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2023 International Conference on Informatics, Multimedia, Cyber and Information systems, ICIMCIS 2023

作者： Andriyani, Yanti Firyadi, Ricki Mahdiyah, Evfi Fitriansyah, Aidil Aminuddin, Al Meitarice, Sonya Niqotaini, Zatin University of Riau Information System Study Program Computer Science Department Pekanbaru Indonesia Universitas Pembangunan Nasional Veteran Jakarta Information System Study Program Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350307306

Efficient communication plays a pivotal role in the success of university community service programs. This study highlights the innovative approach taken by the University of Riau to enhance communication within their community service initiative through the implementation of Kukerti, a cutting-edge chatbot system. Developed specifically for Bahasa Indonesia-speaking users, Kukerti utilizes advanced fuzzy string-matching algorithms to process user inputs and generate relevant responses. In this study, we delve into the deployment and impact of the Kukerti chatbot within the University of Riau's community service program. We explore its functionality in addressing the common challenge of delayed responses from web administrators, which had previously hindered efficient information retrieval for students and participants. Our research aims to explore how does the implementation of the Kukerti chatbot system with advanced fuzzy string-matching algorithms impact the efficiency of communication and information retrieval within the University of Riau's community service program. The result shows the implementation of fuzzy string-matching algorithms improves the communication process and the evaluation of the Kukerti chatbot showed that the implementation of fuzzy string-matching has an accuracy rate of 93.33% in providing the answers. By exploring the fuzzy string-matching implementation in the Kukerti chatbot, it is expected to provide further understanding regarding the implementation and encourage other chatbot developers to use it. © 2023 IEEE.

关键词： String searching algorithms

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Convolutional Neural Network Analysis on Handwriting Patterns and Its Relationship to Personality: A systematical Review

Convolutional Neural Network Analysis on Handwriting Pattern...

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2023 International Conference on Informatics, Multimedia, Cyber and Information systems, ICIMCIS 2023

作者： Mair, Zaid Romegar Cholil, Widya Yulianti, Evi Marcelina, Dona Theresiawati Isnainiyah, Ika Nurlaili Computer and Natural Science Faculty Indo Global Mandiri University Informatics Study Program Indonesia Universitas Pembangunan Nasional Informatics Study Program Computer Science Faculty Jakarta Indonesia Indo Global Mandiri University Information System Study Program Computer and Natural Science Faculty Indonesia

ISBN: (纸本)9798350307306

It can be argued that the Convolutional Neural Network (CNN) used in this research is an efficient algorithm for classifying images based on the end prediction of the path taken. In every plot that is made, there is a similar process for achieving a prediction of the final result. The implementation for each of these procedures follows the steps that are summarized into a flow of analysis stages that can help to develop the application of an algorithm. The initial stage is to take the handwriting from the user which is then pre-processed the image, to eliminate existing noise, and sharpen the contrast, so that the image can be seen clearly. Images will be processed and analyzed using the Convolutional Neural Network model, training will be carried out, with an average training of a dataset of 100 epochs or about 7 to 10 minutes, and labeling on the trained dataset. The accuracy of the training reached 98.89%, as a proportion of the different characteristics of the handwriting sample. © 2023 IEEE.

关键词： Convolutional neural networks

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IoT: Monitoring Temperature, Water pH, and Automatic Water Drainage systems for Gurami Cultivation

IoT: Monitoring Temperature, Water pH, and Automatic Water D...

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2023 International Conference on Networking, Electrical Engineering, computer Science, and Technology, IConNECT 2023

作者： Sulistiani, Heni Saputra, Wijaya Darwis, Dedi Isnain, Auliya Rahman Rimanda Putra, Ardy Khairunisa, Yosi Universitas Teknokrat Indonesia Study Program of Accounting Information System Lampung Indonesia Universitas Teknokrat Indonesia Study Program of Computer Engineering Lampung Indonesia Universitas Teknokrat Indonesia Study Program of Informatics Lampung Indonesia Regional Research and Development Agency Lampung Indonesia

