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检索条件"机构=Computer engineering and informatics"

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Comparison of Single Exponential Smoothing And Holt-Winter Exponential Smoothing Methods in Sales Commercial Business 11

Comparison of Single Exponential Smoothing And Holt-Winter E...

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11th International Conference on Cyber and IT Service Management, CITSM 2023

作者： Arini Suseno, Hendra Bayu Maulana, Fahmi Nur Matin, Iik Muhamad Malik Uin Syarif Hidayatullah Jakarta Informatics Jakarta Indonesia Jakarta State Polytechnic Computer and Informatics Engineering Jakarta Indonesia

ISBN: (纸本)9798350305968

Sales is an important aspect of running a commercial business. To increase sales, it must be accompanied by adequate availability of goods so that consumer needs can be met. However, the challenge in availability is that it is difficult to predict future consumer needs, so overstock and shortages often occur. On the other hand, the organization must be able to provide goods on time. This can be detrimental to the organization. For this reason, many solutions have been developed to overcome this problem, namely by predicting the availability of goods in the future using the single exponential smoothing and Holt-winter exponential smoothing methods. The single exponential smoothing method was used by previous researchers to provide good predictions on annual historical data. However, by using the same data converted into a monthly format, trend and seasonal factors were discovered. Therefore, we perform ex-inventory predictions using a single exponential smoothing method and then compare their performance. Trend and seasonal factors can be handled with the Holt-Winter exponential smoothing method. To determine the best prediction results from both methods for monthly data, we used the MAPE (Mean Absolute Percentage Error) method. The research results show that the Holt-winter exponential smoothing method has better performance with a smaller error value compared to single exponential smoothing, namely 1.828%. © 2023 IEEE.

关键词： Sales

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Towards Human-Like Machine Comprehension: Few-Shot Relational Learning in Visually-Rich Documents 30

Towards Human-Like Machine Comprehension: Few-Shot Relationa...

引用

Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Wang, Hao Li, Tang Chu, Chenhui Zhu, Nengjun Wang, Rui Zhu, Pinpin School of Computer Engineering and Science Shanghai University China Graduate School of Informatics Kyoto University Japan Department of Computer Science and Engineering Shanghai Jiao Tong University China

ISBN: (纸本)9782493814104

Key-value relations are prevalent in Visually-Rich Documents (VRDs), often depicted in distinct spatial regions accompanied by specific color and font styles. These non-textual cues serve as important indicators that greatly enhance human comprehension and acquisition of such relation triplets. However, current document AI approaches often fail to consider this valuable prior information related to visual and spatial features, resulting in suboptimal performance, particularly when dealing with limited examples. To address this limitation, our research focuses on few-shot relational learning, specifically targeting the extraction of key-value relation triplets in VRDs. Given the absence of a suitable dataset for this task, we introduce two new few-shot benchmarks built upon existing supervised benchmark datasets. Furthermore, we propose a variational approach that incorporates relational 2D-spatial priors and prototypical rectification techniques. This approach aims to generate relation representations that are more aware of the spatial context and unseen relation in a manner similar to human perception. Experimental results demonstrate the effectiveness of our proposed method by showcasing its ability to outperform existing methods. This study also opens up new possibilities for practical applications. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Variational techniques

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Detection and Counting Bacilli Tuberculosis Images using Classical Image Processing and Deep Learning 24

Detection and Counting Bacilli Tuberculosis Images using Cla...

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2nd International Conference on Communications, Computing and Artificial Intelligence, CCCAI 2024

作者： Yuniarti, Heny Fatichah, Chastine Sigit, Riyanto Department of Informatics Faculty of Intelligent Electrical and Informatics Technology Institut Teknologi Sepuluh Nopember Indonesia Department of Computer Engineering Politeknik Elektronika Negeri Surabaya Indonesia

ISBN: (纸本)9798400716898

Tuberculosis (TB) has been known as the top two murdering disease worldwide after HIV/AIDS. Indonesia is the second country with the highest number of tuberculosis. One of the reasons for the high number of cases is that the microscopic sputum smear image analysis process takes a long time. This study compares the detection and counting of bacilli images using classical image processing and deep learning to ascertain the performance of both methods. Steps in classical image processing consist of RGB to HSV converting, HSV thresholding, morphological operation and bacilli counting. YOLOv5 is used as deep learning method in this research to detect and count bacilli. While classical image processing accuracy is 59.59%, YOLOv5 can achieve 86.09% which is better than the previous research. By comparing classical image processing methods and deep learning, it is evident that deep learning techniques can detect and count overlapping bacilli. This result can be further improved by increasing the learning dataset especially the severe TB patient. © 2024 ACM.

关键词： Image analysis

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Goalpost Detection and Recognition on Robot Soccer ERSOW Using SSD MobileNet-V2 FPNlite 25

Goalpost Detection and Recognition on Robot Soccer ERSOW Usi...

