NFT Investments Analysis: A Strategic Approach with Ranking Insights and Sales Forecasting System for Informed Decision-Making

Authors

  • Vivi Aida Fitria Universitas Negeri Malang Institut Teknologi dan Bisnis Asia Malang
  • Arif Nur Afandi Universitas Negeri Malang
  • Aripriharta Aripriharta Universitas Negeri Malang
  • Lilis Widayanti Universitas Negeri Malang Institut Teknologi dan Bisnis Asia Malang
  • Danang Arbian Sulistyo Teknik Informatika, Institut Teknologi dan Bisnis Asia Malang

DOI:

https://doi.org/10.12695/ajtm.2023.16.2.2

Keywords:

Forecasting System, PSO, Rangking System, SAW, Single Exponential Smoothing

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

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References

Almajed, R., Abualkishik, A. Z., Ibrahim, A., & Mourad, N. (2023). Forecasting NFT Prices on Web3 Blockchain Using Machine Learning to Provide Forecasting NFT Prices on Web3 Blockchain Using Machine Learning to Provide SAAS NFT Collectors. Fusion: Practice and Applications (FPA), 10(2), 55–68. https://doi.org/10.54216/FPA.100205

Anggraini, D., & Sihotang, H. T. (2019). Decision Support System For Choosing The Best Class Guardian With Simple Additive Weighting Method. Jurnal Mantik, 3(3), 1–9. http://iocscience.org/ejournal/index.php/mantik/article/view/882/595

Bao, H., & Roubaud, D. (2022). Non-Fungible Token: A Systematic Review and Research Agenda. Journal of Risk and Financial Management, 15(5), 215. doi: 10.3390/JRFM15050215

Baykal, T. M., Colak, H. E., & Kılınc, C. (2022). Forecasting future climate boundary maps (2021–2060) using exponential smoothing method and GIS. Science of The Total Environment, 848, 157633. doi: 10.1016/J.SCITOTENV.2022.157633

Bhujel, S., & Rahulamathavan, Y. (2022). A Survey: Security, Transparency, and Scalability Issues of NFT’s and Its Marketplaces. Sensors, 22(8833), doi: 10.3390/S22228833

Branny, J., Dornberger, R., & Hanne, T. (2022). Non-fungible Token Price Prediction with Multivariate LSTM Neural Networks. 2022 9th International Conference on Soft Computing and Machine Intelligence, ISCMI 2022, 56–61. doi: 10.1109/ISCMI56532.2022.10068442

Deng, C., Zhang, X., Huang, Y., & Bao, Y. (2021). Equipping Seasonal Exponential Smoothing Models with Particle Swarm Optimization Algorithm for Electricity Consumption Forecasting. Energies 2021, 14(13), 4036. doi: 10.3390/EN14134036

Ferdinand, M. A. B., Wibawa, A. P., Zaeni, I. A. E., & Rosyid, H. A. (2020). Single Exponential Smoothing-Multilayer Perceptron Untuk Peramalan Pengunjung Unik Jurnal Elektronik. Mobile and Forensics, 2(2), 62–70. doi: 10.12928/mf.v2i2.2034

Fitria, N. D., & Wibawa, A. P. (2021). Sistem Pembobotan Berdasarkan Teknik Analisis Korelasi Untuk Penerimaan Siswa Baru Menggunakan Metode SAW. JURNAL MEDIA INFORMATIKA BUDIDARMA, 5(3), 1116. doi: 10.30865/mib.v5i3.3080

Fitria, V. A. (2019a). Parameter Optimization of Single Exponential Smoothing Using Golden Section Method for Groceries Forecasting. ZERO: Jurnal Sains, Matematika Dan Terapan, 2(2), 89. doi: 10.30829/zero.v2i2.3438

Fitria, V. A. (2019b). Peramalan Harga Sembako di Kota Malang Menggunakan Metode Single Exponential Smoothing. Jurnal Sains Matematika Dan Statistika, 5(1). http://siskaperbapo.com/harga/tabel

Gad, A. G. (2022). Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review. Archives of Computational Methods in Engineering 2022 29:5, 29(5), 2531–2561. doi: 10.1007/S11831-021-09694-4

Ibrahim, A., & Surya, R. A. (2019). The Implementation of Simple Additive Weighting (SAW) Method in Decision Support System for the Best School Selection in Jambi. Journal of Physics: Conference Series, 1338(1), 012054. doi: 10.1088/1742-6596/1338/1/012054

Javed, S. A., & Cudjoe, D. (2022). A novel grey forecasting of greenhouse gas emissions from four industries of China and India. Sustainable Production and Consumption, 29, 777–790. doi: 10.1016/j.spc.2021.11.017

Komang Yanti Suartini, N., Gede Hendra Divayana, D., & Joni Erawati Dewi, L. (2023). Comparison Analysis of AHP-SAW, AHP-WP, AHP-TOPSIS Methods in Private Tutor Selection. I.J. Modern Education and Computer Science, 1, 28–45. doi: 10.5815/ijmecs.2023.01.03

