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检索条件"机构=Informatics Engineering Study Program Department of Mathematics"

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Early Detection and Classification of Melanoma Based on Android MobileNet V2 Convolutional Neural Network

Early Detection and Classification of Melanoma Based on Andr...

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International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Jasmine Iswarini Dyah Prabandari Christy Atika Sari Eko Hari Rachmawanto Candra Irawan Bilal R. Altamer Mohamed Ayad Alkhafaji Study Program in Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Study Program in Information Systems University of Dian Nuswantoro Semarang Indonesia Mosul University Presidency University of Mosul Mosul Iraq Department of Computer Science National University of Science and Technology DhiQar Iraq

Melanoma is a malignant form of cancer that affects the skin and has a particularly high mortality rate, so it requires early detection to increase the level of safety for users. Diagnosis and detection of skin cancer are usually done through manual screening and visual inspection. This process requires a long time, has high complexity, is subjective, and is prone to errors. CNN is one of the algorithms with advantages in accurate classification. In this research, early detection and classification of melanoma cancer were carried out based on two classes, namely benign and malignant using the Convolutional Neural Network method. Our proposed method yields an accuracy of 81.11% for the validation data. The accuracy results obtained can be improved by using more datasets and increasing the number of layers used. This study uses the CNN method using MobileNet V2 architecture to detect melanoma skin cancer. The class used is benign and malignant.

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A Comparison of Multi Class Support Vector Machine vs Deep Convolutional Neural Network for Brain Tumor Classification

A Comparison of Multi Class Support Vector Machine vs Deep C...

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International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Nur Ryan Dwi Cahyo Christy Atika Sari Eko Hari Rachmawanto Cahaya Jatmoko Rabei Raad Ali Al-Jawry Mohamed Ayad Alkhafaji Department of Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Study Program in Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Department Computer Science Northern Technical University Semarang Indonesia Department of Computer Engineering Technology Northern Technical University Mosul Iraq Department of Computer Sciece National University of Science and Technology DhiQar Iraq

Multi SVM has long been one of the popular methods in classification, while DCNN has recently gained significant attention in image processing and pattern recognition. This research evaluates the effectiveness of Multi Class Support Vector Machine (M-SVM) and Deep Convolutional Neural Network (DCNN) techniques in classifying brain tumors. A dataset of 2660 3D medical images with dimensions 227 x 227 x 3; including Glioma, Meningioma, and Pituitary tumors, has been partitioned into distinct sets for both training and testing purposes. DCNN approach achieves excellent accuracy in identifying tumor names, with a training accuracy of 97.8% and 100% success rate in 9 experiments. The Multi SVM method demonstrates relatively good accuracy, with training accuracies ranging from 70% to 90% based on different kernel functions. These findings provide valuable insights for selecting appropriate methods in brain tumor classification and encourage further exploration of hybrid Multi SVM-DCNN approaches to enhance accuracy and reliability.

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Intrusion Detection using Deep Neural Network Algorithm on the Internet of Things 11

Intrusion Detection using Deep Neural Network Algorithm on t...

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11th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2022

作者： Ikhwan, Syariful Wibowo, Adi Warsito, Budi School of Postgraduate Studies Diponegoro University Doctoral Program of Information System Semarang Indonesia Institut Teknologi Telkom Purwokerto D3 Telecommunication Engineering Banyumas Indonesia Diponegoro University Computer Science Informatics Department of Science and Mathematics Faculty Semarang Indonesia Diponegoro University Statistics Department of Science and Mathematics Faculty Semarang Indonesia

ISBN: (纸本)9781665460309

The increasing use of IoT devices on future networks is very helpful for humans in their lives. However, the increase in devices connected to IoT networks also increases the potential for attacks against those networks. Vulnerabilities in Internet of Things (IoT) networks can be exposed at any time. Artificial intelligence can be used to protect the IoT network by being able to detect attacks on the network so that they can be prevented. In this study, network detection was carried out using the Deep Neural Network (DNN) algorithm. The test was carried out using the UNSW Bot-IoT dataset with a comparison of training data of 75% of the overall data. The results obtained show the ability of the algorithm to detect attacks on average with 99.999% accuracy. The validation loss and training loss look very small. In this study, there is a validation loss that still occurs in overfitting, but the difference is very small. © 2022 IEEE.

关键词： Internet of things

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The effect of lard concentration and ozonation on changes in polarization angle of olive oil 10

The effect of lard concentration and ozonation on changes in...

