Muhammad Ramulia Siregar, Imran Lubis, Arief Budiman, Budi Budi
The development of blockchain technology, especially Non-Fungible Tokens (NFT), has created challenges for investors in determining the right investment value. This study aims to develop a decision support system using the Weighted Aggregated Sum Product Assessment (WASPAS) method and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to assess NFT as an investment alternative. The research process involves several stages, including problem identification, problem analysis, literature study, data collection, and data analysis. The criteria used in assessing NFT include price, owner/origin, format, rarity, and industry, each with a set weight. After collecting data on various NFTs, a decision matrix is constructed and normalized to reflect the performance of each alternative. The WASPAS and TOPSIS methods are used to assign preference values to each NFT alternative based on their proximity to the positive and negative ideal solutions. The analysis results show that NFT named "Video Clip" (A3) has the highest value with a preference of 0.671, followed by "Song" (A5) with a value of 0.445, and "Book" (A2) with a value of 0.465. Meanwhile, "Selvie Photo" (A4) and "Photo" (A1) have the lowest preferences of 0.196 and 0.189, respectively. This study contributes to NFT investment decision making, by providing a systematic and data-driven approach that can reduce risk and maximize potential profits for investors. The combination of WASPAS and TOPSIS methods offers a comprehensive framework for NFT valuation, so that it can be adopted by investors and NFT platform developers in evaluating the value of digital assets more effectively.
Our Pharmaceutical Supply chain systems using smart contracts can have wide range of applications across the pharmaceuticals industry.Smart contracts are self-executing agreements with the terms of the agreement directly written into the code.They can be use to automate the process of supply chain management and reduce costs, increase transparency and account ability, and improve patient safety.
Blockchain 3.0, an advanced iteration of blockchain technology, has emerged with diverse applications encompassing various sectors such as identity authentication, logistics, medical care, and Industry 4.0/5.0. Notably, the integration of blockchain with industrial automation and control systems (IACS) holds immense potential in this evolving landscape. As industrial automation and control systems gain popularity alongside the widespread adoption of 5G networks, Internet of Things (IoT) devices are transforming into integral nodes within the blockchain network. This facilitates decentralized communication and verification, paving the way for a fully decentralized network. This paper focuses on showcasing the implementation and execution results of data preservation from industrial automation and control systems to IOTA, a prominent distributed ledger technology. The findings demonstrate the practical application of IOTA in securely preserving data within the context of industrial automation and control systems. The presented numerical results validate the effectiveness and feasibility of leveraging IOTA for seamless data preservation, ensuring data integrity, confidentiality, and transparency. By adopting IOTA’s innovative approach based on Directed Acyclic Graph (DAG), the paper contributes to the advancement of blockchain technology in the domain of Industry 4.0/5.0.
Boosted by modern technologies such as the Internet of Things (IoT), artificial intelligence (AI), Cloud, and so on, an innovative shift in economic models towards collaborative and dynamically constructed productions processes is being shaped worldwide, including in the context of supply chains. Nonetheless, current systems are incapable of managing/analyzing the massive amounts of incoming data as proposed in centralized situations. Consequently, the current network infrastructure cannot fully harness the Internet of Things' potential, resulting in data loss. Besides, the proliferation of IoT devices in the market increases the need for platforms that support data transparency to enforce full trust in information sharing and enable collaboration among the various partners. Existing supply chains have several drawbacks as a result of independent partners' lack of cooperation and mistrust. Blockchain, the distributed ledger technology (DLT), is a promising solution for the new technological business challenges. Based on a cryptographically decentralized platform, it ensures business applications' productivity enhancement and the removal of many limitations such as a lack of trust and data transparency. However, with the massive influx of IoT data, Blockchain faces numerous major challenges that prevent it from integrating into the supply chain. Directed ayclic graph (DAG) DLT, as an alternative to Blockchain, can address all of the drawbacks of Blockchain. Still, it has additional constraints such as smart contract limitations and a lack of task allocation mechanism. To that end, the purpose of this thesis is to define a new supply chain framework that considers the proliferation of IoT and the needs of new collaboration models through the integration of DLT technologies. We propose combining Blockchain and DAG into a single platform to respond to new supply chain requirements while bridging the gaps caused by these DLTs' drawbacks. Furthermore, we propose a distributed algorithm that runs on the DAG side to reallocate the numerous tasks among the various IoT devices, resulting in a high-performing system. Moreover, the DLT transparency feature shows unease with data privacy and supply chain control. Therefore, we investigate and analyse the DLT transparency impacts by shedding light on the existing supply chain projects. The study comes up with several mechanisms that could be used to achieve the supply chain goal and conclude that our DLT proposal has the suitable infrastructure for the required enhancements.