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检索条件"机构=Informatics and Computer Engineering Education Study Program"

共 452 条记录，以下是61-70 订阅

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An Evaluation of Number of Pixels Change Rate (NPCR) in Symetric Cryptography Based on Data Encryption Standard (DES)

An Evaluation of Number of Pixels Change Rate (NPCR) in Syme...

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

作者： Muhammad Rizky Naufal Christy Atika Sari Eko Hari Rachmawanto Lekso Budi Handoko Folasade Olubusola Isinkaye Wisam Subhi Talib Al-Dayyeni Study Program in Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Department of Computer Science Ekiti State University Ekiti Nigeria School of Information Technologies and Engineering ADA University Baku Azerbaijan

Data Encryption Standard (DES) is a symmetric encryption algorithm that uses a single key to encrypt and decrypt information. In addition, the cipher text will be hidden inside an image using Stepic. DES Encryption use symmetric encryption to encrypt and decryption to decrypt. It will secure the symmetric encryption and maintain the speed of the encryption and decryption. The result of this study is the lowest error that we got as the Mean Square Error (MSE) is 0.00% and is inversely proportional with the Peak Signal to Noise Ratio (PSNR) and Avalanche with 79.36% and 34.91% in order that is not stable for result of Avalanche Effect (AVA). Inversely proportional to the length of the text that is hidden in the image, the longest text that is hidden, the more changes that we get in the image with the highest Unified Average Changing Intensity (UACI) and Number of Pixels Change Rate (NPCR) with the biggest image size with 43.47% for UACI and 99.87% for the NPCR.

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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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The Effect of Vehicles Speed on the Performance of VANET Protocol

The Effect of Vehicles Speed on the Performance of VANET Pro...

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Advanced engineering and Technologies (ICONNIC), International Conference on

作者： Osama A. Qasim Dania Mohammed Mahdi Mohammed S. Noori Rabei Raad Ali Christy Atika Sari Eko Hari Rachmawanto Department of Computer Engineering Technology Northern Technical University Mosul Iraq Department of Electrical and Electronics Engineering Uniersiti Tenaga Nasional Selangor Malaysaia Study Program in Informatics Engineering University of Dian Nuswantoro Semarang Indonesia

At present, ad hoc networks are getting a lot of attention, because they have many features that differ from the rest of the networks and because they are technically advanced. In routing protocols, the Vehicular Ad-hoc network (VANET) differ in performance because each protocol has different variables, such as variable density, speed, and traffic scenarios. In this work, we proposed topology-based routing namely Ad hoc On Demand Distance Vector (AODV) protocol. The AODV protocol has been simulated in case of the movement of vehicles at a fixed velocity at 60km/h, and in case of randomly changing the vehicle's locations in the network. There are three parameters used to investigate the performance of the proposed AODV protocol which are packet delivery ratio, and overheads. The Simulation results show a significant decrease from 0.55 to 0.82 in the packet delivery ratio. On the other hand, the performance of the AODV protocol demonstrates effective communication for the VANETs.

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Development and Validation of Customer Intention Factors for Live-Stream Shopping 7

Development and Validation of Customer Intention Factors for...

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7th International Conference on New Media Studies, CONMEDIA 2023

作者： Fernando, Erick Hidayat, Desman Pengaribuan, Christian Haposan Prabowo, Yulius Denny Universitas Multimedia Nusantara Faculty of Engineering and Informatics Department of Information System Tangerang Indonesia Bina Nusantara University Binus Entrepreneurship Center Management Department Jakarta11480 Indonesia Sampoerna University Management Study Program Faculty of Business Jakarta12780 Indonesia Bina Nusantara University Binus Online Learning Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350307504

Along with technological improvements, online shopping is currently developing quickly. Online shops started by selling electronics, clothing, food, and home appliances and continue to evolve, selling various things. Currently, digital marketing has a novel technique, namely live shopping, where sellers can present, promote and offer their products directly through live streaming on social media and e-commerce platforms. This research will focus on gaining insight into the factors influencing customers to watch and purchase through live streaming. This research is an early-stage study of exploring and searching for research models. The study used a literature review approach by reviewing similar previous research articles. Several variable components were used in this study. The findings of the factors include visibility capacity, meta-vocation capacity, purchase orientation capacity, social presence, price, perceived product quality, perceived enjoyment, purchase orientation, perceived usefulness, and intention to buy. These factors were tested and proved to be valid with Cronbach's Alpha value being more than 0.5 and reliable with the value of the loading factor a greater than 0.6. Criteria The factors of the findings for further analysis of the relationship between factors and other variables. © 2023 IEEE.

