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4th International Conference on Electrical, Electronics, informatics, and Vocational Education, ICE-ELINVO 2021

作者： Fatih Rizqon, Muhammad Jati, Handaru Department of Electronics and Informatics Engineering Education Postgraduate Program Yogyakarta State University Indonesia

Some fuzzy time series models have their own advantages and disadvantages. In addition, these models sometimes are complex and claimed to have better forecasting result than each other. The suitable model for forecasting depends on a wide variety of considerations. The models proposed by Chen (1996) applied simplified arithmetic operations and claimed more efficiency than before. The model proposed by Chen was introduced in 1996 and still exists in several previous studies. This research aims to forecast the number of railway passengers in Indonesia using the fuzzy time series. In addition, this research also evaluates the forecasting results based on mean absolute error (MAE) and mean absolute percentage error (MAPE). The results showed the forecasting results in this research has accuracy for 86.6%. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Forecasting

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UC3M-PUCPR at SemEval-2022 Task 11: An Ensemble Method of Transformer-based Models for Complex Named Entity Recognition 16

UC3M-PUCPR at SemEval-2022 Task 11: An Ensemble Method of Tr...

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16th International Workshop on Semantic Evaluation, SemEval 2022

作者： Schneider, Elisa Terumi Rubel Rivera-Zavala, Renzo M. Martinez, Paloma Moro, Claudia Paraiso, Emerson Cabrera Graduate Program on Informatics Pontifícia Universidade Catolica do Paraná Brazil Computer Science and Engineering Department Universidad Carlos III de Madrid Spain

ISBN: (纸本)9781955917803

This study introduces the system submitted to the SemEval 2022 Task 11: MultiCoNER (Multilingual Complex Named Entity Recognition) by the UC3M-PUCPR team. We proposed an ensemble of transformer-based models for entity recognition in cross-domain texts. Our deep learning method benefits from the transformer architecture, which adopts the attention mechanism to handle the long-range dependencies of the input text. Also, the ensemble approach for named entity recognition (NER) improved the results over baselines based on individual models on two of the three tracks we participated in. The ensemble model for the code-mixed task achieves an overall performance of 76.36% F1-score, a 2.85 percentage point increase upon our individually best model for this task, XLM-RoBERTa-large (73.51%), outperforming the baseline provided for the shared task by 18.26 points. Our preliminary results suggest that contextualized language models ensembles can, even if modestly, improve the results in extracting information from unstructured data. © 2022 Association for Computational Linguistics.

关键词： Deep learning

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Benchmarking A New Dataset for Coffee Bean Defects Classification Based on SNI 01-2907-2008 2

Benchmarking A New Dataset for Coffee Bean Defects Classific...

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2nd International Conference on Information Technology Research and Innovation, ICITRI 2023

作者： Antara Kesiman, Made Windu Sulaiman, Ismail Maysanjaya, I Made Dendi Dermawan, Kadek Teguh Universitas Pendidikan Ganesha Virtual Vision Image and Pattern Research Group Dept. of Informatics Engineering Bali Indonesia Universitas Syiah Kuala Program Study Teknologi Hasil Pertanian Faculty of Agriculture Banda Aceh Indonesia

ISBN: (纸本)9798350324945

To develop a device which can determine automatically the quality of coffee beans, the module to identify and classify coffee bean defects based on digital images processing is needed. For this reason, a new dataset of coffee bean defect images is built in this research. 3 classes were defined and obtained from the image sample of coffee beans, namely good coffee beans, defective coffee beans, and non-valid coffee beans. Moreover, specifically for the class of defective coffee beans, the new dataset also provides ground truth image samples for 17 classes of coffee bean with defects which was defined by SNI 01-2907-2008. Two Convolutional Neural Network (CNN) architecture models, InceptionResnetV2 and MobileNet, were tested to do an initial benchmarking process to classify coffee beans and their types of defects. Both architectures show very promising results to classify coffee bean images in 3-classes. With 92.52% of accuracy, MobileNet outperforms slightly InceptionResnetV2. Both architectures are fairly easy to classify the 3-classes of coffee bean images. But the experimental results indicate that the task of classifying more specifically the 17 classes of different types of defects in coffee beans is still quite difficult. Although the accuracy of InceptionResnetV2 of 53.35% seems much higher than the accuracy of MobileNet, the two architectures still seem to be struggling to identify each type of coffee bean defect. This benchmarking results will serve as the initial basis that helps to analyze the level of complexity of problems in the coffee bean new dataset, as well as the basis for further investigations and research processes to improve the performance of the future coffee bean classifier machine. © 2023 IEEE.

