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A Web-Based Zakat Recipient Determination System using the Naïve Bayes Algorithm 5

A Web-Based Zakat Recipient Determination System using the N...

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5th IEEE International Conference on computer and Informatics Engineering, IC2IE 2022

作者： Kurniawan, Rahmad Nurkholis Ahmad Nazri, Mohd Zakree Lestari, Fitra Salambue, Roni Sukamto Universitas Riau Faculty of Mathematics and Natural Sciences Department of Computer Science Pekanbaru Indonesia Graduate Programs Universitas Putra Indonesia 'YPTK' Padang Department of Computer Science Padang Indonesia Faculty of Information Science and Technology Selangor Bangi Malaysia Universitas Islam Negeri Sultan Syarif Kasim Riau Department of Industrial Engineering Pekanbaru Indonesia

ISBN: (数字)9781665453387

ISBN: (纸本)9781665453387

As the third pillar of Islam, zakat must be fulfilled by Muslims who have met the requirements of Islamic law. In Indonesia, zakat is managed by by the National Amil Zakat Agency (BAZNAS). BAZNAS is obliged to distribute zakat to eligible people who call in Islamic is Mustahiq to receive zakat according to the applicable category rules. Mustahiq (beneficiaries) are groups entitled to receive zakat according to the priority scale arranged in the Zakat program. As a case study, BAZNAS in Pekanbaru city determines the recipient of zakat manually by interviewing each candidate. Factors influencing decisions, such as feelings, emotions, sentiments, and moods, can lead to wrong decisions. Human weakness contrasts with the advantages of artificial intelligence. Therefore, this study aimed to design and build a web-based application system to determine the category of the recipient of zakat automatically and quickly using the Naïve Bayes algorithm. A total of 3222 data in 2015-2017 were obtained in BAZNAS Pekanbaru for training data. Based on the experimental testing results, the highest accuracy was 93.7%, and F-measure was 96.7% in 80% of training data. Based on the test results, it can be concluded that web-based applications using the Naïve Bayes method has the potential to be used in determining the recipient of zakat in BAZNAS Pekanbaru. © 2022 IEEE.

关键词： Websites

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Challenges and Emerging Issues for Generative AI and Hyper Intelligence

Challenges and Emerging Issues for Generative AI and Hyper I...

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Cyber science and technology Congress (CyberSciTech), IEEE

作者： Jianhua Ma Qun Jin Hui-Huang Hsu John Paul C. Vergara Antonio Guerrieri Claudio Miceli Ao Guo Faculty of Computer and Information Sciences Hosei University Japan Department of Human Informatics and Cognitive Sciences Waseda University Japan Department of Computer Science and Information Engineering Tamkang University Taiwan Department of Information Systems and Computer Science Ateneo de Manila University Philippines ICAR-CNR Italy Graduate Program in Systems Engineering and Computer Science Federal University of Rio de Janeiro Brazil Graduate School of Informatics Nagoya University Japan

ISBN: (数字)9798331532093

ISBN: (纸本)9798331532109

Generative Artificial Intelligence (GenAI) represents a significant milestone in the development of artificial intelligence, bringing sophisticated AI capabilities into daily life and work. As we approach the era of Hyper Intelligence (Hyper-I), a variety of critical challenges and emerging issues have come to light, ranging from computational complexity to ethical concerns. This paper explores the evolution of AI from the perspective of human learning, comparing machine and human intelligence, and identifying key considerations for the development of future AI systems. It also highlights the growing importance of regulating advanced AI models, such as Reinforcement Learning-based Long-Term Planning Agents, to ensure that Hyper-I remains under human control. Additionally, the paper discusses the computational complexity of transformer-based models, their applicability to intractable problems, and their role in cognitive building systems and resource-constrained environments through TinyML. By analyzing these pressing challenges, this work provides insights into the future of AI and the path toward responsible innovation in generative and hyper-intelligent systems.

关键词： Hyper-intelligent systems Ethics Technological innovation Generative AI Computational modeling Tiny machine learning Pressing Transformers Regulation Computational complexity

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Confidentiality. Integrity, and Availability (CIA) Approach for Private Dataset Management System Design

Confidentiality. Integrity, and Availability (CIA) Approach ...

引用

information technology and Digital Applications (ICITDA), IEEE International Conference on

作者： Mahmud Isnan Gregorius Natanael Elwirehardja Dedy Ariansyah Reza Rahutomo Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331510732

ISBN: (纸本)9798331510749

Dataset management systems are essential for assisting research and development (R&D) organizations in applying data governance protocols, especially in managing the utilization of datasets. In R&D, datasets are vital sources for producing analysis, machine learning prediction, and supporting decision-making. As such, it is important to deploy dataset management systems with considerations regarding the aspects of Confidentiality, Integrity, and Availability (CIA) which is a design principle that emphasized information protection and collaboration facilitation among dataset users. The study proposed an analysis and a design for a dataset management system that takes CIA aspects into account. In the future, the proposed design will be utilized as a benchmark for building a dataset management system. In this study, the Waterfall method was adopted. A use case diagram, an ERD (Entity Relationship Diagram), and an activity diagram were constructed in the design stage. In the project implementation stage, the Laravel framework including Laravel Jetstream was considered to be used.

