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Abstract

Abstract. The non-fungible token (NFT) is a unique token used to represent digital assets such as art, music, videos, and other collections. NFT has gained significant attention from the business and industry sectors in recent years. This study reports an increase in the number of active NFT users from 77,000 to 222,000 in early 2021. Investment in NFT has advantages and disadvantages, and one of the challenges faced by investors is that they may not have enough knowledge about investing risks and may find it difficult to recognize and evaluate potential dangers. To address this problem, this study proposes a system that provides information on NFT collection sales rankings and volume sales forecasts. The simple additive weighting (SAW) method is used to determine the NFT collection rankings, and exponential smoothing is used to forecast sales volume. The Particle Swarm Optimization (PSO) method is applied to optimize the parameter alpha of the Exponential Smoothing method. With an accuracy rate of 80.38%, the combination of using the Single Exponential Smoothing method with PSO optimization can provide good predictions for future NFT sales. The proposed system aims to provide investors with accurate information to make informed decisions when investing in NFT.


Keywords:  Forecasting system, pso, ranking system, saw, single exponential smoothing

Keywords

Forecasting System PSO Rangking System SAW Single Exponential Smoothing

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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Copyright @2017. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (http://creativecommons.org/licenses/by-nc-sa/4.0/) which permits unrestricted non-commercial used, distribution and reproduction in any medium 

How to Cite
Fitria, V. A., Afandi, A. N., Aripriharta, A., Widayanti, L., & Sulistyo, D. A. (2023). NFT Investments Analysis: A Strategic Approach with Ranking Insights and Sales Forecasting System for Informed Decision-Making. The Asian Journal of Technology Management (AJTM), 16(2), 95–108. https://doi.org/10.12695/ajtm.2023.16.2.2
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