Arsitektur Komputasi Kognitif Berbasis Penalaran Neuro-Simbolik untuk Manajemen Sumber Daya Multicore
DOI:
https://doi.org/10.57093/jisti.v9i2.432Keywords:
Komputasi kognitif, Multicore, Penalaran neuro-simbolik, Manajemen sumber daya, Machine learningAbstract
Penelitian ini mengembangkan arsitektur komputasi kognitif neuro-simbolik untuk rekomendasi pengelolaan sumber daya multicore. Sebanyak 5.000 observasi gabungan pengukuran dan simulasi yang dinyatakan telah divalidasi pakar dianalisis menggunakan pembagian terstratifikasi 70:15:15. Arsitektur mengintegrasikan multilayer perceptron, aturan simbolik terpelajar, dan pemeriksaan kelayakan tindakan. Evaluasi menunjukkan Random Forest memiliki Macro-F1 tertinggi (87,29%), sedangkan fusi neuro-simbolik menghasilkan 78,92% dan meningkat menjadi 79,06% setelah pemeriksaan aturan, dengan akurasi 90,27%. Penalaran simbolik meningkatkan sensitivitas beberapa tindakan minoritas, tetapi menimbulkan kompromi kesalahan pada kelas lainnya. Temuan menunjukkan manfaat integrasi penalaran yang dapat ditelusuri, sekaligus menegaskan bahwa keunggulan prediktif dan efisiensi operasional belum otomatis diperoleh dari fusi model. Evaluasi prospektif penjadwalan diperlukan untuk mengukur dampak aktual pada latensi dan throughput
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