OPTIMALISASI TRADE-OFF STORAGE DAN LATENSI DALAM MANAJEMEN DATA INDUSTRI KILANG BERBASIS GENETIC ALGORITHM
DOI:
https://doi.org/10.53026/sntem2025.v5i1.534Keywords:
algoritma, availability, granulasi, instrumen, optimasiAbstract
Pengelolaan data instrumen kilang menghadapi tantangan trade-off yang krusial antara efisiensi penyimpanan (storage) dan kecepatan akses (latensi/Service Level Agreement/SLA). Peningkatan gra-nularitas data (sampling_ rate_hz) dan ketersediaan (replication_factor) seringkali mengorbankan ka-pasitas penyimpanan yang terbatas. Untuk mengatasi konflik tersebut, penelitian ini menerapkan Al-goritma Genetika (GA) sebagai pendekatan optimasi multikriteria. Kromosom GA merepresentasikan empat parameter utama, yaitu sampling_rate_hz, compression_level, retention_days, dan replica-tion_factor. Proses evolusi menggunakan kombinasi operator seleksi (Tournament atau Roulette), crossover (One-Point atau Two-Point), mutasi acak, dan elitisme. Evaluasi dilakukan dengan fungsi fit-ness berbobot yang bertujuan memini-malkan biaya gabungan (kebutuhan storage dan latency) sambil memenuhi kendala granularitas data dan availability sistem. Hasil penelitian menunjukkan GA mampu menemukan serangkaian solusi Pareto optimal yang secara efektif menyeimbangkan kualitas data dan efisiensi sistem. Konvergensi solusi yang teramati pada setiap generasi menghasilkan estimasi peng-gunaan storage tahunan yang terkendali (misalnya, berkurang dari baseline) dengan latency yang tetap memenuhi standar SLA (misalnya, di bawah 50 ms). Berdasarkan temuan ini, Algoritma Genetika terbukti efektif sebagai metode optimasi trade-off dalam sistem manajemen data instrumen kilang, dan berpotensi berfungsi sebagai alat bantu pengambilan keputusan strategis pada pengelolaan data skala industri.
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