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检索条件"机构=Study Program of Computer Science"

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Deep Learning for Cyber Security: Deephoax Detection

Deep Learning for Cyber Security: Deephoax Detection

引用

Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： SY. Yuliani Yakub Rivo Juicer Wowor David Agustriawan Irmawati Dinar Ajeng Kristiyanti P.M. Winarno Rosilah Hassan Roshayu Mohamad Randy Zahran Razzaq Department of Informatics Universitas Multimedia Nusantara Tangerang Indonesia Information System Study Program Universitas Multimedia Nusantara Tangerang Indonesia Center for Cyber Security (CYBER) Universiti Kebangsaan Malaysia Selangor Malaysia College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Department of Informatics UIN Sultan Maulana Hasanuddin Banten Indonesia

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

The increasing prevalence of deep hoaxes, such as fake news and phishing schemes, poses a significant threat to cybersecurity, undermining trust and spreading misinformation. In Indonesia, surveys indicate that more than 60% of people exposed to hoax news believe that it is true, emphasizing the urgent need for robust detection methods. Traditional cybersecurity approaches often struggle to keep pace with the growing scale and sophistication of these attacks. To address this challenge, this research investigates the use of deep learning techniques, specifically focusing on text-based hoax detection in the Indonesian language. The study fine-tunes IndoBERT, a pretrained deep learning model optimized for Indonesian text, to enhance the accuracy and scalability of hoax detection. The IndoBERT model was trained on a balanced dataset of 29,552 articles, comprising both hoax and real news content, collected from the Mafindo API and Kaggle's Indonesia News Dataset. The model was fine-tuned using supervised learning and evaluated using several key metrics, including accuracy, F1-score, precision, and recall. The results demonstrate that IndoBERT outperforms existing state-of-the-art approaches, achieving an accuracy of 98.51%, an F1-score of 98.44%, and a precision of 98.23% on the test set. These results highlight the effectiveness of IndoBERT for hoax detection, which offers a scalable solution to improve cybersecurity defenses against deceptive content. This research contributes to the integration of advanced deep learning models into cybersecurity systems, addressing the evolving landscape of cyber threats.

关键词： Deep learning Measurement Training Surveys Adaptation models Technological innovation Accuracy computer security Fake news Videos

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Testing the Inference Accuracy of Accelerator-Friendly Approximate Phylogeny Tracking

Testing the Inference Accuracy of Accelerator-Friendly Appro...

引用

2025 IEEE Symposium on Computational Intelligence in Artificial Life and Cooperative Intelligent Systems, ALIFE-CIS 2025

作者： Moreno, Matthew Andres Ranjan, Anika Dolson, Emily Zaman, Luis University of Michigan Department of Ecology and Evolutionary Biology Ann Arbor United States Center for the Study of Complex Systems University of Michigan Ann Arbor United States Michigan Institute for Data and Ai in Society University of Michigan Ann Arbor United States Undergraduate Research Opportunities Program University of Michigan Ann Arbor United States Michigan State University Department of Computer Science and Engineering East Lansing United States Program in Ecology Evolution and Behavior Michigan State University East Lansing United States

ISBN: (纸本)9798331508371

computer simulations are an important tool for studying the mechanics of biological evolution. In particular, agent-based approaches provide an opportunity to collect high-quality records of ancestry relationships. Such phylogenies can provide insight into evolutionary dynamics within these simulations. Previous work generally tracks lineages directly, yielding an exact phylogenetic record of evolutionary history. However, challenges exist in scaling direct ancestrytracking approaches to highly-distributed, many-processor evolution in silico. An alternative approach is to estimate phylogenetic history via noncoding annotations on digital genomes, akin to how bioinformaticians build phylogenies by assessing genetic similarities between organisms. Recent work has extended this 'hereditary stratigraphy' approach to support powerful hardware accelerator platforms, such as the Cerebras Wafer-Scale Engine. Although these second-generation 'surface'-based hereditary stratigraphy algorithms have demonstrated order-ofmagnitude speedups over first-generation 'column'-based algorithms, it remains unknown how they impact the accuracy of reconstructed phylogenies. To address this question, we assessed reconstruction accuracy under alternative configurations across a matrix of evolutionary conditions varying in selection pressure, spatial structure, and ecological dynamics. Encouragingly, we find that the second-generation approaches provide higher reconstruction quality across most surveyed conditions. © 2025 IEEE.

