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      "abstract": "enelitian ini dilatarbelakangi oleh permasalahan performa rendering pada\r\npermainan digital berbasis lingkungan 3D dengan kompleksitas objek tinggi dan\r\njarak pandang yang luas, yang menyebabkan penurunan Frame per Second (FPS),\r\nmunculnya lonjakan waktu render (spike), serta ketidakstabilan performa\r\nsehingga berdampak pada kualitas visual dan kenyamanan pengguna. Untuk\r\nmengatasi permasalahan tersebut, penelitian ini menerapkan pendekatan optimasi\r\nberbasis reinforcement learning yang merupakan percabangan dari machine\r\nlearning yang diintegrasikan dengan teknik Spatial Temporal Post Processing, di\r\nmana agent dilatih untuk mempelajari kebijakan optimal dalam menyesuaikan\r\nparameter rendering secara adaptif sesuai kondisi lingkungan. Proses pelatihan\r\ndilakukan melalui integrasi Unity dan Python menggunakan ML-Agents, serta\r\npemantauan performa menggunakan TensorBoard dengan indikator cumulative\r\nreward, value loss, dan policy entropy guna memastikan proses pembelajaran\r\nberjalan stabil dan konvergen. Hasil penelitian menunjukkan bahwa metode yang\r\ndiusulkan mampu meningkatkan performa rendering secara signifikan, yang\r\nditandai dengan penurunan waktu frame rata-rata, berkurangnya spike yang\r\nmelebihi 33 ms, meningkatnya stabilitas FPS, serta distribusi beban rendering\r\nyang lebih seimbang. Selain itu, sistem mampu melakukan penyesuaian skala\r\nrender secara adaptif tanpa mengorbankan kualitas visual, sehingga pendekatan\r\nini terbukti efektif dalam mengoptimalkan kinerja grafis pada permainan digital\r\ndengan lingkungan kompleks.",
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      "institution": "Universitas AMIKOM Yogyakarta"
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