关键词： Defects

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Development of Training Kit Basic Aerial Robotics Using RC Plane Robot for Robotics Course 4

Development of Training Kit Basic Aerial Robotics Using RC P...

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4th International Conference on Electrical, Electronics, informatics, and Vocational Education, ICE-ELINVO 2021

作者： Aziz, F.N. Mashoedah Department of Electronics and Informatics Engineering Education Postgraduate Program Universitas Negeri Yogyakarta Indonesia

RC Plane Robot and basic aerial robotics are technologies that are widely used, so training kits are needed to learn them. This study aims to: (1) Design and build, (2) Test performance and (3) Determine the feasibility level of training kit basic aerial robotics using RC plane robot. The study used the Research and Development method with the following procedures: (1) Potential and problem, (2) Data collection, (3) Design, (4) Validation, (5) Revision and (6) Trial. The object of the research is the RC Plane Robot training kit and the practicum jobsheet. The research subjects were students of the Electronic engineering Education Study program, Faculty of engineering, Universitas Negeri Yogyakarta. The results of the research are in the form of hardware and practicum jobsheets. The hardware part consists of a robot plane, a work area (Box) and a remote control. The level of feasibility of training kits by material experts gets a percentage of 91%. By media experts get a percentage of 89.5%. By respondents (students) get a percentage of 86%. Based on these results, the training kit basic aerial robotics using RC plane robot is very feasible to be applied to the Robotics course. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Robots

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Unraveling Game Addiction Behavior Among Digital Native Students: Leveraging Machine Learning for Insights

Unraveling Game Addiction Behavior Among Digital Native Stud...

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IEEE International Conference on Communication, Networks and Satellite (ComNetSat)

作者： Rona Nisa Sofia Amriza Cut Alna Fadhilla Department of Information System Telkom University Purwokerto Indonesia Informatics Study Program Faculty of Engineering Universitas Samudra Aceh Indonesia

ISBN: (数字)9798350368086

ISBN: (纸本)9798350368093

The allure of gaming often leads to excessive engagement, which can result in addictive and problematic behaviors, negatively impacting academic performance and school achievements. While gaming becomes increasingly prevalent, concerns about game addiction have grown, particularly among students. The excessive and obsessive usage of video games that characterizes game addiction can have detrimental effects like academic deterioration, social isolation, and mental health problems. This study seeks to elucidate the complex patterns of game addiction among digital-native students by utilizing advanced machine learning techniques. We employed Random Forest, SVM, Decision Tree, and Naïve Bayes algorithms to classify game addiction data from students. To address the class imbalance, we applied Borderline-SMOTE. Our results demonstrate that decision tree and random forest outperformed other machine learning approaches, achieving an accuracy of $\mathbf{9 5 \%}$ in classifying game addiction. These findings contribute to the expanding body of knowledge on game addiction. Furthermore, in order to provide real-time monitoring and intervention for students who are at risk of developing a game addiction, future research may investigate incorporating these findings into educational platforms or applications.

