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All Journal Communications in Science and Technology Devotion: Journal of Research and Community Service COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat
Sutedja, Indrajani
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Humanities Economics, Econometrics & Finance Education Engineering Industrial & Manufacturing Engineering Social Sciences

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AniraBlock: A leap towards dynamic smart contracts in agriculture using blockchain based key-value format framework Saputra, Irwansyah; Arkeman, Yandra; Jaya, Indra; Hermadi, Irman; Akbar, Nur Arifin; Sutedja, Indrajani
Communications in Science and Technology Vol 8 No 2 (2023)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.8.2.2023.1240

Abstract

Blockchain technology offers data transparency and traceability, which is particularly useful in the agricultural sector, especially within the supply chains of commodities like coffee and fish. This sector often encounters issues such as quality degradation, unclear information, and socioeconomic injustice affecting stakeholders. The implementation of Static Smart Contracts (SSCs) on blockchains provides a structured method for executing agreements. However, this approach also has limitations, including a lack of flexibility and responsiveness to dynamic changes in the supply chain. Despite these challenges, blockchain remains a valuable tool for ensuring transaction transparency, traceability, and integrity, which are vital in agriculture. These limitations involve unchangeable parameters, rigid rules, and constraints on adaptability and scalability. This study aims to tackle these issues by designing a more dynamic and responsive smart contract system. We introduce AniraBlock, a revolutionary concept for the agricultural supply chain, particularly in the coffee and fish sectors, by implementing Dynamic Smart Contracts (DSCs) based on a key-value format framework. Unlike SSCs, DSCs offer enhanced adaptability and scalability, addressing the former's limitations. Our study adopts a mixed-method approach, utilizing both qualitative and quantitative data to validate AniraBlock's effectiveness. Preliminary results show significant improvements in data management and supply chain transparency. The proposed framework has the potential to influence the agricultural sector by boosting data integrity and operational efficiency.
Health Center Innovation: Using Ai To Prevent Heart Disease Musawaris, Rubil; Sutedja, Indrajani
COMSERVA : Jurnal Penelitian dan Pengabdian Masyarakat Vol. 5 No. 2 (2025): COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/comserva.v5i2.3235

Abstract

Throughout the world, the disease most people suffer from is heart disease. Likewise with Indonesia, many Indonesian people have heart disease. The cause of the high death rate from heart disease is still a lack of experts who can treat heart disease well. Apart from that, there is also a low level of public awareness to carry out regular checks on the development of their heart health. Because heart disease is still one of the main causes of death throughout the world and Indonesia, this is a topic to increase public awareness and explore the role of artificial intelligence (AI) in the prevention and early detection of heart disease. We explore what AI can do to prevent and cure heart disease, AI works by performing deep machine learning, and neural network learning and how it can be applied to analyze large data sets to identify risk factors, predict outcomes, and support decisions. clinical decisions. The focus is to leverage AI and improve early predictions for the public, personally providing patient care. In this article, readers can learn about the benefits and limitations of AI in the context of heart disease and emphasize the need for high-quality data in order to obtain appropriate analysis results. Finally, this paper suggests that other researchers carry out further research, such as improving the interpretability of AI models, expanding and searching for multiple data sources, and encouraging collaboration between government, society and medical personnel. The potential of using AI can reduce people's risk of heart disease and AI offers a path to better public health outcomes and more efficient health services to treat it and detect it early.
Using Deep Learning and Cbir To Address Copyright Concerns of AI-Generated Art: A Systematic Literature Review Vivaldi, William; Sutedja, Indrajani
Devotion : Journal of Research and Community Service Vol. 5 No. 10 (2024): Devotion: Journal of Community Research
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/devotion.v5i10.18642

Abstract

This systematic literature review explores the intersection of deep learning and content-based image retrieval (CBIR) in addressing copyright concerns related to AI-generated art. As artificial intelligence rapidly transforms various artistic domains, it raises critical questions regarding authorship, ownership, and the ethical implications of machine-generated creativity. The review examines the capabilities of CBIR systems in identifying AI-generated images by analyzing visual features such as color, texture, and shape. Additionally, it highlights the role of deep learning models in enhancing the accuracy of these systems through the detection of distinctive patterns characteristic of AI artworks. The findings underscore the importance of developing robust methodologies that leverage AI and CBIR technologies to protect intellectual property rights while fostering innovation in the creative industries. This research contributes to the broader discourse on the legal and ethical challenges posed by AI in art, providing insights for policymakers, artists, and technologists in navigating the evolving landscape of AI-generated content.
Co-Authors Abdurrahman, Eka Santy Irman Hermadi Kemal N. Siregar Musawaris, Rubil Nasir, Narila Mutia Nur Arifin Akbar Rikawarastuti RR. Ella Evrita Hestiandari Saputra, Irwansyah Vivaldi, William Yandra Arkeman