Serious Game Kewaspadaan Tanah Longsor Berbasis TOPSIS Menggunakan HFSM

Athaillah, Andi Muhammad Yassar (2026) Serious Game Kewaspadaan Tanah Longsor Berbasis TOPSIS Menggunakan HFSM. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Sistem peringatan dini tanah longsor saat ini masih terbatas pada penyajian data yang kurang interaktif secara spasial. Penelitian ini mengusulkan pengembangan serious game kesiapsiagaan bencana berbasis Unreal Engine 5 sebagai media edukasi interaktif. Sistem mengintegrasikan Hierarchical Finite State Machine (HFSM) untuk mengendalikan alur skenario bencana secara terstruktur, dan algoritma Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) guna mengevaluasi rute evakuasi terbaik berdasarkan kriteria jarak, kemiringan lereng, dan curah hujan secara waktu nyata. Evaluasi dilakukan terhadap fungsionalitas, akurasi algoritma, kinerja komputasi, dan pengalaman pengguna (MEEGA+). Hasil pengujian menunjukkan fungsionalitas sistem berhasil 100 persen dan akurasi komputasi TOPSIS terbukti presisi tanpa deviasi matematis dibandingkan perhitungan manual, serta secara andal berhasil menetapkan Zona A sebagai titik evakuasi teraman. Kinerja sistem terpantau sangat stabil, mempertahankan rata-rata bingkai per detik lebih dari 100 saat simulasi jatuhnya material longsor. Hasil evaluasi MEEGA+ menunjukkan sistem memperoleh skor rata-rata keseluruhan 3,76 dan skor dimensi Perceived Learning sebesar 4,15, yang keduanya berada pada kategori Baik. Integrasi HFSM dan TOPSIS terbukti andal dan efektif sebagai instrumen edukasi mitigasi kebencanaan.
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Current landslide early warning systems are still limited to data presentations that lack spatial interactivity. This research proposes the development of a disaster preparedness serious game based on Unreal Engine 5 as an interactive educational medium. The system integrates a Hierarchical Finite State Machine (HFSM) to control the disaster scenario flow in a structured manner, and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithm to evaluate the best evacuation routes based on distance, slope inclination, and rainfall criteria in real-time. Evaluations were conducted on system functionality, algorithm accuracy, computational performance, and user experience (MEEGA+). The test results indicate that the system's functionality achieved a 100 percent success rate, and the computational accuracy of TOPSIS was proven to be precise with no mathematical deviation compared to manual calculations, reliably establishing Zone A as the safest evacuation point. System performance was observed to be highly stable, maintaining an average of over 100 frames per second during the falling landslide material simulation. The MEEGA+ evaluation results show that the system obtained an overall average score of 3.76 and a Perceived Learning dimension score of 4.15, both of which fall into the Good category. The integration of HFSM and TOPSIS has proven to be reliable and effective as an educational instrument for disaster mitigation.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Serious Game, Tanah Longsor, TOPSIS, HFSM, Unreal Engine 5, MEEGA+. Serious Game, Landslide, TOPSIS, HFSM, Unreal Engine 5, MEEGA+
Subjects:	Q Science > QA Mathematics > QA76.758 Software engineering Q Science > QA Mathematics > QA76.9 Computer algorithms. Virtual Reality. Computer simulation. T Technology > T Technology (General) > T385 Visualization--Technique T Technology > T Technology (General) > T57.62 Simulation T Technology > T Technology (General) > T58.62 Decision support systems
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Computer Engineering > 90243-(S1) Undergraduate Thesis
Depositing User:	Andi Muhammad Yassar Athaillah
Date Deposited:	23 Jul 2026 09:51
Last Modified:	23 Jul 2026 09:51
URI:	http://repository.its.ac.id/id/eprint/136556

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