引用

25th International Electronics Symposium, IES 2023

作者： Supriyadi, Ujang Bachtiar, Mochamad Mobed Wibowo, Iwan Kurnianto Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350314731

In the Indonesian Robot Contest, EEPIS Robot Soccer on Wheeled (ERSOW) plays soccer on wheels. Based on the rules, the victory of a team is taken from the highest score of the robot in scoring goals against the goalpost. However, it's not as simple as it may be thought to score goals against the goalpost. The robot has to maneuver during the game without stopping, causing the robot's position data generated by the odometry and IMU sensors to experience an accumulation of errors. The goal position data, which will be a reference for the robot's kick against the goalpost, will be impacted by the accumulated errors. This paper presents a goalpost detection system that can be used on robots. Deep Learning Neural Network (DNN) is used by the detecting system to teach itself the crucial visual characteristics of the goalpost in order to make detections. The deep learning model makes use of the Single Snapshot Detector (SSD) MobileNet V2 FPNLite 640x640. An individual image dataset of the goal point from the omnidirectional cameras. SSD MobileNetV2 FPNLite 640x640 model with 1% classification loss, 0.1% localization loss, 9% total loss and confidence score is 96-100%. The accuracy of goalpost detection and recognition is 93% with the frame rate average is 49 FPS. © 2023 IEEE.

关键词： Robots

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Gamification Interaction Design for Membatik Application in Mixed Reality 25

Gamification Interaction Design for Membatik Application in ...

引用

25th International Electronics Symposium, IES 2023

作者： Hanifati, Kirana Rante, Hestiasari Sukaridhoto, Sritrusta Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350314731

Batik is an Indonesian traditional craft that has high artistic value. Indonesian society, especially the younger generation such as students, plays an important role in preserving Indonesian culture such as batik to ensure it remains attached to Indonesia and does not disappear. However, the interest and enthusiasm of students in preserving batik is still lacking, so new strategies are needed to increase their interest and enthusiasm in preserving batik. One of the new strategies is utilizing immersive technology such as Mixed Reality (MR) with the implementation of gamification interaction to enhance students' interest because learning is done as if playing a game. MR technology can provide users with a more complex experience by using MR glasses. The Membatik application is a batik-making simulation application using MR technology where users can simulate with hand gestures and there are quizzes at each stage with a point system as a gamification implementation. The application has been tested with initial user testing. During the testing phase, users were asked to try the application and provide feedback on the overall user experience. The outcomes demonstrated that the MR application might raise students' interest in and involvement in their study. The users also found the quiz feature to be interactive and challenging, which added to their learning experience. By combining gamification elements with MR technology, the application has the potential to improve learning outcomes and increase student motivation. © 2023 IEEE.

关键词： Mixed reality

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An Integrated Platform for Educational and Research Management Using Institutional Digital Resources 3rd

An Integrated Platform for Educational and Research Manageme...

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3rd International Conference on Novel and Intelligent Digital Systems, NiDS 2023

作者： Chytas, Konstantinos Tsolakidis, Anastasios Triperina, Evangelia Karanikolas, Nikitas N. Skourlas, Christos University of West Attica department of Informatics and Computer Engineering Athens12243 Greece

ISBN: (纸本)9783031441455

Currently, universities not only offer technologically enhanced education and automated processes, but also provide a wide spectrum of online services, especially after COVID19. We propose e-EDURES, an integrated platform for educational and research management in tertiary education to further benefit the stakeholders by relying on the data of university online services to enable strategic planning and decision making. More specifically, in this paper, we focus on the presentation of an interactive system that utilizes the data derived by university’s synchronous and asynchronous e-learning services to gain insights about the educational performance of the students. It employs Educational Data Mining techniques to ultimately boost the educational process, by assessing the previous performance, monitoring the provided services and predicting future performance of the students, in order to avoid potential pitfalls. The proposed solution merges the current advances in smart education and educational data mining to offer an advanced and updated version of online services provided by universities that respond better to the current needs in academia. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Data mining

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Stunting Program Classification in East Java, Indonesia from Internet News Using Location-Based and SVM 25

Stunting Program Classification in East Java, Indonesia from...

引用

25th International Electronics Symposium, IES 2023

作者： Ananta, Caesar Jalu Fariza, Arna Asmara, Rengga Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350314731

Stunting is a serious health problem for toddlers in Indonesia, especially in East Java, which makes the Indonesian government seriously try to overcome it to achieve the Golden Generation of Indonesia. To reduce the prevalence of stunting, programs related to stunting are held regularly. This research aims to compile and classify information regarding stunting programs in East Java from internet news. The solution is to create a website that visualizes the prevalence of stunting and stunting programs in East Java, Indonesia, in the form of a map that is divided into several districts in East Java. Stunting prevalence data is taken from SSGI data that has been collected by the Indonesian Ministry of Health, while information about the stunting program is taken from mined internet news, filtered using the SVM machine learning algorithm, and classified based on the location of the stunting program reported. The SVM filtering model created can classify and filter news with an accuracy rate of up to 87%. With this website, it is hoped that the public can easily get information about stunting programs in their location and that the government can conduct an interactive analysis of stunting data in East Java. © 2023 IEEE.