关键词： Protocols Data security Decision making Organizations Machine learning Encryption Protection information technology Research and development System analysis and design

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Voice Enabled Intelligent programming Assistant 4

Voice Enabled Intelligent Programming Assistant

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4th International Conference on Advancements in Computing, ICAC 2022

作者： Wataketiya, Ravindu Chandrasiri, Navinda Kithsiri, Ramesh Malwatta, Hirush Nadeeshani, Madhuka Siriwardana, Samanthi Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

In modern era where software development is of vital importance, software developers are challenged with conditions like Repetitive Strain Injury (RSI) which hinders their ability to work effectively. Furthermore, people with difficulties with using their hands also find it challenging to program in the traditional manner. As a solution, coding with one's voice has been experimented with, but current solutions lack interactivity and are harder to use and setup leaving much room for improvement in this domain. In this research work, by using input classifier models with accuracies over 90%, intent classifiers with accuracies over 70%, code parsing and various human computer interaction techniques, we developed a conversationally interactive, programming language agnostic, easy to setup and easy to use Voice Coding Assistant. This will potentially help a global audience of programmers to achieve their goals and improve productivity and lead a healthier life. We have named the system thus developed, "Venic". © 2022 IEEE.

关键词： Human computer interaction

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Federated Sampling for Privacy-Preserving and Efficient Data Sharing in IoT Networks 5

Federated Sampling for Privacy-Preserving and Efficient Data...

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5th IEEE Middle East and North Africa COMMunications Conference, MENACOMM 2025

作者： Azar, Joseph Tahir, Mohammad Abdullah Bou Abdo, Jacques Demerjian, Jacques Akkari, Nadine Université Marie et Louis Pasteur CNRS Institut FEMTO-ST BelfortF-90000 France University of Cincinnati School of Information Technology CincinnatiOH45221 United States Lebanese University LaRRIS Faculty of Sciences Fanar Lebanon Faculty of Arts and Sciences Jounieh Lebanon Jeddah International College Department of Computer Science and Information Technology Saudi Arabia

ISBN: (纸本)9798331519957

This paper presents a federated sampling strategy aimed at enhancing data efficiency and privacy in IoT networks. By introducing a novel approach to summarization and synthetic data generation, we enable IoT devices to share compact statistical representations of data rather than raw data, significantly reducing the amount of data transmitted while preserving privacy. Our evaluation, using a protocol classification task, demonstrates that synthetic data generated from these summaries effectively replicates the statistical properties of the original data. This proof-of-concept work lays the foundation for future advancements, including network-wide simulations, intrusion detection mechanisms, and federated learning-inspired aggregation techniques, paving the way for resilient and privacy-preserving IoT systems. © 2025 IEEE.

关键词： Data assimilation

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Cardiovascular Disease Analysis Using Correlational Analysis and Association Rules Mining for In-depth Analysis to Identify Predominant Variables

Cardiovascular Disease Analysis Using Correlational Analysis...

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computer science, information technology and Engineering (ICCoSITE), International Conference on

作者： Boby Siswanto Haryono Soeparno Nesti F. Sianipar Widodo Budiharto Computer Science Department BINUS Graduate Program- Computer Science Bina Nusantara University Jakarta Indonesia Food Technology Department Engineering Faculty Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Cardiovascular disease is one of the dangerous non-communicable disorders or diseases that has become one of the causes of death worldwide. Various studies have been conducted to prevent cardiovascular disease in the world. This study analyzed cardiovascular disease medical record data from the Kaggle public dataset by implementing correlational analysis combined with association rule mining to identify variables that are the predominant cause of cardiovascular disease. Correlational analysis can analyze the interrelationships between variables in a dataset, but not in depth. Association rule mining can identify the interrelationships of variables in the form of frequent item sets, which can be calculated for their support and confidence values. The result of this study is a combination of correlation analysis with association rule mining that can identify predominant variables to cause cardiovascular disease. Found that the variable gender=woman, height=short (<165 cm), and age=middle (45-60 years) are more likely to be affected by cardiovascular disease. The variable gender=woman with height=short indicates a 76.07% probability of developing cardiovascular disease.

关键词： computer science Correlation Itemsets Cardiovascular diseases Data mining information technology

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Initial Study of Oil Palm Crop Condition Detection Through Drone Imagery: A YOLO Approach 6

Initial Study of Oil Palm Crop Condition Detection Through D...

