Smart Environmental Response using Recommender System for Serious Game

Damastuti, Fardani Annisa (2025) Smart Environmental Response using Recommender System for Serious Game. Doctoral thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Serious games are intended to increase awareness, problem-solve, educate, and train individuals, with the goal of achieving outcomes such as knowledge acquisition, behavior modification, and skill enhancement. Players’ success is contingent upon their engagement, which fosters motivation and learning by means of personalized, interactive, and immersive experiences. The primary objective of this dissertation is to integrate dynamic difficulty adjustment (DDA), procedural audio, and real-time environmental changes into a VR-based resource management simulation that addresses engagement challenges.
DDA, which is fueled by Q-learning and fuzzy logic, dynamically adjusts the complexity of the game in real-time in accordance with the performance of the player. Immersion is further enhanced by procedural audio generated using Unreal Engine’s Meta-Sounds. These dynamic systems
areexamined in conjunction with recommender-based smart environments to determine how they enhance learning and engagement in resource management. Analyzing player performance, collecting feedback, scenario testing, and comparing gameplay conditions are several methods. Personalized gameplay enhances the educational experience, while the integration of DDA and real-time elements enhances player immersion, adaptability, and sustained interest, as demonstrated by the proof-of-concept, Smart Environmental Games.
Player satisfaction increased from 3.2 to 4.6, resource management efficiency improved by 28%, and decision-making speed increased by 15%. The results indicate a 35% increase in playtime. The action success rate increased by 22%, and player strategies were influenced by dynamic weather. Stormy weather resulted in a 15% longer decision-making time, and it increased boat damage by 20% and reduced fishing success by 50%. Across a variety of educational game genres, these results illustrate the potential of dynamic systems to establish personalized, engaging learning environments."
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"Tantangan dalam mempertahankan keterlibatan pemain jangka panjang sering kali dihadapi dalam permainan serius yang dirancang untuk tujuan instruksional. Disertasi ini berfokus pada simulasi pengelolaan sumber daya berbasis VR yang mengintegrasikan sistem dinamis, termasuk audio prosedural, perubahan lingkungan waktu nyata, dan penyesuaian kesulitan dinamis (Dynamic Difficulty Adjustment/DDA). DDA secara
adaptif memodifikasi kompleksitas permainan secara real-time berdasarkan kinerja pemain dengan memanfaatkan Q-learning dan logika fuzzy. Selain itu, audio prosedural yang dihasilkan melalui Meta-Sounds dari Unreal Engine meningkatkan imersi. Penelitian ini mengeksplorasi peningkatan keterlibatan dan hasil pembelajaran dalam pengelolaan sumber daya serta pengambilan keputusan melalui integrasi sistem-sistem ini dengan lingkungan cerdas berbasis rekomendasi. Metodologi penelitian mencakup analisis kinerja pemain, pengumpulan umpan balik, pengujian skenario, dan evaluasi komparatif terhadap berbagai kondisi permainan. Hasil ini menekankan potensi sistem dinamis dalam menciptakan lingkungan belajar yang personal dan imersif di berbagai genre permainan edukasi. Sistem DDA meningkatkan
waktu bermain sebesar 35%, dari 2,5 jam pada tingkat kesulitan statis menjadi 3,4 jam dengan tingkat kesulitan dinamis. Rata-rata penilaian kepuasan pemain meningkat menjadi 4,6 dari 3,2 pada pengaturan statis. Waktu pengambilan keputusan utama menurun dari 0,8 detik menjadi 0,7 detik karena efisiensi pengelolaan sumber daya meningkat sebesar 28% dan kecepatan pengambilan keputusan naik sebesar 15%. Tingkat keberhasilan tindakan meningkat sebesar 22%, dibandingkan dengan 75% pada permainan tanpa sistem adaptif. Strategi pemain berubah dalam kondisi cuaca badai karena probabilitas kerusakan kapal meningkat sebesar 20% dan keberhasilan penangkapan ikan turun sebesar 50%. Kondisi hujan meningkatkan waktu pengambilan keputusan sebesar 15%.

Item Type:	Thesis (Doctoral)
Uncontrolled Keywords:	Q-learning, Meta-Sounds, Dynamic Difficulty Adjustment, Real-time environmental, Serious Game
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7888.3 Digital computers
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Electrical Engineering > 20001-(S3) PhD Thesis
Depositing User:	Fardani Annisa Damastuti
Date Deposited:	28 Jan 2025 22:05
Last Modified:	28 Jan 2025 22:05
URI:	http://repository.its.ac.id/id/eprint/117011

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