Kraujalienė, L. (2019). Comparative Analysis Of Multicriteria Decision-Making Methods Evaluating The Efficiency Of Technology Transfer. Business, Management and Education, 17, 72–93. doi: 10.3846/bme.2019.11014

Majid, R. (2018). Advances in Statistical Forecasting Methods: An Overview. Economic Affairs, 63(4). doi: 10.30954/0424-2513.4.2018.5

Mao, Y., Pranolo, A., Wibawa, A. P., Putra Utama, A. B., Dwiyanto, F. A., & Saifullah, S. (2022). Selection of Precise Long Short Term Memory (LSTM) Hyperparameters based on Particle Swarm Optimization. 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC), 1114–1121. doi:10.1109/ICAAIC53929.2022.9792708

Maretania, I., Alfadjri, M. R., Paramesywarie, P. U., & Nurcahyo, R. (2021). Comparison of Double Exponential and Single Exponential Smoothing Accuracy in Krakatau Steel Demand Forecasting Fitted Model. Proceedings of the International Conference on Industrial Engineering and Operations Management, 356–364.

Nurmalini, & Rahim, R. (2017). Study Approach of Simple Additive Weighting For Decision Support System. International Journal of Scientific Research in Science and Technology, 3(3), 541–544. https://doi.org/10.32628/IJSRST1733198

Pamungkas, A., Puspasari, R., Nurfiarini, A., Zulkarnain, R., & Waryanto, W. (2021). Comparison of Exponential Smoothing Methods for Forecasting Marine Fish Production in Pekalongan Waters, Central Java. IOP Conference Series: Earth and Environmental Science, 934(1), 012016. doi: 10.1088/1755-1315/934/1/012016

Sihombing, V., Siregar, V. M. M., Tampubolon, W. S., Jannah, M., Risdalina, & Hakim, A. (2021). Implementation of simple additive weighting algorithm in decision support system. IOP Conference Series: Materials Science and Engineering, 1088(1), 012014. doi: 10.1088/1757-899X/1088/1/012014

Siregar, B., Butar-Butar, I. A., Rahmat, R., Andayani, U., & Fahmi, F. (2017). Comparison of Exponential Smoothing Methods in Forecasting Palm Oil Real Production. Journal of Physics: Conference Series, 801, 012004. doi: 10.1088/1742-6596/801/1/012004

Swari, M. H. P., Handika, I. P. S., Satwika, I. K. S., & Wahani, H. E. (2022). Optimization of Single Exponential Smoothing using Particle Swarm Optimization and Modified Particle Swarm Optimization in Sales Forecast. Proceeding - IEEE 8th Information Technology International Seminar, ITIS 2022, 292–296. doi: 10.1109/ITIS57155.2022.10010034

Taherdoost, H. (2023). Analysis of Simple Additive Weighting Method (SAW) as a MultiAttribute Decision-Making Technique: A Step-by-Step Guide. Journal of Management Science & Engineering Research, 6(1). doi: 10.30564/jmser.v6i1.5400

Vijayakumar, T., & Vinothkanna, M. R. (2020). Efficient Energy Load Distribution Model using Modified Particle Swarm Optimization Algorithm. Journal of Artificial Intelligence and Capsule Networks. doi: 10.36548/jaicn.2020.4.005

Wang, Q., Li, R., Wang, Q., & Chen, S. (2021). Non-Fungible Token (NFT): Overview, Evaluation, Opportunities and Challenges. doi: 10.48550/arxiv.2105.07447

Wang, Z., Chen, Q., & Lee, S.-J. (2023). Prediction of NFT Sale Price Fluctuations on OpenSea Using Machine Learning Approaches. Computers, Materials & Continua, 75(2), 2443–2459. doi: 10.32604/cmc.2023.037553

Wibawa, A. P., Mahmudy, W. F., Rizki, A. M., Yuliastuti, G. E., Tama, I., & Pambudi. (2022). Multi-Site Aggregate Production Planning Using Particle Swarm Optimization. Journal of Engineering, Project, and Production Management. https://doi.org/10.32738/JEPPM-2022-0006

Wilson, K. B., Karg, A., & Ghaderi, H. (2022). Prospecting non-fungible tokens in the digital economy: Stakeholders and ecosystem, risk and opportunity. Business Horizons, 65(5), 657–670. doi: 10.1016/J.BUSHOR.2021.10.007

Wira Trise Putra, D., & Agustian Punggara, A. (2018). Comparison Analysis of Simple Additive Weighting (SAW) and Weigthed Product (WP) In Decision Support Systems. MATEC Web of Conferences, 215, 01003. doi: 10.1051/matecconf/201821501003

Zakaria, Elmunsyah, H., & Fahmi, A. (2019). Implementasi algoritma simple additive weighting untuk menentukan reviewer PKM pada portal PKM di Universitas Negeri Malang. TEKNO, 28(2), 149. doi: 10.17977/um034v28i2p149-165

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Submitted

2023-05-22

Accepted

2023-12-29

Published

2023-12-30

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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