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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Eben, J. Lysa Jayasudha, R. Ramya, S. Kaliappan, S. Aswal, Shobha Al-Salehi, Khalid Ali Salem Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Tamilnadu Chennai India Department of Mathematics Dr.N.G.P.Institute of Technology Coimbatore India Department of Computer Science and Engineering Kongu Engineering College Tamilnadu Erode India Division of Research and Development Lovely Professional University Punjab Phagwara144411 India School of Computing Graphic Era Hill University Dehradun India Biomedical Engineering Study Program Faculty of Science and Technology Airlangga University Surabaya Indonesia

ISBN: (纸本)9798350398496

Diabetes mellitus is one of the most pressing health concerns because so many people are afflicted by its disabling symptoms. Factors such as age, excess body fat, insufficient physical activity, a history of diabetes in one's family, a sedentary lifestyle, an unhealthy diet, hypertension, etc., all increase the likelihood of developing diabetes mellitus. Health complications are more common in people with diabetes, including cardiovascular disease, renal failure, stroke, blindness, and nerve injury. To validate a diagnosis of diabetes, hospitals typically perform a battery of procedures on the patient. Big data analytics has many vital applications in the healthcare sector. Numerous large computer systems are used in the healthcare sector. With the help of big data analytics, researchers can sift through mountains of data in search of previously unseen patterns and insights. Current techniques have a poor degree of precision in classification and forecast. While previous research has focused on factors such as glucose, body mass index, age, insulin, etc., the proposed model takes these into account and also the other factors that may be more relevant to the development of diabetes. The newer sample is superior to the older one based on categorization accuracy. A workflow algorithm for diabetes prognosis is also required to improve the accuracy. © 2023 IEEE.

关键词： Forecasting

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Machine Learning Stroke Prediction in Smart Healthcare:Integrating Fuzzy K-Nearest Neighbor and Artificial Neural Networks with Feature Selection Techniques

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computers, Materials & Continua 2025年第3期82卷 5115-5134页

作者： Abdul Ahad Ira Puspitasari Jiangbin Zheng Shamsher Ullah Farhan Ullah Sheikh Tahir Bakhsh Ivan Miguel Pires Department Information System Study Program Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Software Northwestern Polytechnical UniversityXi’an710072China Research Center for Quantum Engineering Design Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Computer Science and Software Engineering Shenzhen UniversityShenzhen518061China Cybersecurity Center Prince Mohammad Bin Fahd University617Al JawharahKhobarDhahran34754Saudi Arabia Cardiff School of Technologies Cardiff Metropolitan UniversityWestern AvenueCardiffCF52YBUK Instituto de Telecomunicacoes Escola Superior de Tecnologia e Gestao de AguedaUniversidade de Aveiroágueda3750-127Portugal

This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both *** BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke *** findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.

关键词： Fuzzy K-nearest neighbor artificial neural network accuracy precision recall F-measure Chi-Square best search first heart stroke

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Smart Campus Framework: A Solution for New Normal Education System 5

Smart Campus Framework: A Solution for New Normal Education ...

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5th IEEE International Conference on Information technology, Information Systems and Electrical Engineering, ICITISEE 2021

作者： Wahid, Abdul Luhriyani, Seny Nurhikmah Parenreng, Jumadi Mabe Fajar B, Muhammad Nur, Muh. Irfan Universitas Negeri Makassar Computer Engineering Study Program Makassar Indonesia English Department Universitas Negeri Makassar Makassar Indonesia Education Technology Study Program Universitas Negeri Makassar Makassar Indonesia ICT Center Universitas Negeri Makassar Makassar Indonesia

ISBN: (纸本)9781665401968

The Covid-19 pandemic has affected all aspects of human life and has even forced humans to shift their life habits, including in the world of education. The learning model must shift from the traditional to modern synchronous or asynchronous models with information technology-based applications. This condition is known as the New Normal era in the education system. Y Generation and Z Generation as educational objects are now very close to technology, especially information technology, including in the teaching and learning process, so there is no need for extra effort in this regard. Therefore, it is essential to create a learning environment under the characteristics of this generation and can also support the new normal era of the education system. We offer the concept of a smart campus framework that integrates an IT-based learning system and a hybrid smart classroom system as a modern education method in an educational scenario that promises to accelerate quality education without leaving a good learning process. This article designs the concept of a smart campus framework that will accommodate a learning model that will accelerate the improvement of the quality of education through smart learning solutions and smart blended learning systems. We also offer achievements in improving service performance through smart academic services solutions and accommodating the management of existing classroom resources through smart equipment system solutions. This framework is designed to automatically solve all university-level education system problems based on multimedia and integrated with websites and information systems used on campus. © 2021 IEEE.

