Enhancing Decentralized Science with Dynamic Smart Contracts: A Blockchain-Based Reputation and Incentive System Ummu Radiyah, Irwansyah Saputra, Heru Triana, Arfhan Prasetyo, Fitra Septia Nugraha, Maruloh, Muhammad Faisal, Juarni Siregar, Indrajani Sutedja Proceeding 2025 International Conference on Information Technology Research and Innovation Harnessing Intelligent Machines for Sustainable Development Aligning AI with the Sdgs Icitri 2025, 2025 Blockchain has been increasingly adopted in various sectors to support transparent and tamper-proof data management. In the academic world, however, reputation systems remain centralized and often fail to represent the true contributions of researchers. Previous innovations such as Dynamic Smart Contracts (DSC) have enabled more flexible interaction models on blockchain, allowing logic and reward schemes to be updated without redeploying contracts. Building on this foundation, this paper introduces a blockchain-based reputation and incentive system tailored for the Decentralized Science (DeSci) ecosystem. The system records academic contributions such as publications, peer reviews, and experimental data into smart contracts that dynamically compute and update reputation scores. Each interaction is validated and permanently stored on-chain, enabling traceable, contribution-based recognition independent of centralized academic institutions. A series of tests conducted on the Ethereum testnet demonstrate that the system operates reliably, supports dynamic rule updates, and effectively tracks contribution-based reputation. This approach enhances transparency and fairness in scientific evaluation, strengthens community-driven validation, and supports the broader vision of DeSci by aligning incentives with openness, accountability, and verifiability.
From Traditional to Innovation: Large Language Models in Fisheries Data Extraction Fitra Septia Nugraha, Irwansyah Saputra, Heru Triana, Mochamad Wahyudi, Ummu Radiyah, Arfhan Prasetyo, Muhammad Faisal, Juarni Siregar, Sumanto Proceeding 2024 International Conference on Information Technology Research and Innovation Icitri 2024, 2024 The fisheries industry is crucial for the global economy, providing essential food resources and livelihoods. However, accurate data collection from fishermen’s narrative reports is challenging due to their unstructured nature and low technological literacy among fishermen. This study explores the application of Large Language Models (LLMs), specifically the Merak-7B model, to automate data extraction from these reports. Due to resource limitations, Claude AI was used to generate narrative examples based on predefined prompts, reflecting potential reports by fishermen. Additionally, the study conducts a comparative analysis between Merak-7B and Claude AI to assess accuracy and reliability in data extraction. The Merak-7B model demonstrates high accuracy in converting unstructured text into structured data, thereby improving data collection efficiency and reliability. The results indicate that Merak-7B achieved an accuracy rate of 91% in extracting key data points from fishermen’s reports using 10 narrative examples as samples. This approach addresses the technological challenges faced by fishermen and supports better decision-making in fisheries management through enhanced data quality. The findings suggest that LLMs can significantly streamline data collection processes, aiding in sustainable fisheries management.
Use Case Points (UCP) with 3 Point in Program Evaluation and Review Technique (PERT) to Estimate Effort Software E Prayitno, J Siregar, Y N Dewi, C Bachri, L Indriyani, S Ma’arif Journal of Physics Conference Series, 2020 Software has an important influence in business to accelerate and manage business processes better. Software development projects and projects in general require precise estimates for good business management and running smoothly. The method of Use Case Points is one of the methods used to estimate object-based software utterances, but there are some disadvantages such as subjective assessment of Technical Complexity Factor (TCF) and Environmental Complexity Factor (ECF) for novice developers who are inexperienced. Three points from the Program Evaluation and Review Technique (PERT) are used at UCP to help estimate the two factors better by modifying both assessment factors. By using 3 PERT points in UCP, it is expected to help novice developers in estimating the size of the software as predicted by experienced developers. Results of the calculation of the estimated software information systems sales of goods in the Madrasah Istiqlal foundation obtained an effort value is 1,011 man-hours.
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