关键词： Biostratigraphy

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Testing the Inference Accuracy of Accelerator-Friendly Approximate Phylogeny Tracking

Testing the Inference Accuracy of Accelerator-Friendly Appro...

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Computational Intelligence in Artificial Life and Cooperative Intelligent Systems (ALIFE-CIS), IEEE Symposium on

作者： Matthew Andres Moreno Anika Ranjan Emily Dolson Luis Zaman Department of Ecology and Evolutionary Biology University of Michigan Ann Arbor United States Center for the Study of Complex Systems University of Michigan Ann Arbor United States Michigan Institute for Data and AI in Society University of Michigan Ann Arbor United States Undergraduate Research Opportunities Program University of Michigan Ann Arbor United States Department of Computer Science and Engineering Michigan State University East Lansing United States Program in Ecology Evolution and Behavior Michigan State University East Lansing United States

ISBN: (数字)9798331508371

ISBN: (纸本)9798331508388

computer simulations are an important tool for studying the mechanics of biological evolution. In particular, agent-based approaches provide an opportunity to collect high-quality records of ancestry relationships. Such phylogenies can provide insight into evolutionary dynamics within these simulations. Previous work generally tracks lineages directly, yielding an exact phylogenetic record of evolutionary history. However, challenges exist in scaling direct ancestrytracking approaches to highly-distributed, many-processor evolution in silico. An alternative approach is to estimate phylogenetic history via noncoding annotations on digital genomes, akin to how bioinformaticians build phylogenies by assessing genetic similarities between organisms. Recent work has extended this “hereditary stratigraphy” approach to support powerful hardware accelerator platforms, such as the Cerebras Wafer-Scale Engine. Although these second-generation “surface”-based hereditary stratigraphy algorithms have demonstrated order-ofmagnitude speedups over first-generation “column”-based algorithms, it remains unknown how they impact the accuracy of reconstructed phylogenies. To address this question, we assessed reconstruction accuracy under alternative configurations across a matrix of evolutionary conditions varying in selection pressure, spatial structure, and ecological dynamics. Encouragingly, we find that the second-generation approaches provide higher reconstruction quality across most surveyed conditions.

关键词： Surface reconstruction Accuracy Annotations Heuristic algorithms Computational modeling Biological system modeling Stratigraphy Phylogeny History Bars

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Determination of C4.5 Algorithm Performance Based on Training Data and Testing Data in Determining Diseases in Rice Plants 7

Determination of C4.5 Algorithm Performance Based on Trainin...

引用

7th IEEE International Conference of computer science and Information Technology, ICOSNIKOM 2023

作者： Siregar, Rahmadani Hartono Roslina Zarlis, Muhammad Wanayumini Universitas Potensi Utama Computer Science Postgraduate Study Program Medan Indonesia Politeknik Negeri Medan Computer Science Postgraduate Study Program Medan Indonesia University of Bina Nusantara Computer Science Postgraduate Study Program Jakarta Indonesia

ISBN: (纸本)9798350360752

Disease detection in rice plants at the Padang Lawas District Agriculture Service is carried out every year by collecting data from the Agriculture Service. Therefore, it is necessary to carry out classification to determine the best percentage of training data and test data in determining rice plant diseases and a comparison between testing data and training data for accurate accurate predictions. So data mining algorithms are needed that must be used to classify these diseases. Classification is carried out to group data into categories or groups so that objects from the same data are more similar to data objects from different groups. In this study, disease classification was processed using the C4.5 algorithm using disease data collection on rice plants from 2017 to 2020 which had a total of 248 data. The results of testing data and training data will be compared to see accuracy performance. © 2023 IEEE.

关键词： Agriculture

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New Student Prediction Using Algorithm Naive Bayes and Regression Analysis in Universitas Potensi Utama 4

New Student Prediction Using Algorithm Naive Bayes and Regre...

