Sistem Rekomendasi Item IAP (In App Purchase) Berbasis NFT(Non Fungible Token) Menggunakan Smart Contract

Pradana, Reza Putra (2022) Sistem Rekomendasi Item IAP (In App Purchase) Berbasis NFT(Non Fungible Token) Menggunakan Smart Contract. Masters thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Market value industri game pada tahun 2021 dikatakan lebih dari 198.40 Miliar USD. Hal itu juga didukung dengan semakin meningkatnya jumlah gamer dengan jumlah 4,75 Miliar pada januari 2022. Pada era modern sekarang, metode pendapatan In-App Purchase telah menjadi trend besar. Dengan banyaknya model game free-to-play, dimana gamer dapat memilih atau membeli banyak item selama permainan untuk mempercepat progres permainan atau menikmati konten penuh dari permainan. Namun terkadang para pemain kewalahan dengan jumlah item yang di tawarkan, dan menyebabkan pemain kesulitan untuk memilih karena konten game yang terlalu sulit. Selain itu, Pemain juga khawatir dengan keamanan asset digital mereka, karena tahun 2021 terdapat 7,5 juta asset digital yang hilang karena peretasan. Untuk mengataasi permasalahan ini, kami mengusulkan sistem rekomendasi untuk memudahkan pemain memilih item yang sesuai dengan playstyle mereka. Kami menggunakan metode multi-criteria recommender system (MCRS) karena metode ini dapat meningkatkan akurasi rekomendasi dibanding rekomendasi konvensional yang hanya menggunakan satu kriteria. Pada penelitian ini kami menggunakan delapan kriteria untuk menghitung rekomendasi. Hasil pengujian rekomendasi kami menunjukan nilai akurasi = 0,71, presisi = 0,76, recall = 0,71 dan skor F1 = 0,66. untuk mengatasi permasalahan keamanan, kami mengusulkan implementasi Non-Fungible Token (NFT) untuk setiap item. penggunaan NFT dapat meningkatkan keamanan karena menggunakan arsitektur desentrailisasi blockchain yang setiap transaksinya dienkripsi. Sistem menjamin bahwa aset akan tetap online, sehingga pengguna tidak ada resiko kehilangan kepemilikan asset mereka ketika developer melakukan perubahan pada game atau ditutupnya server game
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The market value of the gaming industry in 2021 is said to be more than 198.40 billion USD. Market value is also supported by the number of gamers, with 4.75 billion in January 2022. In today's In-App Purchase (IAP) income method has become a big trend in today's modern era models of free-to-play games, where gamers can choose or buy many items during the game to speed up the progress of the game or enjoy the full content of the game. However, sometimes players are overwhelmed with the number of items on offer, making it difficult for players to choose because the game content is too diverse. In addition, players are also worried about the security of their digital assets because, in 2021, there will be 7.5 million digital assets lost due to hacking. We propose a recommendation system to make it easier for players to choose items that suit their playstyle to solve this problem. We use the multi-criteria recommender system (MCRS) method because this method can improve the accuracy of recommendations compared to conventional recommendations that only use one criterion. In this study, we used eight criteria to calculate the recommendations. The results of our recommendation test show the accuracy value = 0.71, precision = 0.76, recall = 0.71 and F1 score = 0.66. To address security issues, we propose the implementation of a Non-Fungible Token (NFT) for each item. NFT can increase security because it uses a decentralized blockchain architecture in which every transaction is encrypted. The system guarantees that the assets will remain online so that users do not risk losing ownership of their assets when the developer changes game data, or the game server closes
Purchase, RPG(Role Playing Game), Multi-Criteria Recommender System, NFT, Blockchain.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	In-App-Purchase, RPG(Role Playing Game), Multi-Criteria Recommender System, NFT, Blockchain.
Subjects:	G Geography. Anthropology. Recreation > GV Recreation Leisure > GV1469.2 Computer games Q Science > QA Mathematics > QA336 Artificial Intelligence Q Science > QA Mathematics > QA76.585 Cloud computing. Mobile computing. Q Science > QA Mathematics > QA76.9.I58 Recommender systems (Information filtering)
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Electrical Engineering > 20101-(S2) Master Thesis
Depositing User:	REZA PUTRA PRADANA
Date Deposited:	27 Jan 2023 03:17
Last Modified:	27 Jan 2023 07:11
URI:	http://repository.its.ac.id/id/eprint/95677

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