关键词： Location

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Mobile Application for Early Screening of Skin Cancer Using Dermoscopy Image Data Based on Convolutional Neural Network 25

Mobile Application for Early Screening of Skin Cancer Using ...

引用

25th International Electronics Symposium, IES 2023

作者： Fariza, Arna Muliawati, Tri Hadiah Wibowo, Mohamad Akbar Fadlika Setiowati, Yuliana Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350314731

The dermoscopy image produced by the dermatoscope represents the skin surface structure in more detail than clinical images because the imaging technique uses optical magnification and liquid immersion lighting or cross-polarization. Detection of skin cancer is a challenge for several reasons. Doctors find it difficult to detect the presence of cancer on the skin only with dermoscopy images. Sometimes further tests are needed to confirm the results of the diagnosis, such as a biopsy or pathology examination, that is relatively time-consuming. Mobile skin screening technology can be used as an early screening tool for the early detection of melanoma, allowing patients to share images of their lesions with their skin specialists quickly. In this study, a novel method for early skin cancer detection based on dermoscopy pictures and the convolution neural network (CNN) algorithm is proposed. Three convolution layers, two average pooling layers, and a batch normalization layer make up the five feature extraction layers of CNN's proprietary design. The ISIC Archive, which has a collection of skin lesion photos, was the dataset utilized to create the model. Four thousand dermoscopy images in JPG format are used in this study and are divided into two classes: benign and malignant. The dataset is divided randomly into 80% training data and 20% test data. The experiments found that the model with the best performance resulted in a training data accuracy of 99.3% and a test data accuracy of 97.63%. It concludes that the model with batch normalization can distinguish the benign and malignant well. The model requires a small enough space to be easily accessible through mobile devices. © 2023 IEEE.

关键词： Diagnosis

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Obstacle Detection and Tracking System for Robot Soccer ERSOW based on Centroid Tracking Method 25

Obstacle Detection and Tracking System for Robot Soccer ERSO...

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25th International Electronics Symposium, IES 2023

作者： Wardhana, Vega Kurnia Garindra Wibowo, Iwan Kurnianto Bachtiar, Mochamad Mobed Politeknik Elektronika Negeri Surabaya Departement of Informatics and Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350314731

EEPIS Robot Soccer on Wheeled (ERSOW) is a wheeled soccer robot developed by Politeknik Elektronika Negeri Surabaya and participated in the national robot competition, Indonesian Robot Contest in KRSBI Wheeled division. ERSOW team has striker robot, striker robot was used in attack strategy for scoring goals. The ability to tracking the obstacle is one of main abilities that striker robot must has. Obstacle tracking plays a crucial role in robot soccer on wheels due to the dynamic and unpredictable nature of the game. As robots navigate the field, they encounter various obstacles such as opponents, teammates, and static objects (obstacle). Accurate and real-time tracking of these obstacles enables robots to make informed decisions, adapt their strategies, and effectively interact with the environment. Obstacle tracking enhances situational awareness. By continuously monitoring the positions and movements of obstacles, robots gain valuable information about the current game state. Tracking system on this research works with centroid tracking algorithm. The fundamental concept of the centroid tracking algorithm revolves around assigning a distinct identification (ID) to every tracked object by taking their centroids, this algorithm takes the center of obstacle bounding box as an input, to generated bounding box this system used thresholding detection method based on HSV color space. The center of bounding box (in pixel coordinate) converted into world coordinate by using Ab-exponential regression and Linear regression. The testing results of tracking system could estimate obstacle from 50cm-250cm from the robot with 0,967 cm error average, the system also could track the obstacle with 86% accuracy when the robot moved 330 cm ahead. © 2023 IEEE.

关键词： Sports

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The combination of the NDBI and machine learning algorithms to classification the development of urban areas in Surabaya uses Landsat 8 Imagery. 25

The combination of the NDBI and machine learning algorithms ...

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25th International Electronics Symposium, IES 2023

作者： Wibowo, Prasetyo Fariza, Arna Saputra, Ferry Astika Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350314731

Development is a transformation that occurs in order to better people's lives. In this instance, efforts are required to monitor development progress swiftly and efficiently so that outcomes that support the government's new policies are obtained. With a population of 2,848,583 people, Surabaya is the largest metropolitan city in Eastern Indonesia. An increase in population can impact Surabaya's development and generate potential issues with urban planning development. Through these issues, remote sensing technology can efficiently and swiftly detect advances in Surabaya. This research study employs Landsat 8 image data with a combination of Normalized Build-Up Index (NDBI) as pre-processed pixels to segment into urban areas. It employs machine learning to classify urban areas to separate class types in depth. The findings show that the method successfully classified subclasses in residential and industrial areas. Random Forest (RF) outputs have an overall accuracy of 85%, whereas Decision Tree (DT) outputs have an overall accuracy of 84%. This study's findings also indicate the possibility for practical applicability to various case studies, such as conducting detections in forests, plantations, mining, and coastal environments. © 2023 IEEE.

关键词： Landsat

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