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Integrating System Dynamics and Random Forest Algorithm for Sales Forecasting

Integrating System Dynamics and Random Forest Algorithm for ...

引用

IEEE International Symposium on Consumer Electronics (ISCE)

作者： Natalia Magdalena Rafu Mamulak Erma Suryani Jerry Dwi Trijoyo Purnomo Doctoral Program in Technology Management Interdisciplinary School of Management and Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Information Systems Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Statistics Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9798350365191

ISBN: (纸本)9798350365207

This study proposes an approach to improve sales forecasting accuracy by integrating System Dynamics (SD) modeling with the Random Forest (RF) algorithm. The combination of these two methods offers a promising way to make sales predictions more reliable, especially in uncertain market conditions. Using SD principles within the RF algorithm helps us simulate expected sales growth, gain insights into market dynamics, and make our forecasting model more understandable to stakeholders. This study using synthetic sales data shows that the integrated approach is offered as an open idea and customizable, particularly in scenarios with dynamic growth patterns and feedback effects.

关键词： Radio frequency System dynamics Heuristic algorithms Predictive models Prediction algorithms Market research Stakeholders Reliability Forecasting Random forests

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Image Processing Implementation to Classify Coffee Fruit Ripeness Using K-Nearest Neighbor (KNN) Algorithm

Image Processing Implementation to Classify Coffee Fruit Rip...

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International Conference on information Management and technology (ICIMTech)

作者： M. Farhan Hussaini Dermawan Deden Witarsyah Dedy Syamsuar Hanif Fakhrurroja Ahmad Luthfi Muhammad Izman Herdiansyah Faculty of Industrial Engineering Telkom University Bandung Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Department of Informatics Faculty of Industrial Technology Islamic University of Indonesia Jogjakarta Indonesia Informatics Study Program Faculty of Science and Technology Universitas Bina Darma Palembang Indonesia

Coffee is one of the plantation crops that has long been a cultivated plant in Indonesia. The classification of coffee fruit maturity manually still has several weaknesses and requires a long process, has low accuracy and is inconsistent, this is because the determination is made subjectively by coffee farmers. As for the classification of coffee fruit maturity levels automatically, it can be faster with objective determination, therefore the use of image processing is relatively easier, faster, and based on a quantified descriptive assessment to determine coffee maturity. Image Processing is a method used to process or manipulate images in 2-dimensional form. In the classification process, there are many methods used to obtain classification of objects based on training data. One of the algorithms used for the classification process is K-Nearest Neighbor (KNN). KNN is a classification technique for objects based on training data that is the closest or has similar characteristics to the object. KNN includes supervised learning algorithms, where the results of the new query instance are classified based on the majority of the categories in K-Nearest Neighbors (K-NN). The finding indicated that class classification of ripe and unripe were 88,24 % and 100% respectively with 93,33% accuracy level.

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A Comparison Diagnosis Algorithm for Conditional Fault Local Diagnosis of Multiprocessor systems 25th

A Comparison Diagnosis Algorithm for Conditional Fault Loca...

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25th International Computer Symposium on New Trends in Computer Technologies and Applications, ICS 2022

作者： Lv, Yali Lin, Cheng-Kuan Hsu, D. Frank Fan, Jianxi School of Information Technology Henan University of Chinese Medicine Zhengzhou China Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Undergraduate Degree Program of Systems Engineering and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan Computer Science and Information Engineering Chung Cheng Institute of Technology NDU Taoyuan City Taiwan Department of Computer and Information Science Fordham University New York United States School of Computer Science and Technology Soochow University Suzhou China

