PROCEDURAL SEGMENT GENERATION TOPOLOGI JALUR DAN IMPLEMENTASI MOUNT MECHANIC DINAMIS PADA GIM ENDEMIC RUSH

The design of the game “Endemic Rush” was driven by the challenges of performance stability and control responsiveness on Android devices with limited specifications. The main issues stem from the computational load of the dynamic environment and the complexity of the character logic. This research implements the Procedural Segment Generation (PSG) algorithm for automatic path topology and a Finite State Machine (FSM) architecture for Mount Mechanic stability. Using the Game Development Life Cycle (GDLC) method, the results show that the application of PSG successfully creates path variations without causing sudden performance drops, while the FSM ensures precise character transitions. Supported by the Object Pooling method, the game is able to maintain a stable frame rate above 30 FPS. Furthermore, beta testing with users yielded an average playability index of 89.32% (“Very Playable”). These results definitively validate “Endemic Rush” as a responsive, stable, and optimal educational tool for wildlife on mobile platforms.

Keywords: Android, Finite State Machine, Lane Topology, Mount Mechanic, Procedural Segment Generation.

Perancangan gim "Endemic Rush" dilatarbelakangi tantangan stabilitas performa dan responsivitas kontrol pada perangkat Android berspesifikasi terbatas. Masalah utama terletak pada beban komputasi lingkungan dinamis dan kompleksitas logika karakter. Penelitian ini mengimplementasikan algoritma Procedural Segment Generation (PSG) untuk topologi jalur otomatis dan arsitektur Finite State Machine (FSM) untuk stabilitas Mount Mechanic. Menggunakan metode Game Development Life Cycle (GDLC), hasil menunjukkan penerapan PSG berhasil menciptakan variasi jalur tanpa menurunkan performa secara mendadak, sementara FSM menjamin transisi karakter yang presisi. Didukung metode Object Pooling, gim mampu menjaga frame rate stabil di atas 30 FPS. Lebih lanjut, pengujian Beta pada pengguna menghasilkan indeks kelayakan rata-rata sebesar 89,32% (Sangat Layak). Hasil ini secara definitif memvalidasi "Endemic Rush" sebagai media edukasi fauna yang responsif, stabil, dan optimal pada platform mobile.

Kata Kunci: Android, Finite State Machine, Mount Mechanic, Procedural Segment Generation, Topologi Jalur.