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10th International Seminar on New Paradigm and Innovation of Natural Sciences and itsApplication, ISNPINSA 2020

作者： Saputra, F.A. Azam, M. Arianto, F. Sofjan, K.F. Sugito, H. Alkian, I. Department of Physics Faculty of Science and Mathematics Diponegoro University Semarang50275 Indonesia Study Program of Automation Engineering Technology Vocational School Diponegoro University Indonesia

This research aims to analyze the effect of ozone exposure time and lard mixture on the polarization angle of transmission of olive oil. The study uses a transmission polarization method with a laser (λ = 532 nm) as a light source. The research sample in the form of olive oil mixed with lard at a concentration of 10, 20, 40, and 80%, with an ozone exposure time of 0-30 minutes. The sample is subjected to external electric fields with variations from 0-4.5 x 102 kV/m to increase the change's polarization angle. The results showed that the change in polarization angle would change linearly with the increase in lard concentration with olive oil. Olive oil with a low concentration of lard (10%) experienced an increase in the change in polarization angle with a value of 0.31 due to the effect of 90 minutes of ozone exposure. In general, the results of the study showed that increased concentrations of lard, ozone exposure time, electric field strengths caused an increase in changes in the polarization angle in a mixture of olive oil and lard. © Published under licence by IOP Publishing Ltd.

关键词： Mixtures

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Digital Forensics Investigation and Incident Response in Internet of Things (IoT-DFIR): Challenges and Models 9

Digital Forensics Investigation and Incident Response in Int...

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9th International Conference on Wireless and Telematics, ICWT 2023

作者： Ahdan, Syaiful Zaki Hamidi, Eki Ahmad Riskiono, Sampurna Dadi Negara, Ridha Muldina Mayasari, Ratna Ramadona, Suci Nurhayati, Ade Sucipto, Adi Jupriyadi Nurkahfi, Galih Nugraha Farahiyah, Dzata Naseer, Muchammad Universitas Teknokrat Indonesia Faculty of Engineering & Computer Science Lampung35132 Indonesia Uin Sunan Gunung Djati Bandung Electrical Engineering Department Bandung40614 Indonesia School of Electrical Engineering Telkom University Bandung40257 Indonesia Elearical Engineering Study Program Politeknik Caltex Riau Riau28265 Indonesia Institut Teknologi Telkom Jakarta Faculty of Engineering Jakarta11710 Indonesia Bandung40135 Indonesia Universitas Islam Indonesia Yogyakarta Faculty of Industrial Technology Yogyakarta55584 Indonesia Sekolah Tinggi Teknologi Bandung Informatics Department Bandung40235 Indonesia

ISBN: (纸本)9798350305029

Many different industries are currently making substantial use of the Internet of Things (IoT). IoT is the process through which electronic devices communicate with their surrounding virtual environment by continuously exchanging data via sensors. Due to increased IoT security concerns, digital forensic investigators now possess greater knowledge and abilities to investigate IoT devices. IoT-DigFor are required in terms of cybercrime due to the rapid growth in the number of electronic devices and the collection and consumption of enormous amounts of data. IoT systems with billions of devices have produced a significant amount of evidence, which presents a significant challenge to digital investigators and practitioners who must connect with IoT devices to conduct fast and thorough investigations into cybercrimes. This article presents detailed information about forensics in the IoT environment. The IoT forensic paradigm and the challenges that design-based security requirements and IoT system security offer for IoT-DigFor are covered in great detail at the beginning of the article. © 2023 IEEE.

关键词： Internet of things

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Machine Learning-based Modulation Format Identification and Optical Performance Monitoring Techniques Implementation

Machine Learning-based Modulation Format Identification and ...

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2021 Advanced Communication Technologies and Signal Processing, ACTS 2021

作者： Devi, Pukhrambam Puspa Vincent Simatupang, Joni Welman National Institute of Technology Silchar Department of Electronics and Communication Engineering Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung40132 Indonesia President University Study Program of Electrical Engineering Bekasi17530 Indonesia

ISBN: (纸本)9781665423373

In this paper, machine learning-based techniques are used to solve and analyze the modulation format recognition problem. The combination of intelligent software and high-performance hardware provides a large scope for innovation in optical networking. Machine learning algorithms can use a large amount of data available from the network monitors to learn and make the network more robust. This is a problem in optical communication that consists of defining the type of digital modulation process in which an electrical signal should be sent. A dataset to represent realistic transmission behaviors was generated using a simulator based on a Gaussian noise model. A multi-layer perceptron was used and tested with different architectures to show that a high level of accuracy is achievable with machine learning. An analysis of the input features was made by using the select K best features method. Finally, an attempt to visualize the data in 2-dimension was made using the Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) methods to reduce the dimensionality of the input features and see their relationships. © 2021 IEEE.

关键词： Principal component analysis

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Deep Learning for Cyber Security: Deephoax Detection

Deep Learning for Cyber Security: Deephoax Detection

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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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Multiplayer Ludo Game Application Based on Mixed Reality Using UrhoSharp and RabbitMQ Message Broker

Multiplayer Ludo Game Application Based on Mixed Reality Usi...

