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EXPLORING INFLUENTIAL FACTORS IN KNOWLEDGE SHARING WITHIN INDONESIAN PRISONS 22

EXPLORING INFLUENTIAL FACTORS IN KNOWLEDGE SHARING WITHIN IN...

22nd International Conference on e-Society 2024, ES 2024 and 20th International Conference on Mobile Learning 2024, ML 2024

作者： Imandeka, Ejo Hidayanto, Achmad Nizar Suhartanto, Heru Pidanic, Jan Department of Computer Science Faculty of Computer Science University of Indonesia Depok Indonesia Department of Electrical Engineering Faculty of Electrical Engineering and Informatics Pardubice University Pardubice Czech Republic

The exchange of knowledge is widely recognized as a crucial aspect of effective knowledge management. When it comes to sharing knowledge within Prison settings, things get complicated due to various challenges such as limited resources, social stigma, isolation, communication barriers, and security concerns. Additionally, there's a scarcity of resources dedicated to promoting knowledge sharing in secure environments. This study aims to dig deep into the key factors that influence how knowledge is shared in Indonesian prisons. To navigate this intricate terrain, the study uses questionnaires distributed among Prison officers to understand the complex relationship between the identified critical factors and knowledge sharing in Prison. Leadership support, organizational culture, organizational structure, incentives, and individual beliefs emerge as pivotal elements in knowledge sharing. It's also noted that the connection between these critical factors and the intention to share knowledge is significantly influenced by organizational structure. Recognizing and addressing critical factors in knowledge sharing within prisons not only improves rehabilitation within prison walls but also transforms the broader criminal justice system. This approach fosters evidence-based practices, better communication, and a comprehensive understanding of factors leading to successful rehabilitation and reduced recidivism, ultimately creating a more just and effective system. © 2024 International Conferences e-Society 2024 and Mobile Learning 2024. All rights reserved.

关键词： Critical Factors Indonesia Knowledge Knowledge Sharing Prison

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A classifier design for micro bubble generators based on deep learning technique

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Materials Today: Proceedings 2023年 80卷 2684-2696页

作者： Qaddoori, Ayman S. Saud, Jamila H. Hamad, F.A. Informatics Institute for Postgraduate Studies Iraqi Commission for Computer and Informatics Baghdad Iraq College of Science Mustansiriyah University Baghdad Iraq School of computing Engineering and Digital Technology Teesside University Middlesbrough United Kingdom

This paper aims to study the sizes of bubbles generated by the micro-bubble generator device (MBGs) in the water because of its impact on the dissolved oxygen percentage in the water and we find this in aquaculture where oxygen is important for marine life and in many applications. Where we have designed a classifier for micro-bubbles of different sizes and the effectiveness of the device has been proven in generating bubbles whose size ranges from 20 to 50 and calculating their numbers in the image by converting the image from (RGB) to (HSV), it has proven its effectiveness in maintaining lighting and neglecting color information Which is not important in identifying bubbles in the image, as well as increasing the processing speed, and then finding edges in the image, using canny edge detection, which has proven its great effectiveness in finding weak and strong edges. Where we then used the (Circular Hough Transform) algorithm to identify the tiny bubbles in the image, calculate their average diameter and numbers in the image, and find the overlapping bubbles between them by using (Two Thresholds) one for the edge and the other for the center of the bubble and then making (Segmentation) for these bubbles and inserting them on (Gaussian Normal Distribution) Do not neglect the dark bubbles, because in most cases they are unreal bubbles and may be distortions in the image or reflections in the lighting. And then make (zero paddings) for the extracted images to make them equal in size and avoid the distortion that will happen in the image if another method is used to make all bubbles of equal dimensions to prepare them for classification. And then we calculated the (mean square error) of the resulting bubble images to avoid repeating the same bubble in (datasets). The efficiency of the classifier was calculated by using deep learning technology. Where we built a (Convolution Neural Network) consisting of (15) layers and then we trained these layers on the images resul

关键词： Feature extraction

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Modified MobileNetV2 with Convolutional Block Attention Module for Facial Expression Recognition

Modified MobileNetV2 with Convolutional Block Attention Modu...