ISBN: (纸本)9789811995811

An efficient diagnosis is very important for a multiprocessor system. In this paper, we present a (α, β) -trees combination S(u, X, α, β) and give some conclusions about the local diagnosis. Based on the (α, β) -trees combination, we give a conditional fault local diagnosis algorithm to identify the fault or fault-free status of each processor correctly under the MM ∗ model when the number of faulty nodes does not exceed α+ 2 β- 3 and every node has at least one fault-free neighboring node. According to our results, a connected network with a (α, β) -trees combination S(u, X, α, β) for a node u is conditionally locally (α+ 2 β- 3 ) -diagnosable at node u and the time complexity of our algorithm to diagnose u is O(α2β+ αβ2). As an application, we show that our algorithm can identify all the faulty nodes of n-dimensional star graph Sn if the faulty node number does not exceed 3 n- 8. Compared with existing algorithms, our algorithm allows more faulty node in a multiprocessor system. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computational complexity

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Rotational Regrasping For a Stick-type Object Using Inertial Force Based on High-speed Swing-up Motion

Rotational Regrasping For a Stick-type Object Using Inertial...

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2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： Senoo, Taku Konno, Atsushi Otsubo, Hayato Ishii, Idaku Hokkaido University Course of Systems Science and Informatic Graduate School of Information Science and Technology Kita 14 Nishi 9 Kita-ku Hokkaido Sapporo060-0814 Japan Hiroshima University Smart Innovation Program Graduate School of Advanced Science and Engineering 1-4-1 Kagamiyama Hiroshima Higashi-Hiroshima739-8527 Japan

ISBN: (纸本)9798350325706

In this paper, robotic regrasping is considered with the goal of achieving dexterous manipulation. The strategy using quick wrist snap is based on human regrasping, and involves rotating the grasped object due to the inertial force while maintaining the contact state. The posture of the grasped object is measured by high-speed vision in real time and is used for posture control. Experimental results are shown where regrasping of a stick-type object in the direction contrary to gravity can be achieved by a high-speed robot hand. © 2023 IEEE.

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Analysis Implementation of Chatbots to Increase Knowledge of Stunting Prevention in Indonesian Society with Bibliometric

Analysis Implementation of Chatbots to Increase Knowledge of...

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International Conference on information Management and technology (ICIMTech)

作者： Indrajani Sutedja Lutfi Handayani Maryuni Hendry Mohammad Reza Faisal Information Systems Department School of Information Sytems Bina Nusantara University Jakarta Indonesia Faculty of Nursing and Midwifery Midwifery Study Program Binawan University Jakarta Indonesia Faculty of Information Technology Satya Wacana Christian University Salatiga Indonesia Department of Computer Science FMIPA Lambung Mangkurat University Banjarmasin Indonesia

ISBN: (数字)9798350390025

ISBN: (纸本)9798350390032

The healthcare industry uses AI, because it is more efficient. In Indonesia, the accuracy of measurements and transmission of children's growth and development is often wrong. Many small hospitals usually don't have length measuring equipment, so the results are inaccurate. This research aims to determine the effectiveness of the stunting prevention program in Indonesia using an information system. The methodology used is a qualitative method, to conduct subjective research and analysis. The results of the research are to improve the website/mobile application information system which can display a child's growth and development history and simple educational information related to nutrition. Digital recording methods can speed up the process of updating data with a central database in real time. The value of this research is to reach the public to utilize technology and obtain personalized and interactive information directly to individuals. The theoretical implications of this research focus on information dissemination and understanding how chatbots influence knowledge acquisition and behavior change in the context of stunting prevention can contribute to advancing these theories in the field of public health. Managerial implications can provide cost effectiveness as Chatbots offer potentially cost-effective solutions and data-driven insights where managers can leverage chatbot analytics.

关键词： Prevention and mitigation Scalability Bibliometrics Refining Chatbots Real-time systems Stakeholders Artificial intelligence Sustainable development Research and development

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Classification Of Ornamental Fish Types Using CNN Method With VGG-16 And U-NET Architecture

Classification Of Ornamental Fish Types Using CNN Method Wit...