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International Conference on Wireless and Telematics (ICWT)

作者： Agus Sukoco Aghil Alfhauzhi Apriliansyah Rin Rin Nurmalasari Vitradisa Pratama Ary Setijadi Prihatmanto Agus Juhana School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Department of Electrical Engineering UIN Sunan Gunung Djati Bandung Indonesia Multimedia Education Study Program Universitas Pendidikan Indonesia Bandung Indonesia

This paper describes the design and implementation of the Ludo game on Microsoft Hololens to create a mixed reality-based application with Hololens technology connected to many services. The Ludo game is made for two players, with player one playing on Hololens and player two sending and receiving player one's data via the console. The Ludo game with Hololens is one of the results of the development of Hololens technology and the RabbitMQ Messaging Server. The Ludo game is made on Visual Studio software using UrhoSharp Framework and RabbitMQ Messaging Server service with the AMQP protocol. As application frameworks, Hololens and UrhoSharp function as devices for visualization and running games. At the same time, the RabbitMQ Messaging Server and AMQP protocol serve as services that connect two players from the Hololens and console by sending the data needed by the application. The test results show that Hololens is running successfully and communicating with other console players. Thus, the results obtained are the purpose of this paper, which is to create a mixed reality-based application with Hololens technology connected to many services.

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Implementation of AI-Based Support Vector Machine Algorithm for Fraud Detection in Digital Payment Systems

Implementation of AI-Based Support Vector Machine Algorithm ...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Edy Prayitno Rikie Kartadie Uning Lestari Munengsih Sari Bunga Accounting Information Systems Faculty of Information Technology Universitas Teknologi Digital Indonesia Yogyakarta Indonesia Computer Engineering Study Program Faculty of Information Technology Universitas Teknologi Digital Indonesia Yogyakarta Indonesia Informatics Study Program Faculty of Science and Information Technology Universitas Akprind Indonesia Yogyakarta Indonesia Department of Computer Engineering Smart City Information Systems Politeknik Negeri Indramayu Indramayu Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

The increasing use of digital payment systems has led to a rise in fraudulent activities, presenting a significant challenge in ensuring secure transactions. This research focuses on implementing the Support Vector Machine (SVM) algorithm with a Radial Basis Function (RBF) kernel to detect fraud in digital payment systems. One of the main challenges addressed in this study is the severe class imbalance in the dataset, where fraudulent transactions account for only 0.17% of total transactions. To overcome this, the SMOTE (Synthetic Minority Over-sampling Technique) method was applied to balance the dataset, allowing the model to better recognize fraudulent patterns. The results indicate that the SVM model achieved an accuracy of 99.93%, with a precision of 86.23% and a recall of 75.51%. These results demonstrate that SVM, combined with SMOTE and RBF kernel, is highly effective in detecting fraudulent transactions while minimizing false positives. This research provides a strong foundation for improving fraud detection models in the context of digital payment systems, offering enhanced security and trust for users. Further research could explore hybrid models and real-time data analysis to improve performance.

关键词： Support vector machines Seminars Adaptation models Accuracy Data models Real-time systems Fraud Security Kernel Intelligent systems

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ML-Agents in Kart Racing Game using Microgame

ML-Agents in Kart Racing Game using Microgame

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International Conference on Telecommunication Systems, Services, and Applications (TSSA)

作者： Afina Anfa Ana Vitradisa Pratama Agus Sukoco Ary Setijadi Prihatmanto Rin Rin Nurmalasari Agus Juhana School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Departement of Informatics Universitas Mayasari Bakti Tasikmalaya Indonesia Department of Electrical Engineering UIN Sunan Gunung Djati Bandung Indonesia Multimedia Education Study Program Universitas Pendidikan Indonesia Bandung Indonesia

This paper presents an innovative approach to integrating ML-Agents into a microgame-based kart racing environment, showcasing the potential for enhancing gameplay experiences through AI-driven opponents. Kart Unity Game is a racing game that uses small motorized vehicles but still uses the concept of racing in the real world. One of the racing game development platforms is Unity. Unity has developed a platform, Unity Karting Microgame, which users can modify. Unity Karting Microgame applies self-driving to ML-Agents trained and designed to drive their karts with suitable sensors and actuators. The method used to develop the game is to modify the racetrack and modify ML-Agents to make the game more exciting and fun. As a result of this study, successful implementation of ML-Agents in a microgame-based kart racing environment. The study highlights the potential of using Unity ML-Agents to create AI-driven opponents that enrich gameplay experiences. Games can run well and seem varied. ML-Agents can also control their karts on their lanes. Thus, kart racing games can be implemented.

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