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2023 International Workshop on Artificial Intelligence and Image Processing, IWAIIP 2023

作者： Nafi'an, Muh. Hafidh Ilmi Bachtiar, Fitra Abdurrachman Setiawan, Budi Darma Brawijaya University Computer Science Faculty Informatics Engineering Department Malang Indonesia

ISBN: (纸本)9798350382914

Facial emotion recognition is one of the artificial intelligence implementations used to recognize emotions based on data learned by computers. Unlike humans, who can recognize a person's emotions directly, computers need several trainings procedures conducted by humans to be able to recognize a person's emotions. Previous research has proposed various methods with deep learning and traditional machine learning approaches to classify emotions based on faces. Some studies obtained relatively high accuracy, but on evaluation by cross-validation, the results were much lower than the accuracy obtained. Therefore, this study proposes an approach using a modified MobileNetV2 deep learning architecture in the residual layer by adding a Convolutional Block Attention Module (CBAM) to improve accuracy and data generalization. This experiment uses the Karolinska Directional Emotional Faces (KDEF) public benchmark dataset. Preprocessing is done with image augmentation to enrich the training data by resizing, center cropping, random horizontal flipping, random affine, and random rotation. The proposed method is evaluated by 5-fold cross-validation and compared with MobileNetV2 without modification and state-of-the-art methods. Experimental results show that the proposed model outperform all comparative methods by achieving a 5 -fold cross-validation accuracy of 94.387%. © 2023 IEEE.

关键词： Emotion Recognition

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Analysis of *** Email Domain Security Status Using DMARC (Domain-Based Message Authentication, Reporting, and Conformance) 6

Analysis of *** Email Domain Security Status Using DMARC (Do...

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6th International Conference on computer and informatics engineering, IC2IE 2023

作者： Arnaldy, Defiana Theyser, Regina Patricia Politeknik Negeri Jakarta Dept. of Informatics and Computer Engineering Depok Indonesia

ISBN: (纸本)9798350345162

The rapid development of technology not only provides benefits that facilitate all human activities. However, it also encourages more security loopholes due to a person's lack of awareness in maintaining data security. Phishing emails are one of the fastest growing security crimes and harm both businesses and individuals. This research aims to implement DMARC (Domain-based Message Authentication, Reporting, and Conformance) as an email authentication method that can monitor the security status of all types of messages that enter *** email domains. This research uses email phishing and spoofing as penetration testing techniques to analyze how good that email security method is. DMARC is implemented in three types of scenarios (none, quarantine, and reject) which manage to get different delivery rates and the success rate of DMARC work performance reaches 90% in monitoring and managing all email messages that go to the *** email domain. In the situation without policy enforcement, the DMARC system effectively observed a 100.0% external delivery rate during the phishing email delivery test. In the scenario where a quarantine policy was implemented, the DMARC system successfully monitored the external sending of phishing emails at a rate of 88.5%. Similarly, in the case of the reject policy scenario, DMARC effectively oversaw incoming phishing emails with an external delivery rate of 97.7%. © 2023 IEEE.

关键词： Apilogy.id DMARC Email Security Phishing Spoofing

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Revolutionizing Agricultural Education with Virtual Reality and Gamification: A Novel Approach for Enhancing Knowledge Transfer and Skill Acquisition 1

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

作者： Strousopoulos, Panagiotis Troussas, Christos Papakostas, Christos Krouska, Akrivi Sgouropoulou, Cleo Department of Informatics and Computer Engineering University of West Attica Egaleo12243 Greece

ISBN: (数字)9783031441462

ISBN: (纸本)9783031441455

Agricultural education holds significant importance as it provides comprehensive training in the principles and practices of agriculture, including apiculture, which plays a crucial role in sustainable practices and the cultivation of skilled farmers and apiarists. However, traditional approaches to agricultural education face inherent limitations when it comes to engaging students effectively and facilitating optimal knowledge transfer and skill acquisition. Recognizing this compelling need for innovative methodologies, this research paper introduces a novel approach to apiculture education that harnesses the power of virtual reality (VR) and gamification principles in a captivating application named Beekeeper World. Beekeeper World immerses users in an interactive and engaging VR environment where they assume the role of beekeepers responsible for safeguarding bees from threats, symbolized by spiders. By leveraging digital technologies and gamification techniques, this application aims to cultivate a new generation of passionate and skilled apiarists. This study explores the implications of Beekeeper World for apiculture education and the broader agricultural sector, underscoring the importance of innovative teaching methods in preparing future professionals for the dynamic realm of apiculture and ensuring the long-term sustainability of global food systems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Virtual reality