引用

4th International Conference on Cybernetics and Intelligent System, ICORIS 2022

作者： Aditya, Elsa Situmorang, Zakarias Hayadi, B. Herawan Zarlis, Muhammad Wanayumini Universitas Potensi Utama Computer Science Postgraduate Study Program Medan Indonesia Saint Thomas Catholic University Computer Science Postgraduate Study Program Medan Indonesia University of Sumatera Utara Computer Science Postgraduate Study Program Medan Indonesia

ISBN: (纸本)9781665453950

Universitas Potensi Utama has various study programs, and also has various facilities to support student learning activities. However, the main obstacle in higher education is the uncertainty of the interest of students who register, so that sometimes the facilities are inadequate. To overcome these problems, there must be activities to predict prospective new student applicants to improve facilities and interest of prospective students to choose to study at the Universitas Potensi Utama. Forecasting is the most important thing that must be applied to a company. With this forecasting, companies can see the opportunities that exist to generate sales predictions in the future based on the results of past sales data. Naive Bayes is a classification using probability and statistical methods, the Naive Bayes algorithm can be used in scientific fields, one of which is predicting future opportunities based on previous experience. Linear regression method can be used for forecasting with the assumption that the correlation between variables will continue in the future. Linear Regression is a Regression Method where the resulting equation is linear. Based on the resulting equation, predictions can be calculated by entering the values of the predictor variables in the equation. Based on this process, the predictive value of the response variable can be generated. In this study, the author explains more about how to determine new student predictions using a combination of the two algorithms where the Naïve Bayes algorithm is used to state which study programs have a lot of interest in the coming year, and the Linear Regression algorithm is used to show which study programs are most in demand based on the number of students who registered in the previous year, In the description that has been stated above, the author makes a paper with the title: 'Predictions of New Student Registration Using the Naive Bayes Algorithm and Regression Analysis at the Universitas Potensi Utama'. ©

关键词： Forecasting

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Percentage of Effect of Blended Learning Madel on Learning Outcome 4

Percentage of Effect of Blended Learning Madel on Learning O...

引用

4th International Conference on Informatics and Computing, ICIC 2019

作者： Anggrawan, Anthony Computer Science study program University of Bumigora Mataram Indonesia

ISBN: (纸本)9781728122076

Learning technology provides new strategies in the learning patterns. Learning activities do not depend entirely on face-to-face classes, but could be realized in blended learning. Blended learning is an embodiment of learning that integrates face-to-face and online teaching. Consequently it could be better in accommodating student learning styles. The problem is what percentage of blended learning model influence the student learning outcomes. It is known that research related to the magnitude or percentage of the affect of mixed learning patterns on learning results has not been studied before, so this research becomes relevant research. This research method is experimental research. The benefit of this study is to discover how much the blended learning model contributed to student learning outcome. The result of this study found that the percentage impact of mixed learning model on learning outcome in Algorithm and programming instruction is 34.81%. © 2019 IEEE.

关键词： Students

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Text encryption algorithm based on chaotic map 3

Text encryption algorithm based on chaotic map

引用

3rd International Conference on science, ICOS 2019

作者： Irsan, M.Y.T. Antoro, S.C. Actuarial Science Study Program School of Business Universitas Presiden Cikarang Indonesia Computer Science Study Program Universitas Gunadarma Indonesia

To maintain the confidential data, a scheme or algorithm is needed to protect the data from many attacks. Recently, many cryptography schemes and algorithms have been introduced. One of them is the cryptography algorithm based on chaotic maps. The nature of the chaotic maps gives many advantages to the algorithm. In this paper, an algorithm to encrypt the text is purposed. The algorithm utilizes the nature of the chaotic map by using the MS map as a keystream generator. The ciphertext found by the purposed algorithm is very difficult to be cracked by the bruce-force attack and known-plaintext attack. The decryption process has been tested using 3 different secret keys (that is very similar with the secret key on the encrypted process), and resulting the decrypted text is the same with plaintext only if the input is exactly the same secret key as in encrypted process. © Published under licence by IOP Publishing Ltd.

关键词： Cryptography

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Optimization of artificial neural networks to improve accuracy of vocational competence selection of vocational school students using nguyen-widrow 2

Optimization of artificial neural networks to improve accura...

引用

2nd International Conference on Vocational Education and Technology, IConVET 2019

作者： Christyaditama, I.G.P. Candiasa, I.M. Gunadi, I.G.A. Study Program of Computer Science Postgraduate Program Ganesha University of Education Singaraja Indonesia