ISBN: (纸本)9798350331172

Gurami is a native fish from Indonesian waters that is much-loved by Indonesian people for daily food consumption. The gurami inhabits still waters such as swamps, lakes, and ponds. Besides that, carp cannot live in a water content that is too alkaline or acidic, the oxygen content in the water must be stable. Water quality is a very important factor and can affect the survival of cultivated fish. Several parameters must be considered in improving the development of carp, one of which is the temperature and pH of the water and automatic drainage of water. In this paper, we conduct IoT-based research that focuses on controlling water temperature and pH and automatically draining pool water. Water temperature and pH control such as a pH sensor temperature sensor and a water turbidity sensor will later be connected to the ESP 32 microcontroller which already has a wi-fi module. A system that is linked to electrical power and the internet then the sensor will read the water quality including the pH temperature value and water turbidity from inside the fish pond, after getting the data, the data will go to ESP 32. This research can help gurami cultivating farmers to make it easy to control the pH temperature and perform automatic pool water draining. The results of sensor testing with actual data are pretty accurate with the average error value of the ds18b20 sensor being 0.05% while the value of the pH sensor itself is 0.07%. © 2023 IEEE.

关键词： Temperature sensors

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Enhancing Information system Design with Decision Model and Notation (DMN): A Comprehensive Approach to Decision Modeling

Enhancing Information System Design with Decision Model and ...

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International Conference on Informatics, Multimedia, Cyber and Information system (ICIMCIS)

作者： Yanti Andriyani Evfi Mahdiyah Al Aminuddin Zatin Niqotaini Computer Science Department Information System Study Program University of Riau Pekanbaru Indonesia Computer Science Department Information System Study Program Universitas Pembangunan Nasional Veteran Jakarta Jakarta Indonesia

ISBN: (数字)9798331533243

ISBN: (纸本)9798331533250

Business rules are essential for organizational decision-making, shaping the functional requirements of information systems. These rules establish constraints within business processes, influencing decisions like product pricing strategies or supplier compliance evaluations. Integrating business rules into information systems ensures accurate, consistent decisions aligned with organizational objectives. The Decision Model and Notation (DMN) provides a standardized way to model decision logic, capture dependencies, and organize related data elements. DMN enhances the Business Process Model and Notation (BPMN) by incorporating decision-making logic into business process models, making them accessible to both business and technical stakeholders. While the Unified Modeling Language (UML) and BPMN are widely used in system modeling, DMN's practical application for decision modeling remains underexplored. This paper presents a practical implementation of DMN, focusing on its integration with BPMN for a comprehensive approach to information system design. Using a case study, we demonstrate how DMN effectively represents decision logic, showcasing benefits such as improved clarity, flexibility, and reusability. In evaluating different scenarios, Scenario S1 achieved the highest efficiency improvement of 19%, while Scenario S3 showed the least with only a 5% gain. These findings indicate that the adjustments in Scenario S1 can significantly enhance the performance of the Publication Incentive Information system, leading to recommendations for decision-makers to optimize evaluation criteria and processes.

关键词： Visualization Process modeling Unified modeling language Decision making systems modeling Data models Logic Stakeholders Information systems Business

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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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Online Handwritten Recognition for Alphabet Writing Practice 4

Online Handwritten Recognition for Alphabet Writing Practice

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4th International Conference on Cybernetics and Intelligent system, ICORIS 2022

作者： Santiari, Ni Putu Linda Rahayuda, I Gede Surya Institute of Technology and Business STIKOM Bali Information System Study Program Faculty of Informatics and Computer Denpasar Indonesia Informatics Study Program Faculty of Math and Natural Sciences Udayana University Mangupura Indonesia

ISBN: (纸本)9781665453950

Learning to write in children requires the child's habit of using a pencil to write on paper. The manual method has drawbacks such as the use of a lot of paper when children have to study harder to keep repeating the exercises and also cannot be done online remotely. In addition to this, writing exercises on the touchscreen can also train children to be more skilled at using the touchscreen. To overcome these problems, the author will discuss the use of handwriting recognition technology to practice writing capital letters (A-H), where the technology will be built on a web-based basis. The recognition algorithm used is K-NN. Testing the use of the website was carried out at Tunas Mekar Sari Kindergarten, Denpasar. The identification number and result will be stored on the web server. Based on the experiment, it was found that from 80 image data, the K-NN algorithm succeeded in providing accurate recognition results on 60 image data, and obtained an accuracy of 78.75%. © 2022 IEEE.