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Mental Confusion Prediction in E-Learning Contexts with EEG and Machine Learning 3rd

Mental Confusion Prediction in E-Learning Contexts with EE...

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

作者： Trigka, Maria Dritsas, Elias Mylonas, Phivos Department of Informatics and Computer Engineering University of West Attica Athens Greece Department of Electrical and Computer Engineering University of Patras Patras Greece

ISBN: (纸本)9783031440960

In cognitive science, the term confusion is used to capture the decline in learners’ cognitive ability, which affects their ability to think, solve a problem, learn and understand. Unlike classroom education, in online e-learning contexts (e.g., in Massive Open Online Courses (MOOC)), confusion hinders the smooth evolution of the learning process from the learners’ side, as the educator can’t immediately interact with the students to restore cognitive equilibrium. This paper presents a Machine Learning (ML) based approach by comparing several classifiers that were trained and tested exploiting Electroencephalogram (EEG) data (namely, band power, attention and mediation features) acquired by the MindSet device to efficiently distinguish "Confused" from "Not-Confused" subjects. In particular, J48 was the dominant model reaching an optimal performance with accuracy, precision and recall equal to 99.9% and an Area Under the Curve (AUC) of 100%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Electroencephalography

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Fine-Tuning Deep Learning Model for Detecting Indonesian Traffic Accident Tweet 6

Fine-Tuning Deep Learning Model for Detecting Indonesian Tra...

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6th International Conference on computer and informatics engineering, IC2IE 2023

作者： Adi, Rizky Fukumoto, Fumiyo Irnawan, Bassamtiano Renaufalgi Politeknik Negeri Jakarta Department of Computer and Informatics Engineering Depok Indonesia University of Yamanashi Department of Computer Science and Engineering Yamanashi Japan

ISBN: (纸本)9798350345162

Twitter is one of Indonesia's most used social media, with total visits reaching 192 million visits per month, making it the fifth most-visited website in Indonesia. Twitter users exchange information with each other daily. One of them is a traffic accident event. With over 100,000 cases every year from 2017-2021, it is not usual to see a tweet that contains traffic accident information. By implementing text classification, traffic accidents can be detected through tweets and used to send alert messages to emergency responders to respond as quickly as possible. Previous research used a machine learning approach to solve this text classification problem. The study resulted in the best accuracy of 85% from the SVM model, which leaves room for improvement using state-of-the-art deep learning models. This research proposes a transfer learning method using two BERT-based and two RoBERTa-based pre-trained models, namely IndoBERT, IndoBERTweet, Indonesian RoBERTa Base Wiki, and Indonesian RoBERTa Base OSCAR to create the best model to classify tweets that contain traffic accident information. We used two kinds of fine-tuning architecture: the vanilla version fine-tuning architecture and the modified version, which combines the pre-trained model with CNN. This study used 1,002 manually labeled tweets, where 360 were labeled as traffic accident tweets. The experiment found that transfer learning from a pre-trained model Indonesian RoBERTa OSCAR provided the best result with an f1-score of 88.73 % and an accuracy of 92%, which means a 7% accuracy improvement over the previous research's best model. © 2023 IEEE.

关键词： BERT RoBERTa Text Classification Traffic Accident Transfer Learning Tweet

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Eye Disease Classification Based on Fundus Images Using Convolutional Neural Network 25

Eye Disease Classification Based on Fundus Images Using Conv...