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Consumer technology (ICCT-Pacific), International Conference on

作者： Ni Luh Wiwik Sri Rahayu Ginantra Christina Purnama Yati I Gusti Ayu Agung Diatri Indradewi I Komang Agus Yudiawan Technology and Informatics Informatics Engineering Institut Bisnis dan Teknologi Indonesia (INSTIKI) Denpasar Indonesia Department of Informatics Engineering Information Systems Study Program Faculty of Engineering and Vocational Studies Universitas Pendidikan Ganesha (UNDIKSHA) Denpasar Indonesia

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

Ornamental fish are a type of fish that live in freshwater or seawater habitats and are typically maintained for aesthetic purposes, often in ornamental ponds or as household pets. The diversity of colors, shapes, and patterns observed in ornamental fish presents a challenge in their classification. Therefore, a method capable of accurately recognizing the visual characteristics of these fish is required. The objective of this study is to evaluate the performance of two Convolutional Neural Network (CNN) architectures, specifically VGG-16 and U-Net, using five distinct types of ornamental fish, namely Angel Fish, Betta Fish, Clown Fish, Gold Fish, and Guppy Fish. The dataset comprises 3.240 images that have been processed using TensorFlow and Keras on Google Collaboratory. The results demonstrated that U-Net demonstrated superior performance in segmentation, with an Average Intersection over Union (IoU) of 58.91 % and an Average Dice Coefficient of 73.72%. In contrast, VGG-16 exhibited signs of overfitting, as evidenced by lower evaluation metrics. To enhance the efficacy of the transfer learning model, further optimization is recommended.

关键词： Measurement Image segmentation Visualization Shape Transfer learning Fish Internet Convolutional neural networks Optimization Overfitting

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Blockchain technology for vehicle maintenance registration 6

Blockchain technology for vehicle maintenance registration

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6th International Conference on information Management and technology, ICIMTech 2021

作者： Meyliana Surjandy Fernando, Erick Widjaja, Henry Antonius Eka Cassandra, Cadelina Tan, Arifa Carolina, Miranda Carolina, Michele Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Technology Visionaries Petaling Group CEO Jaya46330 Malaysia Bina Nusantara University Faculty of Humanities Business Law Program Law Department Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

Vehicle maintenance is done regularly by vehicle owners to maintain the best performance to avoid harmful effects, such as accidents. However, according to the news, counterfeit spare parts can easily found in vehicle maintenance workshops. Therefore, this study tries to use blockchain technology to record the vehicles for sparepart maintenance. Several previous studies have mentioned the benefits of blockchain technology that can carry out tracking and tracing in the automotive industry, but no discussion on the laboratory-based experiments to see how the implementation of blockchain technology for vehicle identification registration which in the future can be used for tracking components in vehicles. Therefore, this study simulates the recording of vehicle identities using a multichain application. This qualitative research was carried out by doing field observations and discussions with experts in related fields. The simulation results in the laboratory are shown to the expert as a discussion material for validation. From the validation results, it can be concluded that blockchain technology can be used to record vehicle identities, which can be developed for tracking spare parts in vehicles. This result is beneficial for business actors to improve the quality of vehicles. © 2021 IEEE.

关键词： Blockchain

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Tracing of spare parts replacement to improve the usage of genuine parts with blockchain technology 6

Tracing of spare parts replacement to improve the usage of g...

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6th International Conference on information Management and technology, ICIMTech 2021

作者： Meyliana, Meyliana Surjandy Fernando, Erick Eka Widjaja, Henry Antonius Cassandra, Cadelina Tan, Arifa Carolina, Miranda Carolina, Michele Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Technology Visionaries Group CEO Petaling Jaya46330 Malaysia Bina Nusantara University Faculty of Humanities Business Law Program Law Department Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

The problem of counterfeit vehicle parts is something that is still happening today. Various techniques facilitate to prevent counterfeit spare parts in the market. One of many vehicles distributed is a vehicle repair shop - the previous study, stating that blockchain technology can trace and track spare parts. However, in previous studies, the model or method of implementation had not been found. Therefore, this research tries to simulate replacing replacement parts in the Blockchain to make it easier to track. This qualitative research involved a workshop for field observations and discussions with experts in related fields. In this study, a simulation is carried out directly of vehicle parts recorded in blockchain-based (multichain) applications. The results of this study indicate that Blockchain technology possible use for recording vehicle parts. © 2021 IEEE.