关键词： Learning systems

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E-Learning Satisfaction: Analysis of the Support Factors

E-Learning Satisfaction: Analysis of the Support Factors

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International Conference on Information technology, computer and Electrical Engineering (ICITACEE)

作者： Boby Siswanto Doni Purnama Alamsyah Doni Morika Norfaridatul Akmaliah Othman Billiam Christofer Wijaya Putri Giyan Adinda Computer Science Study Program School of Computer Science Bina Nusantara University Jakarta Indonesia Entrepreneurship Department BINUS Business School Bina Nusantara University Jakarta Indonesia Interior Design Study Program School of Design Bina Nusantara University Jakarta Indonesia Department of Management Technology Universiti Teknikal Malaysia Melaka Melaka Malaysia

E-learning satisfaction is an important factor in assessing the effectiveness and efficiency of distance learning. The use of Big Data technology can have a significant impact on e-learning satisfaction. The purpose of this research is to examine the implementation of big data in e-learning and the factors that can increase student e-learning satisfaction. This study uses a quantitative survey method with a questionnaire as a data collection tool. The research respondents were randomly selected e-learning students from several educational institutions that use Big Data technology in their e-learning platforms. There are 663 data collected through online questionnaires, then the data is processed through SmartPLS to test the research hypothesis. The results of the study show that the use of Big Data technology in an e-learning environment has a positive impact on student satisfaction. Several factors determine e-learning satisfaction including usability requirements, quality level, learning competence, and material relatedness. Relatedness to e-learning is more important in increasing student motivation in learning, as can be seen from the correlation value which is more dominant in forming student satisfaction. The e-learning platform must have navigation that is easy for users to understand and use. Learning content must be well-designed and follow established learning standards. The use of Big Data technology in e-learning can have a significant impact on student satisfaction and the quality of learning. Future research can develop a more personal and adaptive e-learning platform, using Big Data technology as the basis for its development.

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Online Handwritten Recognition For Alphabet Writing Practice

Online Handwritten Recognition For Alphabet Writing Practice

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Ni Putu Linda Santiari I Gede Surya Rahayuda Information System Study Program Faculty of Informatics and Computer Institute of Technology and Business STIKOM Bali Denpasar Indonesia Informatics Study Program Faculty of Math and Natural Sciences Udayana University Mangupura Indonesia

ISBN: (纸本)9781665453967

Learning to write in children requires the child's habit of using a pencil to write on paper. The manual method has drawbacks such as the use of a lot of paper when children have to study harder to keep repeating the exercises and also cannot be done online remotely. In addition to this, writing exercises on the touchscreen can also train children to be more skilled at using the touchscreen. To overcome these problems, the author will discuss the use of handwriting recognition technology to practice writing capital letters (A-H), where the technology will be built on a web-based basis. The recognition algorithm used is K-NN. Testing the use of the website was carried out at Tunas Mekar Sari Kindergarten, Denpasar. The identification number and result will be stored on the web server. Based on the experiment, it was found that from 80 image data, the K-NN algorithm succeeded in providing accurate recognition results on 60 image data, and obtained an accuracy of 78.75%.

关键词： Handwriting recognition Image recognition Manuals Writing Touch sensitive screens Animation Web servers

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Performance LoRa technology for Autonomous Vehicles 24

Performance LoRa Technology for Autonomous Vehicles

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24th International Seminar on Intelligent technology and Its Applications, ISITIA 2023

作者： Adi, Puput Dani Prasetyo Purnama, Iwan Siregar, Arfanda Anugrah Juledi, Angga Putra Edi, Firman Karim, Abdul Wahyu, Yuyu Maulana, Fairuz Iqbal Susilo, Suhardi Atmoko Budi Toni Harahap, Ilham Maliki Patria, Lintang Bandung Indonesia Labuhanbatu University Faculty of Science and Technology North Sumatra Labuhanbatu Indonesia Politeknik Negeri Medan Telecommunication Engineering Study Program North Sumatra Medan City Indonesia Batam Institute of Technology Faculty of Industrial Technology Department of Engineering Management Riau Islands Batam City Indonesia Budi Darma University Department of Information Technology North Sumatra Medan Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia University Labuhanbatu Faculty of Teacher Training and Education North Sumatra Labuhanbatu Indonesia Universitas Terbuka Faculty of Engineering Jakarta Indonesia

ISBN: (纸本)9798350313956

This research is an approach to intelligent vehicles with a LoRa communication system, LoRaWAN compatible for Long-Range and Outdoor Communication, but in this paper, we will test the ability of LoRa to handle autonomous vehicles, a Blind-Adaptive Data method will be tried on vehicles systems. The system offered is when the vehicle is running, the sensor will send position data in real-time, the number of vehicles is four, and GPS data is sent alternately using the Blind Adaptive Data Rate principle to avoid data collisions, and when the GPS shows a certain position, the value of SF is ascertained, which affects the data rate and ToA, and the output given is RSSI on each car, speed of a car, power consumption of sensor nodes, signal power, SNR, Bit rate or data rate, and sensitivity. With the Blind ADR principle, it is ensured that there is no data collision between car position data that causes packet loss. © 2023 IEEE.

