radio Electronics V.V. Popovskyy Department of Infocommunication Engineering Kharkiv Ukraine International University of Sarajevo Department of Engineering Computer Sciences and Engineering Program Sarajevo Bosnia and Herzegovina Technical University of Kosice Department of Computers and Informatics Kosice Slovakia

ISBN: (纸本)9781665466929

The paper presents an improved method of confidential messages secure routing over intersecting paths in cyber resilient networks. It allows making fuller use of the available network resources. Within the method, the level of network security of routes, networks, and confidential messages, in general, was assessed due to the compromise probability of the network links. In turn, the message compromise probability is calculated by the formula of total probability, considering the level of network security of bridges in the network structure without prior calculation of disjoint paths set. According to the numerical study results of the method application, the most significant gain in terms of the message compromise probability has been obtained at a relatively low level of cyber threats in the network. The secure routing method was implemented and presented by software prototypes of secure routing protocols that can form the basis of advanced routing protocols supporting network security metrics. © 2022 IEEE.

关键词： Probability

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Application for Airport Tenant Selection Using Weighted Product-based Decision Support System

Application for Airport Tenant Selection Using Weighted Prod...

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2023 International Conference on Electrical and Information Technology, IEIT 2023

作者： Sumari, Arwin Datumaya Wahyudi Saputra, Muhamad Andika Ayuningtyas, Astika Sudarmadi, Catur Adisutjipto Institute of Aerospace Technology Aerospace Intelligent System and Data Engineering Research Group Yogyakarta Indonesia Politeknik Negeri Malang Cognitive Artificial Intelligence Research Group Malang Indonesia Adisutjipto Institute of Aerospace Technology Informatics Study Program Faculty of Industrial Technology Yogyakarta Indonesia Adisutjipto Institute of Aerospace Technology Aerospace Health Informatics Research Group Faculty of Industrial Technology Yogyakarta Indonesia

ISBN: (纸本)9798350327298

This research aims to develop an application for selecting the best new tenant at PT Angkasa Pura II Sultan Thaha Jambi Airport Branch by utilizing Weighted Product (WP) method as the decision support system. Our approach is descriptive research with data analysis technique using four prospective tenants coded A-1 to A-4. The data was collected through observation, interviews, documentation, and literature study. The steps for selecting the best new tenant using the WP method include determining the criteria, sorting the suitability of alternatives and criteria, determining the weight of each criterion and its normalization, calculating the score, S value as an alternative preference, and calculating the ranking, V value as the final alternative preference. Based on the final calculation results, the new tenant coded A-4 is the best alternative based on the data collected from the Financial Management (FHFM) Unit and the Commercial Unit, with a value of V-4 is 0.294. © 2023 IEEE.

关键词： Decision support systems

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Classification Based on Texture Analysis for Synthetic Hazy Image from Mount Kelud Haze Image Density 8

Classification Based on Texture Analysis for Synthetic Hazy ...

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8th International Conference on Electrical, Electronics and Information engineering, ICEEIE 2023

作者： Nugroho, Fresy Basid, Puspa Miladin Safitri A. Arif, Yunifa Miftachul Diah, Norizan Mat Bahtiar, Firma Sahrul Nurhayati, Hani Kurniawan, Fachrul Priandani, Nurizal Dwi Dermawan, Dodik Arwin Universitas Islam Negeri Maulana Malik Ibrahim Informatics Engineering Faculty of Science and Technology Malang Indonesia School of Computing Sciences College of Computing Informatics and Media Universiti Teknologi Mara Shah Alam Malaysia Library and Information Science Universitas Islam Negeri Maulana Malik Ibrahim Faculty of Science and Technology Malang Indonesia Manajemen Informatika Program Vokasi Universitas Negeri Surabaya Surabaya Indonesia

ISBN: (纸本)9798350340501

This paper classifies the simulations of homogeneous synthetic images, heterogeneous synthetic hazy images, and original hazy images taken from CCTV (Close Circuit Television) of Mt. Kelud crater using the GLCM (Gray Level Co-Occurrence Matrix) method. The average feature values obtained using the GLCM (Gray Level Co-Occurrence Matrix) method are used to compare the similarity of gray feature values of the three and then classify thin, medium, and thick images. The results for classifying thin haze, medium haze, and thick haze on the homogeneous synthetic hazy image test data obtained an accuracy value of 50%, a precision value of 46%, and a sensitivity value of 65%. As for the classification of thin, medium, and thick fog on heterogeneous synthetic hazy images, test data obtained an accuracy value of 42%, a precision value of 32%, and a sensitivity value of 48%. © 2023 IEEE.

关键词： Textures

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Optimizing Indoor Vertical Farming with DDPG Reinforcement Learning: Enhancing Control of Nutrient Levels, Climate Conditions, and Plant Health

Optimizing Indoor Vertical Farming with DDPG Reinforcement L...

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International Conference on Technology, informatics, Management, engineering, and Environment (TIME-E)

作者： Albert Good Fried Panggabean Rahmat Romadhon Ramot Lubis Endra Joelianto Doctoral Program in Engineering Physics Institut Teknologi Bandung Bandung Indonesia Faculty of Informatics and Electrical Engineering Institut Teknologi Del Toba Indonesia Engineering Physics Study Program Institut Teknologi Bandung Bandung Indonesia Instrumentation Control and Automation Research Group Institut Teknologi Bandung Bandung Indonesia

ISBN: (数字)9798350364750

ISBN: (纸本)9798350364767

In indoor vertical farming systems, managing dynamic and complex environments poses significant challenges. Traditional control strategies often fall short in adapting to rapid changes in critical parameters such as nutrient levels, climate conditions, and plant health indicators. To address these challenges, this paper presents a novel approach for optimizing indoor vertical farming using Deep Deterministic Policy Gradient (DDPG), a reinforcement learning algorithm well-suited for environments with continuous action spaces. Our study focused on the control and monitoring of key parameters, including nutrient levels (pH and EC), climate conditions (temperature, humidity, light, and CO2 levels), and plant health indicators (biomass accumulation and leaf size structure). By defining a comprehensive state and action space, we simulated an indoor farming environment and trained the DDPG model to maximize crop yield and health.

关键词： Smart agriculture Precision agriculture Reinforcement learning Humidity Aerospace electronics Nutrients Temperature control informatics Monitoring Farming

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