关键词： Domestic appliances

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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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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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Citrus Limon L. Ripeness Identification Using Feature Extraction of Mean RGB, HSV, LBP and K-Nearest Neighbor

Citrus Limon L. Ripeness Identification Using Feature Extrac...

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

作者： Ade Cahyo Priyo Utomo Christy Atika Sari Eko Hari Rachmawanto Heru Pramono Hadi Salama A. Mostafa Wisam Subhi Talib Al-Dayyeni Study Program in Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Study Program in Information Systems University of Dian Nuswantoro Semarang Indonesia Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Johor Malaysia School of Information Technologies and Engineering ADA University Baku Azerbaijan

Citrus Limon L. (Lemon) is a type of fruit that is currently widely consumed, because it contains abundant vitamin C, fiber, and antioxidants. This fruit have high potential in the agribusiness sector and is widely cultivated by traditional farmers. However, during harvest time, traditional farmers generally still use manual methods using the human eye in distinguishing the maturity level of lemons which is less efficient because it has a low accuracy. Digital image processing is one solution to this problem. In the research on the classification of lemon maturity levels using digital image processing with the feature extraction method of the Mean RGB, HSV, and LBP methods and the K-Nearest Neighbor classification algorithm in this study, a total of 120 lemon images were used which were divided into 80 training image data and 40 image data. testing. The results of model performance measurements in the form of the highest accuracy level in the Mean RGB method of 100%, the highest accuracy in the HSV method of 98%, and the highest accuracy in the LBP method of 82.5%.

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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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IoT System for Floating Raft Hydroponics: Nutrient Monitoring and Automation 13

IoT System for Floating Raft Hydroponics: Nutrient Monitorin...

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13th International Physics Seminar 2024, IPS 2024

作者： Purwalaksana, A.Z. Wahyu, S. Ndari, W.E.W. Syah, S.A. Rismawati, E. Kusumadjati, A. Suharmanto, P. Department of Physics Universitas Negeri Jakarta Jl. Rawamangun Muka Jakarta13220 Indonesia Department of Physics Education Universitas Negeri Jakarta Jl. Rawamangun Muka Jakarta13220 Indonesia Physics Study Program Indonesia Defense University IPSC Sentul Area West Java16810 Indonesia Department of Industrial Engineering Faculty of Engineering and Computer Science Universitas Indraprasta PGRI Jl. Nangka Raya No.58 C Jakarta12530 Indonesia

The utilization of hydroponic cultivation in agriculture is increasing due to its high productivity even in limited spaces. However, in practice, monitoring and controlling nutrients in hydroponic systems often require intensive human supervision and intervention. This issue can be addressed through technological advancements, particularly with the adoption of microcontrollers, sensors, and the Internet of Things (IoT). Previous research has developed IoT-based monitoring and control systems for DFT hydroponics using the Blynk platform. This study aims to develop an IoT-based nutrient monitoring and automation system for floating raft hydroponics. Floating raft hydroponics was chosen for its simplicity, lower installation costs, and space optimization. The system developed in this study uses the HC-SR04 ultrasonic sensor with an average error of 1.229%, the DS18B20 water temperature sensor with an error of 0.415%, and the TDS sensor with an error of 2.055%. The sensor and actuator measurement data are presented via the Antares Web. This research has the potential to minimize the necessity for manual intervention in hydroponic systems, thereby enhancing both efficiency and consistency in nutrient delivery. © Published under licence by IOP Publishing Ltd.

关键词： Hydroponics

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Deep Learning for Cyber Security: Deephoax Detection 2

Deep Learning for Cyber Security: Deephoax Detection

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2nd International Conference on Advanced Innovations in Smart Cities, ICAISC 2025

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

ISBN: (纸本)9798331506995

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. © 2025 IEEE.

关键词： Cyber attacks

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