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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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Application of the K-Medoids Clustering Model and Borda Decision Group in Viewing Flood Area Groupings in North Aceh Regency

Application of the K-Medoids Clustering Model and Borda Deci...

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Artificial Intelligence and Image Processing (IWAIIP), International Workshop on

作者： Wahyu Fuadi Ilham Sahputra Dedi Fariadi Agustinar Informatics Engineering Study Program Faculty of Engineering Universitas Malikussaleh Lhokseumawe Indonesia Information Systems Study Program Faculty of Engineering Universitas Malikussaleh Lhokseumawe Indonesia Electrical Engineering Study Program Faculty of Engineering Universitas Malikussaleh Lhokseumawe Indonesia

The importance of the K-Medoid model in viewing disaster-prone areas in each sub-district in North Aceh. The advantage of the k-medoid model results is that it has better cluster centers than other models. The novelty of this research is the cluster results are assessed by each decision maker using the Borda group model in providing flood disaster assistance or areas that are a priority for handling, which are included in the flood-prone cluster. Therefore, this research is important because it can be used to identify areas most severely affected by flooding. This research aims to analyze and identify the impact of flood hazards based on grouping and decision-making using the Borda group model in each sub-district in North Aceh. The results of the k-medoids model show 3 clusters that can see potential areas for flood disasters, namely very vulnerable. There are six sub-districts. For the k-medoids results, ten sub-districts are vulnerable. Lastly, the safe zones are 11 sub-districts. This research’s k-medoid model analysis is more precise in seeing clusters of places that are potentially affected by flooding based on spatial maps of their nearest neighboring areas. The percentage of each cluster is as follows: Cluster 1 is 4%, Cluster 2 is 55%, and Cluster 3 is 41%. The k-medoid model provided this result after three iterations, with a closeness difference of 1.594510537. The results of the Borda model group research can be used as a recommendation for the areas that receive the highest priority in disaster management.

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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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Classification of Rupiah Banknote for the Visually Impaired using Convolutional Neural Network with Custom Architecture and RMSProp Optimizer

Classification of Rupiah Banknote for the Visually Impaired ...

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International Conference on ICT For Smart Society (ICISS)

作者： Dede Kurniadi Murni Lestari Rahmi Asri Mulyani Muhammad Rikza Nashrulloh Yosep Septiana Indri Tri Julianto Department of Computer Science Institut Teknologi Garut Garut Indonesia Informatics Study Program Institut Teknologi Garut Garut Indonesia

ISBN: (数字)9798350389654

ISBN: (纸本)9798350389661

Visual impairment is a condition that affects the sense of sight. In Indonesia, around 1.5% of the 4 million people are blind. In their daily activities, the visually impaired rely on their sense of touch to recognize the value of money by feeling the double lines on the right and left sides of the rupiah banknote. However, if the money is in bad condition, they are unable to recognize it, so they have to involve the help of others with the risk of fraud. Research is underway to develop a paper currency detection tool to assist the visually impaired in everyday transactions and reduce the risk of fraud. The research in this paper focuses on creating a classification model for detecting Indonesian rupiah banknotes. The method approach used in this research involves using a custom architecture Convolutional Neural Network model that combines elements from the AlexNet and VGGNet architectures, along with the RMSProp Optimizer. Before training the model, the private dataset that has been collected is augmented to increase the dataset and then split data. Next, the model is trained and tested using a private dataset of 800 images of rupiah banknotes for 2016, 2020, and 2022 emissions years, divided into eight classes of banknotes with denominations of IDR 1000, IDR 2000, IDR 5000, IDR 10,000, IDR 20,000, IDR 50,000, IDR 75,000, and IDR 100,000. Finally, the model's performance is evaluated using loss function, accuracy, precision, recall, and AUC-ROC evaluation metrics. The model evaluation results show a value loss function of 1.28, an accuracy of 95.31 %, a precision of 95.24%, a recall of 93.75%, and an AUC-ROC value of 0.99. This evaluation result indicates that the model is highly effective for use in the rupiah currency nominal detection system for the visually impaired.

关键词： Training Measurement Accuracy Visual impairment Data models Fraud Classification algorithms Convolutional neural networks Currencies Optimization

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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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Design of an Infusion Capacity Monitoring System for Patients Based on the Internet of Things in Hospitals

Design of an Infusion Capacity Monitoring System for Patient...

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International Conference on Business and Industrial Research (ICBIR)

作者： Vina Septiana Windyasari Silvia Ayunda Murad Syahriani Syam Maulia Mahardika Johan Budiman Diah Rahmawati Information Systems Study Program Islamic Syekh-Yusuf University Tangerang Indonesia Information System Department BINUS Online Learning Binus University Jakarta Indonesia Informatics Engineering Study Program Islamic Syekh-Yusuf University Tangerang Indonesia Civil Engineering Study Program Islamic Syekh-Yusuf University Tangerang Indonesia

ISBN: (数字)9798350383027

ISBN: (纸本)9798350383034

The patient's infusion fluid will only be changed if the nurse changes it or based on information from the patient's family. The research method used in terms of fluid volume utilizes the function of the Arduino microcontroller as the brain, the load cell sensor as an infusion fluid weight detector connected to the IoT, fuzzy logic calculations on the infusion volume weight measuring device in line with real-time decision-making when reading gram weight changes with sensors, and the system development method using a prototype model. The test results of three infusion bottles containing mineral water with a liquid volume of 500 ml and a load cell sensor as an infusion weight detector can be accepted by the Arduino Wemos D1 as a load cell sensor processing component. The infusion liquid weight data is uploaded to the MySQL database, and a notification is sent and displayed on the smartphone when the infusion volume reaches 12% of its weight. The results of this study indicate that the design of tools connected to applications on smartphones can work well and facilitate the process of monitoring infusions for patients in real-time

关键词： Weight measurement Fuzzy logic Solid modeling Liquids Databases Volume measurement Prototypes Detectors Real-time systems Monitoring

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Aircraft Flight Schedule Announcement at Pionir Indonesian Airport Based on SMS Broadcast

Aircraft Flight Schedule Announcement at Pionir Indonesian A...