关键词： Websites

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Improving University Community Service Communication with Kukerti’s Fuzzy String Matching Chatbot

Improving University Community Service Communication with Ku...

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International Conference on Informatics, Multimedia, Cyber and Information system (ICIMCIS)

作者： Yanti Andriyani Ricki Firyadi Evfi Mahdiyah Aidil Fitriansyah Al Aminuddin Sonya Meitarice Zatin Niqotaini Computer Science Department Information System Study Program University of Riau Pekanbaru Indonesia Computer Science Department Information System Study Program Universitas Pembangunan Nasional Veteran Jakarta Jakarta Indonesia

Efficient communication plays a pivotal role in the success of university community service programs. This study highlights the innovative approach taken by the University of Riau to enhance communication within their community service initiative through the implementation of Kukerti, a cutting-edge chatbot system. Developed specifically for Bahasa Indonesia-speaking users, Kukerti utilizes advanced fuzzy string-matching algorithms to process user inputs and generate relevant responses. In this study, we delve into the deployment and impact of the Kukerti chatbot within the University of Riau’s community service program. We explore its functionality in addressing the common challenge of delayed responses from web administrators, which had previously hindered efficient information retrieval for students and participants. Our research aims to explore how does the implementation of the Kukerti chatbot system with advanced fuzzy string-matching algorithms impact the efficiency of communication and information retrieval within the University of Riau’s community service program. The result shows the implementation of fuzzy string-matching algorithms improves the communication process and the evaluation of the Kukerti chatbot showed that the implementation of fuzzy string-matching has an accuracy rate of 93.33% in providing the answers. By exploring the fuzzy string-matching implementation in the Kukerti chatbot, it is expected to provide further understanding regarding the implementation and encourage other chatbot developers to use it.

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Modeling the Percentage of NEET in Indonesia with Spatial Cauchy Regression through the Bayesian Analysis Approach

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IAENG International Journal of Applied Mathematics 2024年第7期54卷 1288-1295页

作者： Rantini, Dwi Fakhruzzaman, Muhammad Noor Ningrum, Ratih Ardiati Othman, Fazidah Choir, Achmad Syahrul Ramadan, Arip Alya, Najma Attaqiya Putri, Elfira Rahma Pratama, Muhammad Alfian Engineering Department Data Science Technology Study Program Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Indonesia Engineering Department Data Science Technology Study Program Faculty of Advanced Technology andMultidiscipline Universitas Airlangga Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia Statistical Computing StudyProgram Politeknik Statistika STIS Indonesia Information System Study Program School of Industrial and System Engineering Telkom University Indonesia

Indonesia has entered a period of demographic bonus. Human resources must be optimized. The number of children who do not in employment, education or training (NEET) in each province needs attention. Several factors that could contribute to the decline in the NEET percentage are literacy rates and the number of adolescents with computer skills. Increasing these two factors is believed to be able to reduce the percentage of NEET in every province in Indonesia. To find out the relationship between these two factors and how much influence they have on the percentage of NEET, this research is modeled by Cauchy regression and includes spatial effects. The results of the analysis show that the best model is when the spatial effect is modeled by Fernandez Steel Skew Normal conditionally autoregressive (FSSN CAR). This result is seen from the smallest value of the Watanabe Akaike Information Criterion (WAIC) in this model, which is 190.5. The parameter estimated shows that the higher the literacy rate and the number of adolescents with computer skills, the lower the percentage of NEET in each province in Indonesia. The results of this research can be useful for the Indonesian government to increase the number of educational facilities related to these two factors. © (2024), (International Association of Engineers). All rights reserved.

关键词： Bayesian CAR Cauchy Education NEET

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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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Convolutional Neural Network Analysis on Handwriting Patterns and Its Relationship to Personality: A systematical Review

Convolutional Neural Network Analysis on Handwriting Pattern...

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International Conference on Informatics, Multimedia, Cyber and Information system (ICIMCIS)

作者： Zaid Romegar Mair Widya Cholil Evi Yulianti Dona Marcelina Theresiawati Ika Nurlaili Isnainiyah Informatics Study Program Computer and Natural Science Faculty Indo Global Mandiri University Indonesia Informatics Study Program Computer Science Faculty Universitas Pembangunan Nasional Jakarta Indonesia Information System Study Program Computer and Natural Science Faculty Indo Global Mandiri University Indonesia

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