关键词： Autonomous vehicles

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Inappropriate Benefits and Identification of ChatGPT Misuse in programming Tests: A Controlled Experiment

arXiv

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arXiv 2023年

作者： Toba, Hapnes Karnalim, Oscar Johan, Meliana Christianti Tada, Terutoshi Djajalaksana, Yenni Merlin Vivaldy, Tristan Master of Computer Science Study Program Faculty of Information Technology Maranatha Christian University Bandung Indonesia Bachelor of Informatics Study Program Faculty of Information Technology Maranatha Christian University Bandung Indonesia Media Culture Department Faculty of Information Sciences and Arts Toyo University Kawagoe Japan EC-Council TampaFL United States

While ChatGPT may help students to learn to program, it can be misused to do plagiarism, a breach of academic integrity. Students can ask ChatGPT to complete a programming task, generating a solution from other people’s work without proper acknowledgment of the source(s). To help address this new kind of plagiarism, we performed a controlled experiment measuring the inappropriate benefits of using ChatGPT in terms of completion time and programming performance. We also reported how to manually identify programs aided with ChatGPT (via student behavior while using ChatGPT) and student perspective of ChatGPT (via a survey). Seventeen students participated in the experiment. They were asked to complete two programming tests. They were divided into two groups per the test: one group should complete the test without help while the other group should complete it with ChatGPT. Our study shows that students with ChatGPT complete programming tests two times faster than those without ChatGPT, though their programming performance is comparable. The generated code is highly efficient and uses complex data structures like lists and dictionaries. Based on the survey results, ChatGPT is recommended to be used as an assistant to complete programming tasks and other general assignments. ChatGPT will be beneficial as a reference as other search engines do. Logical and critical thinking are needed to validate the result presented by ChatGPT. © 2023, CC BY.

关键词： Students

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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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Salt Hash Password Using MD5 Combination for Dictionary Attack Protection

Salt Hash Password Using MD5 Combination for Dictionary Atta...

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International Conference of computer and Informatics Engineering (IC2IE)

作者： Agung Nugroho Teddy Mantoro Cyber Defense Engineering Study Program Sains and Defense Technology Faculty The Republic of Indonesia Defense University Bogor Indonesia Departement of Computer Science Media-Tech Lab Sampoerna University Jakarta Indonesia

Using MD5 hashing of passwords in web logins is highly vulnerable to dictionary attacks. One online application for cracking hashed passwords using a dictionary attack that is free and easy to use is ***. Even so, the use of hashing passwords using MD5 which has a low level of security is still commonly found to secure web login passwords. This research presents an increase in security through a combination of salt hash passwords when using MD5. The methodology of this study uses a quantitative experimental method to apply hash salt passwords using the MD5 combination. The password cracking test was carried out using the *** online hash cracking application on weak passwords such as “admin” with the addition of a password hash salt using the MD5 combination. The results showed that Salt Password using the MD5 combination could not be cracked by *** using a dictionary attack.

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Smart Home Security System Using Face Recognition Based on IoT- CNN

Smart Home Security System Using Face Recognition Based on I...

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Information technology Research and Innovation (ICITRI), International Conference on

作者： M. Aldiki Febriantono Alvin Zuhair Khaeruddin Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Electrical Engineering Department University of Islam Blitar Blitar Indonesia Diploma III Electronic Technology Vocational Study Program University of Muhammadiyah Malang Malang Indonesia

Home security is a crucial aspect that requires careful attention, particularly when it comes to addressing theft concerns. Hence, implementing smart door technology equipped with facial recognition holds promising potential for enhancing home security. This study aims to develop a more secure and regulated home entry system by leveraging Internet of Things (IoT) technology and Machine Learning computer Vision for facial recognition. The system integrates IoT devices, such as cameras and automatic doors, wherein facial image data is captured by the camera and processed using the Convolutional Neural Network (CNN) algorithm to identify individuals. Once an individual is recognized, the system grants access to the home through an automated door. By relying on facial features, the system effectively restricts unauthorized access and safeguards homes against theft risks. Therefore, the advancement of a safer and more controlled home entry system utilizing IoT technology and Machine Learning computer Vision holds tremendous benefits for homeowners.

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