引用

6th International Conference on Informatics, Multimedia, Cyber and information System, ICIMCIS 2024

作者： Nugraha, Winata Ermatita Stiawan, Deris Universitas Sriwijaya Faculty of Computer Science Doctoral Program Faculty of Engineering Palembang30139 Indonesia Universitas Sriwijaya Department of Information System Palembang30139 Indonesia Universitas Sriwijaya Faculty of Computer Science Palembang30139 Indonesia

ISBN: (纸本)9798331533243

Oil palm plantations in Indonesia still have many challenges, especially in terms of monitoring and mapping technology. One of the important aspects in the monitoring stage of oil palm plantations is monitoring the productivity of oil palm plants. To identify the oil palm plants, many previous studies have utilized image analysis to solve the problem. The problem is that poor image conditions can affect the architecture model to be built.[1] So in this research we do tune mapping on the image to produce a quality image. Each image data that has been improved will be feature extracted using ResNet and VGG to produce the best model. Furthermore, the classification model development process is carried out using CNN architecture as the model architecture. We also provide innovation by using object detection technology. Which uses artificial neural networks. Here we train the model using aerial video datasets of oil palm plants obtained from drones. Furthermore, to evaluate the performance of the above technique, it is proposed to use the mapping model, precision and accuracy evaluation obtained in the test phase[2] © 2024 IEEE.

关键词： Photomapping

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Machine Learning Algorithms for Natural Language Processing Tasks: A Case of COVID-19 Twitter data (Thailand)

Machine Learning Algorithms for Natural Language Processing ...

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2022 International Conference on Computational science and Computational Intelligence, CSCI 2022

作者： Kularbphettong, Kunyanuth Vichivanives, Rujijan Kanjanaprakarn, Pannawat Chutrtong, Jaruwan Ruksuntorn, Nareenart Suan Sunandha Rajabhat University Computer Science Program Bangkok Thailand Suan Sunandha Rajabhat University Faculty of Science and Technology Bangkok Thailand Suan Sunandha Rajabhat University Robotics Engineering Program Faculty of Industrial Technology Bangkok Thailand

ISBN: (纸本)9798350320282

This paper presents the use of natural language processing for the problem of information extraction and sentiment analysis. The dataset is from Twitter that has the information of people mentioning about COVID-19, this study has two tasks: (i) classification approach for information extraction task and (ii) deep learning approach for sentiment analysis task. In information extraction task, the data was gathered from twitter that related to COVID-19 information, and the sequence labelling method applied to classify text before giving it to classification algorithms (K-NN, Naïve Bayes, Decision Tree, Random Forest, and SVM). In sentiment analysis task, data was classified by convert the word into index and using word embedding, then to process deep learning algorithm (Bi-directional GRU). The accuracy of two tasks are 98% and 79% respectively. © 2022 IEEE.

关键词： COVID-19

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Intelligent Decision Support System for Higher Education Institutions

Intelligent Decision Support System for Higher Education Ins...

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International Conference of Signal Processing and Intelligent Systems (ICSPIS)

作者： Nunik Afriliana Meyliana Ford Lumban Gaol Haryono Soeparno Computer Science Department Binus Graduate Program Computer Science Program Bina Nusantara University Jakarta Indonesia Information System Department School of Information System Bina Nusantara University Jakarta Indonesia

To ensure the sustainability of their operations, higher education institutions have to establish a robust strategic plan. Higher education institutions have an obligation to continuously assess the quality of their educational offerings in every program to comply with the standards established by the government and other educational entities. The implementation of intelligent decision support systems is a feasible approach among other techniques for addressing the aforementioned challenges. An intelligent decision support system has the potential to enable the business to uphold its adherence to regulations, enhance operational effectiveness, and optimize overall performance. This study aimed to determine the current state of intelligent decision support systems through the implementation of a comprehensive literature review. The second goal is to evaluate the best practices, methods, and implementation of intelligent decision support systems within higher education institutions. The findings indicate that intelligent decision support systems have the capacity to serve as a viable option for higher education, particularly in terms of enhancing the accuracy and efficiency of decision-making processes. Intelligent decision support systems exhibit a strong correlation with the fields of data mining and computational intelligence techniques. Nevertheless, the amount of research conducted on this subject is limited.

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information technology Challenges for Integrated Governance, Risk and Compliance (GRC) 1

Information Technology Challenges for Integrated Governance,...

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1st International Conference on Smart technology, Applied Informatics, and Engineering, APICS 2022

作者： Mahendra, Irfan Meyliana Prabowo, Harjanto Hidayanto, Achmad Nizar Bina Nusantara University Computer Science Program Jakarta11480 Indonesia Nusa Mandiri University Technology Information Faculty Jakarta11450 Indonesia Bina Nusantara University Information System Department Jakarta11480 Indonesia Bina Nusantara University Management Departement Jakarta11480 Indonesia Universitas Indonesia Faculty of Computer Science Depok16424 Indonesia

ISBN: (数字)9781665489850

ISBN: (纸本)9781665489850

Integrated GRC is an area of concern for organizations today. Integrated GRC provides benefits for increasing competitiveness and organizational excellence, realizing effectiveness, efficiency, and optimization, as well as supporting the prevention of corruption and fraud. information technology has a very important role in determining the success of implementing an integrated GRC in the organization. However, based on the research conducted, adequate research on the application of information technology in the implementation of integrated GRC is still lacking. In this paper, a study will be conducted on trends and research gaps related to the application of information technology in the current integrated GRC implementation. The conclusion of this study states that there is a great opportunity for wider research on the opportunities and challenges of implementing the latest information technology such as blockchain, artificial intelligence, big data, and cloud computing to support the effectiveness and success of integrated GRC implementation. © 2022 IEEE.

关键词： Crime

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