The present study used Neural Network Backpropagation combined with Nguyen-Widrow to optimize the disadvantages of ANN causes, which is the difficulty in initializing initial weights. The test was conducted on a dataset of values in semesters 1 and 2. The test results show that the best performance in training model of artificial neural networks with Nguyen-widrow is the smallest average MSE error of 0.002 and the highest average accuracy of 96.38%. Training on artificial neural network model training data with Nguyen-widrow has the smallest MSE error, 0.000996 and the highest accuracy is 97.49% on architecture ANN 9-9.1 with training function parameters: traingdx, epoch: 1000, learning rate: 0.1, and error: 0.001. The best performance was also seen in testing the testing of artificial neural network models with Nguyen-widrow with the smallest average error-MSE of 0.026 and the highest average accuracy of 87.85%. Training data testing on artificial neural network models with Nguyen-widrow has the smallest error-MSE which is 0.004436 and the highest accuracy is 94.50% on architecture ANN 9-9.1 with training function parameters: traingdx, epoch: 1000, learning rate: 0.1, and error: 0.001. The artificial neural network model with Nguyen-widrow has an accuracy difference of 8.53% smaller than the artificial neural network model with an accuracy difference of 9.28%. It can be concluded that the Artificial Neural Network with Nguyen-Widrow can overcome the ANN problem in determining initial weights so that it gives an increase in the accuracy of the prediction of students' competency selection better than the Artificial Neural Network without Nguyen-Widrow. © 2020 IOP Publishing Ltd. All rights reserved.

关键词： Errors

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Publication of the society's sensitive personal data in the legitimate site of the general election committee 2

Publication of the society's sensitive personal data in the ...

引用

2nd International Conference on Vocational Education and Technology, IConVET 2019

作者： Parwata, A. Aryanto, K.Y.E. Divayana, D.G.H. Study Program of Computer Science Postgraduate Program Ganesha University of Education Singaraja Indonesia

It is highly important to protect the society's sensitive personal data. The reason is that many personal data are published by the government institution without paying attention to the prevailing regulations. In this current study, the search for the data in the form of hyperlink with its contents in the legitimate site of the General Election Committees was conducted with the assistance of Crawling Web. The obtained contents were preprocessed using the Preprocessing Text, which were then weighted using the TF-IDF method before they were classified using the Nayve Bayes method. After that, an analysis on the types of the published sensitive personal and the extent of the publication based on the area groups was conducted. Out of 6,700 instances of the personal data which were analyzed, 6.430 were published. The personal data which were published were full name, place and date of birth, religion, marital status, ID Number of the government civil servants, identity card number, number of the tax payer, account number, mobile number, e-mail, address, position, and face photo. The level of publication based on the total data found was as follows: 11.45% in the Central General Election Committee, 21.60% in the eastern area, 17.01% in the central area and 49.94% in the eastern area. The accuracy of the Nave Bayes method averaged 96.99%. Prior to publication, the General Election Committee is recommended to respect someone's personal data as privacy and the data which would be published should obtain approval and an easily-contacted contact person. © 2020 IOP Publishing Ltd. All rights reserved.

关键词： Hypertext systems

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Analysis of jejak bali virtual class using usability testing, including concurrent think aloud techniques and performance measurement techniques 3

Analysis of jejak bali virtual class using usability testing...

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3rd International Conference on Vocational Education and Technology, IConVET 2020

作者： Jaya, M.P. Dantes, G.R. Candiasa, M. Computer Science Study Program Ganesha University of Education Singaraja Indonesia

Jejak Bali is a website created specifically by PUSTEKKOM to manage learning activities so Bali Provincial Education Office can carry out supervision and guidance for e-learning in Bali. Many obstacles were experienced by teachers and students when using Jejak Bali. It encouraged the design of a study looking at the usability of a system from a user perspective to increase user satisfaction. The methods used were usability testing technique, Concurrent Think Aloud, and Performance Measurement which measured the effectiveness, efficiency, and user satisfaction. The population of this study were teachers and students from SMAN 2 and SMKN 3 Denpasar. In measuring Performance Measurement and CTA, the sample used consisted of 40 participants including teachers and students as well as 86 users who used the questionnaire SUS. The results of the research were: (1) Jejak Bali, for teacher and student users, was still ineffective because there were still errors made while doing the assigned assignments, (2) Jejak Bali which was seen from the time when teacher and student users were working was still inefficient because of the differences of the significant time from the advanced group to the beginners, (3) Jejak Bali still did not meet user satisfaction as seen from the results of the SUS questionnaire recapitulation in which a teacher scored 49.0 and a student scored 48.3. Because the value obtained was below 60 in the percentile rank, so it can be concluded that Jejak Bali still did not meet usability good as seen from the results obtained. © Published under licence by IOP Publishing Ltd.

关键词： Students

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