关键词： Support vector machines Video games Addiction Games Real-time systems Vectors Bayes methods Decision trees Older adults Random forests

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Improved IP lookup technology for trie-based data structures

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Journal of Computer and System Sciences 2023年 133卷 41-55页

作者： Lin, Yen-Heng Hsieh, Sun-Yuan Master Degree Program on Artificial Intelligence National Cheng Kung University Tainan701 Taiwan Department of Computer Science and Information Engineering Institute of Manufacturing Information Systems Institute of Medical Informatics National Cheng Kung University Tainan701 Taiwan Department of Computer Science and Information Engineering National Chi Nan University Nantou Puli Township54561 Taiwan

Many Internet protocol (IP) lookup algorithms have been formulated to improve network performance. This study reviewed and experimentally evaluated technologies for trie-based methods that reduce memory access, memory consumption and IP lookup time. Experiments involving binary tries (for simplicity) were conducted on four real-world data sets, two of which were on IPv4 router tables (855,997 and 876,489 active prefixes) and two of which were on IPv6 router tables (155,310 and 157,579 active prefixes). Technologies designed to reduce time taken (at the expense of memory consumption) worked well for IPv4, and those designed to reduce memory consumption worked well for IPv6. Various combinations of these technologies were applied together in the experiments. © 2022 Elsevier Inc.

关键词： Table lookup

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Creating Mathematical Models to Help Formulate Company Strategies for Knowledge Management System Implementation 9

Creating Mathematical Models to Help Formulate Company Strat...

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

作者： Sardjono, Wahyu Perdana, Widhilaga Gia Pranata, Dewi Sagita BINUS Graduate Program Master of Information Systems Management Bina Nusantara University Information Systems Management Department Jakarta11480 Indonesia Post Graduate Program of Management Science State University of Jakarta Jakarta13220 Indonesia King's College London Cyber Security Studi Program Faculty of Natural Mathematical & Engineering Sciences Department of Informatics Strands West MinsterWC2R 2LS United Kingdom

ISBN: (纸本)9798350390025

This research aims to build a mathematical model to formulate the problems of implementing knowledge management systems in companies that often face obstacles in achieving the desired objectives and goals. With increasing competition in the business sector, organizations or organizations realize the importance of utilizing the knowledge assets that reside in each individual and organization. If this knowledge can be managed optimally, it can become a competitive advantage for the company due to the emergence of innovative ideas by the concepts of knowledge management theory. By implementing optimal knowledge management (KM), companies can design innovative solutions to improve business operations and increase overall revenue. The factor analysis method will be used to find the determining factors for the success of the implementation of knowledge management systems (KMS) and the regression analysis method will also be used to form a mathematical model of several new factors which are formed as independent variables with the current level of community understanding of KMS as the dependent variable. The research results provide insight into strategies to improve success factors to successfully facilitate KMS implementation. This study contributes to existing knowledge management by providing insight into dynamics that go beyond the technical aspects of KMS, ultimately developing strategies for more effective and efficient utilization of knowledge and organizational growth and supporting competitive advantage for companies. © 2024 IEEE.

关键词： Knowledge organization system (KOS)

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Internet of Things for Real-Time Multi Room Monitoring System

Internet of Things for Real-Time Multi Room Monitoring Syste...

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Smart Technology (ICE-SMARTec), IEEE International Conference on

作者： Kim Sung Tae Antonius Suhartomo Vincent Study Program of Electrical Engineering President University Bekasi Study Program of Electrical Engineering President University Bekasi Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

Electronic devices have become an integral component in our daily activities. In fact, the consumption of electrical energy in households reached 49% in 2018, and is expected to increase to 58% in 2050. Concurrently, the LPG is the second highest consumed energy in the household and is expected to increase to 37% in 2050. However, the devices utilizing these energies could lead to a danger such as housefire if not operated and treated carefully. An Internet of Things (IoT)-based smart home monitoring system would solve this problem, especially by effectively monitoring several parameters in the house simultaneously. In this study, a system is built to monitor and control three rooms in the house independently and simultaneously. The sensors and/or actuators in all rooms are controlled by NodeMCU ESP8266s. All microcontrollers are linked via the Internet to a local server, where all data is analyzed, saved, and transmitted to the user using the MQTT protocol. The monitoring is done by the user via MQTT dashboard in the smartphone. Following testing, the three microcontrollers are fully capable of exchanging data with the user's smartphone. The humidity changed by 1%, while the temperature changed by 0.1 °C. Perfect data interchange is possible between the relays, flame, gas, and movement activity sensor.