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25th International Electronics Symposium, IES 2023

作者： Denandra, Rafi Fariza, Arna Prayogi, Yanuar Risah Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350314731

The eye is a vital organ in the human body. Through the eyes, humans can absorb over 80% of visual information used to carry out various daily activities. However, in Indonesia, there are many cases of vision impairments that, if not properly addressed, can lead to blindness. Examples of such impairments include cataracts, glaucoma, and diabetic retinopathy. Currently, there are many research studies on eye diseases that are aided by technologies like Artificial Intelligence (AI). One of the AI technologies that is commonly developed is Convolutional Neural Network (CNN). CNN is a deep learning framework that excels in solving image or picture classification problems because its architecture applies convolution layers, which can break down images to extract features and easily reduce the high dimensionality without losing the image's information itself. In this study, the author used various CNN models to find the most suitable model for classifying eye diseases, including EfficientNet, ResNet, Inception V3, and many others. Through various model training experiments, the accuracy for the ResNet50 model was found to be 95% with a loss of 17%, followed by Xception with an accuracy of 95% and a loss of 21%, and EfficientNetV2B2 with an accuracy of 92% and a loss of 25%. © 2023 IEEE.

关键词： Convolutional neural networks

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Tackling an Unbalanced Dataset for Classifying Indonesian E-Commerce Reviews Using Multi Word Embedding Model 8

Tackling an Unbalanced Dataset for Classifying Indonesian E-...

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8th International Conference on informatics and Computing, ICIC 2023

作者： Adi, Rizky Irnawan, Bassamtiano Renaufalgi Li, Jiyi Politeknik Negeri Jakarta Department of Computer and Informatics Engineering Depok Indonesia University of Yamanashi Department of Computer Science and Engineering Kofu Japan

ISBN: (纸本)9798350342604

Product reviews are an integral part of ecommerce. By Q2 2020 alone, the average of e-commerce visitors in Indonesia almost reached 400 million. This vast amount of traffic resulting a massive amount of product review information. Product reviews in Indonesia are usually paired with a rating system from one to five based on user satisfaction with the product. Mostly, the number of reviews in each rating is unbalanced. Ratings 5, 4, and 1 are usually more frequent than ratings 2 and 3. This imbalanced data makes classification challenging to categorize the review rating, leading to inaccurate predictions for the rating with fewer data than the others. Previous research used various approaches for classifying review ratings, such as machine learning and fine-tuning a state-of-the-art deep learning model. In this research, we propose a new approach that leverages multiple-word embedding combined with CNN to deal with the highly unbalanced dataset problem. By leveraging multiple-word embedding techniques, the model can extract more features from the review text. We also proposed an extended pipeline to enhance the model performance. The proposed model and pipeline perform better than the fine-tuned Indonesian BERT-based and RoBERTa-based pre-trained models baseline with 0.93 accuracy and 0.82 F1-Macro scores. © 2023 IEEE.

关键词： Embeddings

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Machine Learning Prediction Based UI for Aircraft Cockpit 14

Machine Learning Prediction Based UI for Aircraft Cockpit

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14th International Conference on Electrical and Electronics engineering, ELECO 2023

作者： Topal, Bilge Çarkacioǧlu, Levent Töreyin, Behçet Uǧur Istanbul Technical University Faculty of Computer and Informatics Engineering Dept. of Computer Engineering Istanbul Turkey Aselsan Inc. Ankara Turkey Istanbul Technical University Informatics Institute Istanbul Turkey

ISBN: (纸本)9798350360493

Pilot vehicle interfaces on aircraft, similar to the automobile digital displays found in automobiles, are used to present the current status of various systems (e.g., navigation, engine information). Aircraft interfaces exhibit deterministic behavior due to safety concerns, ensuring that user input yields predictable outputs. Consequently, integrating text-based pattern recognition or artificial neural network based solutions to aircraft systems are challenging because of their deterministic nature. In this paper, an approach complementary to the existing user interface models is proposed. This approach is based on learning recurring user interactions during flight. It utilizes text-based pattern recognition and prediction models based on user interaction logs and sensor readings. Test results on sample data suggest that the proposed approach yields faster and context-aware user interaction model that decreases the time needed to perform tasks in the aircraft cockpit, where timing is crucial. © 2023 IEEE.

关键词： User interfaces

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