检索结果-内蒙古大学图书馆

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

来源：评论

学校读者我要写书评

暂无评论

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

Unraveling Game Addiction Behavior Among Digital Native Stud...

引用

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

来源：评论

学校读者我要写书评

暂无评论

Improved IP lookup technology for trie-based data structures

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

Creating Mathematical Models to Help Formulate Company Strat...

引用

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)

来源：评论

学校读者我要写书评

暂无评论

Internet of Things for Real-Time Multi Room Monitoring System

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

引用

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.

关键词：

来源：评论

学校读者我要写书评

暂无评论

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 ...

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

Trajectory Analysis of Inter-Island Logistics Transportation...

引用

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

来源：评论

学校读者我要写书评

暂无评论

Spatial Hotspot Data and Weather for Forest Fire Data Clustering 5

Spatial Hotspot Data and Weather for Forest Fire Data Cluste...

引用

5th International Conference on Information and Communications Technology, ICOIACT 2022

作者： Mimboro, Prasetyo Kusrini, Kusrini Laksito, Arif Dwi Postgraduate Program Universitas Amikom Yogyakarta Yogyakarta Informatics Engineering Departement Amikom Yogyakarta Indonesia Universitas Amikom Yogyakarta Faculty of Computer Science Amikom Yogyakarta Indonesia

ISBN: (数字)9781665451406

ISBN: (纸本)9781665451406

Prediction of fire forest will be needing several parameters that located on the same location and on the same time frame. This is important to have data on the same location and same time periods, since forest fire mostly triggered by weather or temperature condition on certain area on the certain time. Since there are many parameters involved, the preprocessing will need to made the data have standard structure. The clustering process needed to give a label to the data with five class label, very low risk, low risk, medium risk, high risk, very high risk. Based on the data set, we measurement the best cluster number related with DB index, the results shows that 8 cluster is the best number of the data with DB Index score are 0.8856. © 2022 IEEE.

关键词： Fires

来源：评论

学校读者我要写书评

暂无评论

Evaluating an e-Learning Platform at Graduate School Based on User Experience Evaluation Technique 3

Evaluating an e-Learning Platform at Graduate School Based o...

引用

3rd International Conference on Electrical, Electronics, informatics, and Vocational Education, ICE-ELINVO 2020

作者： Abubakari, M.S. Nurkhamid Hungilo, G. Department of Electronics Engineering Education Postgraduate Program Universitas Negeri Yogyakarta Indonesia Department of Informatics Engineering Graduate Program University Atma Jaya Yogyakarta Indonesia

This study was aimed to evaluate the e-learning platform in a graduate school by using User Experience Questionnaire (UEQ). The data were collected from 65 respondents and analyzed using an excel data analysis tool. The results indicate that all six scales of the instrument are consistent except the novelty scale. Also, the calculated means of the scales are slightly positive as all values are greater than 0.8 compared to the UEQ benchmark. Although the respondents show a little positive impression with the platform, all scales means are below average category except efficiency scale which is above average as compared to the benchmark. This indicates that the platform needs more improvement in all aspects of user experience for it to be at least in a good category. The findings discussed in this paper are valuable for all academic stakeholders in various educational institutions which implement interactive e-learning systems as well as practitioners of user experience and developers of e-learning systems. © 2021 Published under licence by IOP Publishing Ltd.

关键词： Learning systems

来源：评论

学校读者我要写书评

暂无评论

Sentiment Analysis in Indonesian Trading using Lexicon-based and Support Vector Machine

Sentiment Analysis in Indonesian Trading using Lexicon-based...

引用

2023 International Conference on Computer Science, Information Technology and engineering, ICCoSITE 2023

作者： Oetama, Raymond Sunardi Yanfi, Yanfi Ikhsan Assiddiq Up, Masagus M. Muhamad Isa, Sani Universitas Multimedia Nusantara Information System DepartmentFaculty of Engineering and Informatics Jakarta Indonesia Bina Nusantara University BINUS Graduate Program Computer Science School of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350320954

Many individuals now trade online utilizing trading software in the digital world. Binomo is one of Indonesia's most popular trading platforms. This is because some influencers made several promises to Binomo customers. Since many customers were deceived, this case became quite popular. This study was executed to see how Indonesians felt about the Binomo application after the case went viral. The solution taken was in the form of sentiment analysis because there had been no previous research on sentiment analysis that discussed the Binomo case. The data was scanned using Netlytic tools, a cloud-based text and social network analyzer capable of identifying any talks on social media sites such as Twitter. The sentiment analysis of Binomo trading tweets by using the Multi-Perspective Question Answering lexicon utilized the KNIME tool. But unfortunately, the accuracy of sentiment analysis results is low. Furthermore, the Support Vector Machine technique is also being conducted. The Term Frequency-Inverse Document Frequency method is applied to perform feature extraction whilst the chi-square approach is utilized to identify features that are thought to be useful for inclusion in the classification process and to exclude features that are irrelevant to the target class. The obtained accuracy is 86%. The study proposes that words from the algorithm's outputs can be utilized to improve the quality of sentiment analysis using the lexicon. As an outcome of the algorithm, positive and negative terms are added to the lexicon, increasing the accuracy of sentiment analysis using the new vocabulary from 58.984% to 71.146%. © 2023 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：