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Aerospace Electrical Engineering and informatics (ICAAEEI), International Conference of Adisutjipto on

作者： Anggraini Kusumaningrum Septian Wahyu Ahmad Sahal Asih Pujiastuti Astika Ayuningtyas Elizabeth Anna Pratiwi Informatics Study Program Adisutjipto Institute of Aerospace Technology Yogyakarta Indonesia Informatic Engineering Department Adisutjipto College of Technology Yogyakarta Indonesia D3 Information Technology Study Program Faculty of Science and Technology Respati University Yogyakarta Indonesia Aerospace Engineering Study Program Adisutjipto Institute of Aerospace Technology Yogyakarta Indonesia

ISBN: (数字)9798331507930

ISBN: (纸本)9798331507947

The design and construction of delivering announcements using a web-based SMS gateway aims to help overcome problems that often occur at pioneer airports; this is because when passengers want to travel, many passengers still arrive late to check-in, even though the passenger ticket has a notification for checking. -in 1 hour before Departure. If a flight's Departure is delayed or accelerated, passengers will only receive this information when they arrive at the airport, whereas if at the time the departure notification is delayed or accelerated, the passenger is still outside the airport or still on their way to the airport, as a result, they will definitely not receive the information. Testing voice announcements on parking, arrival, departure, delay, check-in, and waiting room services, as well as testing SMS broadcasts (30 user numbers and one airline), shows that the announcement application provides information through voice data recording and broadcasts SMS to passengers and families. Testing was carried out at Indonesian Pioner airport for 30 passengers for a week. The percentage of Ease of obtaining test voice information was 81.98%, and the percentage of Ease of obtaining broadcast SMS information was 83.1%. Implementing the announcement is feasible at pioneer airports with a percentage of 82.54%.

关键词： Electrical engineering Schedules Logic gates Airports Recording Delays informatics Aircraft Testing Airline industry

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Unveiling Factors Affecting Intentions to Use ChatGPT: Perspectives from Indonesian Higher Education Students

Unveiling Factors Affecting Intentions to Use ChatGPT: Persp...

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International Conference on informatics and Computing (ICIC)

作者： Zaenal Abidin Kongkiti Phusavat Riza Arifudin Endang Sugiharti Study Program of Informatics Engineering Universitas Negeri Semarang Semarang Indonesia Industrial Enginering Faculty of Engineering Kasetsart University Bangkok Thailand

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

ChatGPT has generated significant interest and discussion within the academic community since its initial introduction. This study aims to investigate the students’ intentions to use ChatGPT for educational purposes in higher education. A proposed conceptual model based on the unified theory of acceptance and use of technology model-2 (UTAUT2) was employed in this current study. A total of 575 data obtained from a questionnaire survey of higher education students were analyzed using partial least square-structural equation modeling (PLS-SEM). The results of the hypothesis testing revealed that both cognitive attitude and emotional attitude have a significant influence on the intention to use ChatGPT. The cognitive attitude was influenced by performance expectancy and effort expectancy. Affective attitudes were shaped by factors such as novelty value, performance expectancy, hedonic motivation, and effort expectancy.

关键词： Surveys Analytical models Education Position measurement Chatbots Mathematical models Data models informatics Testing

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Image Processing Implementation to Classify Coffee Fruit Ripeness Using K-Nearest Neighbor (KNN) Algorithm 8

Image Processing Implementation to Classify Coffee Fruit Rip...

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8th International Conference on Information Management and Technology, ICIMTech 2023

作者： Hussaini Dermawan, M. Farhan Witarsyah, Deden Syamsuar, Dedy Fakhrurroja, Hanif Luthfi, Ahmad Herdiansyah, Muhammad Izman Telkom University Faculty of Industrial Engineering Bandung Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Islamic University of Indonesia Faculty of Industrial Technology Department of Informatics Jogjakarta Indonesia Universitas Bina Darma Informatics Study Program Faculty of Science and Technology Palembang Indonesia

ISBN: (纸本)9798350326093

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. © 2023 IEEE.

关键词： Classification (of information)

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Analyzing the use of SEO and Google Analytic on the Website of Vocational High School of Information Technology Annajiyah 3

Analyzing the use of SEO and Google Analytic on the Website ...

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

作者： Supraba, L.D. Jati, H. Electrical and Informatics Engineering Education Study Program Faculty of Engineering Yogyakarta State University Indonesia

The number of Islamic boarding school teaching both Information Technology (IT) Education and Religious Education is still limited. Vocational High School of Information Technology Annajiyah located in Tambakberas, Jombang, East Java is a school having both subjects taught. The existence of Vocational High School of Information Technology Annajiyah has not been widely known by outside and inside society of Jombang Regency. Therefore, Therefore, analysis of the use of SEO is necessary to determine the potential of website visit and school promotion. Previously, Vocational High School of Information Technology Annajiyah promoted school by using brochures only. This method was considered able to introduce the school through traditional way. Analysis result of improving SEO can be seen in Google Analytic recorded a number of site visits amounted 403 in June, where 82.2% of active users are dominated by mobile phone users and fluctuation of the number of visits. This increase is affected by both the implementation of SEO and online PPDB process during the COVID pandemic. © 2021 Published under licence by IOP Publishing Ltd.

关键词： Websites

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