关键词： Blockchain

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The Effect of Data Augmentation on the Accuracy of Fish Species Classification Using Deep Learning

The Effect of Data Augmentation on the Accuracy of Fish Spec...

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International Conference on Informatics and Computing (ICIC)

作者： Ellya Helmud Catur Edi Widodo Oky Dwi Nurhayati Doctor Information System Postgraduate Diponegoro University Semarang Indonesia Faculty of Information Technology Information Systems Study Program Atma Luhur Institute of Science and Business Pangkalpinang Indonesia Physics Department Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Computer Engineering Faculty of Engineering Diponegoro University Semarang Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Fish image classification presents an intriguing challenge in the field of computer vision. This research aims to develop an accurate classification model to differentiate between four different fish species using a convolutional neural network. The dataset used consists of $\mathbf{3 0 1 0}$ fish images, divided into training, validation, and testing sets. The convolutional neural network model was trained both with and without data augmentation. Evaluation results show that the model trained with data augmentation achieved an accuracy of $95 \%$ with a loss value of 0.0983, slightly better than the model without augmentation which achieved an accuracy of $94.56 \%$ with a loss value of $\mathbf{0. 1 7 9 4}$. This indicates that data augmentation techniques are effective in improving model performance, likely because augmentation helps the model generalize better to variations in fish image data. The results of this research demonstrate the significant potential of convolutional neural network for fish image classification tasks. The developed model can serve as a foundation for the development of computer vision-based applications such as automatic fish species identification in fisheries or educational applications. Further research can be conducted by exploring different convolutional neural network architectures, more advanced data augmentation techniques, and larger datasets to improve model performance.

关键词： Deep learning Training Accuracy Computational modeling Computer architecture Fish Data augmentation Data models Convolutional neural networks Image classification

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Gateway Implementation of Cybersecurity Requirements 73

Gateway Implementation of Cybersecurity Requirements

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73rd International Astronautical Congress, IAC 2022

作者： Hanson, Svetlana Mustain, Rose E.W. Sosa, Rosa NASA Johnson Space Center METECS United States NASA Johnson Space Center Gateway Program Planning and Control Information Management and Solutions United States NASA Johnson Space Center Jacobs Technology Inc. Avionics Systems Cybersecurity United States

Cybersecurity threats are a constant present-day reality for any type of business - Space exploration is not excluded from these threats either. The Gateway program is one of NASA's latest initiatives that extend space exploration beyond low earth orbit. Gateway allows for NASA to prove technologies and mature systems necessary to live and work on another celestial body before embarking on multi-year missions to Mars. The Gateway is a small, human-tended space station in orbit around the Moon. With the increased autonomy, distance and criticality of systems, cybersecurity is a critical discipline that touches and integrates with most if not all subsystems of the Gateway. Building a gateway to the lunar orbit is no simple task. In this presentation, we outline an approach that the Gateway team adopted in creating a cyber safe and robust vehicle to support operations and assure protection of the critical functions. Gateway program is required to implement National Institute of Standards and technology (NIST) guidelines to adhere to the Federal information Security Modernization Act (FISMA). NIST provides a framework for managing and controlling cybersecurity risks by defining cybersecurity controls and methodologies for implementation. The NIST framework is based upon the system, data within the system, integrations with external systems, and risk assessments to determine impacts for each of those systems. The goals and objectives are to identify appropriate security controls that fulfil and map to the NIST 800-53 framework. The implementation process involves developing an organizational understanding to manage cybersecurity risk to systems, people, assets, data, and capabilities. NIST Security controls are interpreted and defined within the Gateway vehicle requirements subsystems specifications. This paper details the approach, implementation, and challenges faced during the development and design phases to address cyber threats during the Gateway vehicle operations

关键词： Risk assessment

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