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Achievement of green campus indications based on assessment indicators on H-BAT program Universitas Negeri Semarang 9

Achievement of green campus indications based on assessment ...

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9th engineering International Conference, EIC 2020

作者： Prihanto, T. Fathoni, K. Prasetiyo, B. Study Program of Architecture Faculty of Engineering Universitas Negeri Semarang Indonesia Study Program of Electrical Engineering Faculty of Engineering Universitas Negeri Semarang Indonesia Study Program of Informatics Universitas Negeri Semarang Indonesia

Universitas Negeri Semarang (UNNES) has a spirit to become a Conservation and International Reputation University is realized through strategic missions. One of the conservation-oriented implementation programs is H-BAT (Green, Clean and Healthy) program. In its implementation, there is a problem, namely the achievement of H-BAT program has not been maximized during the last 3 years in assessment. The objectives of this research are: (1) to identify of the potential and character of environmental support in the work units;(2) to determine the achievement of the indication of a green campus based on the assessment indicators on H-BAT program. This research was carried out in four stages: (1) Data collection;(2) Spatial data processing;(3) Non Spatial data processing;(4) Analysis (ecological, technical standards);The results achieved in June 2020 are as follows: FT got 87,50%, FE got 85,87%, FMIPA got 81,52%, BUHK got79,49%, FIP got 73,91%, LP3 got 69,23%, LP2M got 63,46%, FIS got 60,33%,PPS got 57,07%, FIK got 56,52%, FH got 52,72%, FBS got 49,46%, TIK got 40,38%, BPB got 34,62%, Library got 30,13%, Archive got 17,31%. From these results, it can be seen that there is a lack of maximum achievement due to, among others, the lack of guidance and technical assistance in the management of the unit. So it is necessary to complete governance guidelines and technical assistance that are more intensive in unit governance to achieve the goals of UNNES as a green campus. © Published under licence by IOP Publishing Ltd.

关键词： Data handling

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Trajectory Analysis of Inter-Island Logistics Transportation for Aggregate Material Using Web-Based Information 2

Trajectory Analysis of Inter-Island Logistics Transportation...

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2nd International Conference on Technology Innovation and Its Applications, ICTIIA 2024

作者： Suraji, Aji Ishak, Syarifuddin Prabowo, Prabowo Fatkhurrozi, Muhammad Alawy, Gholiqul Amrodh Putra, Rangga Pahlevi University of Widyagama Malang Dept. of Civil Engineering Malang City Indonesia Malang State University Study Program of D4 Eng. Tech and Civil Building Maintenance Malang City Indonesia Institut Teknologi Sumatera Dept. of Ocean Engineering Lampung Selatan Regency Indonesia University of Widyagama Malang Dept. of Informatics Engineering Malang City Indonesia

ISBN: (纸本)9798350351613

Transportation of material aggregates over long distances and between islands requires a long time by changing modes of transportation. Therefore, determining a material aggregate travel trajectory model using a web database is something that can help in logistics movement analysis. This study aims to create a web-based material aggregate transportation trajectory model. The data collection method involves extracting data with Google Maps which shows distance and time indications. Next, validation is carried out in the field with a direct survey at the location. The analytical method uses statistical methods to create modeling using a linear regression model approach and statistical tests using anova. The results of the study show that the linear regression model is simple with an independent variable coefficient of 0.0306 and an R-square of 0.8649. Meanwhile, using the ANOVA test, it was stated that the modeling results were quite significant and the model could be used to make predictions. © 2024 IEEE.

关键词： aggregate material logistics trajectory transportation web-based information

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