Evaluasi Algoritma Genetika Berbasis Multi-Seed Untuk Optimasi Primer PCR
DOI:
https://doi.org/10.47701/jbf4c861Keywords:
desain primer, PCR, algoritma genetika, robustness, konvergensiAbstract
Desain primer merupakan tahap penting dalam Polymerase Chain Reaction (PCR) karena kualitas primer mempengaruhi spesifisitas, efisiensi amplifikasi, dan keberhasilan analisis molekuler. Penelitian sebelumnya telah merangkum karakteristik primer PCR, meliputi panjang primer, melting temperature (Tm), selisih Tm, GC content, GC clamp, hairpin, dimer, repetition, specificity, dan panjang produk [16]. Penelitian lanjutan mengembangkan desain primer PCR menggunakan Algoritma Genetika (GA) dengan representasi kromosom berbasis indeks awal primer forward, panjang primer forward, panjang produk, dan panjang primer reverse [17]. Penelitian ini bertujuan mengevaluasi robustness dan konvergensi pipeline GA untuk optimasi desain primer PCR melalui replikasi multi-seed. Kandidat primer menggunakan representasi kromosom yang sama dengan penelitian sebelumnya, sedangkan fungsi objektif dibangun berdasarkan akumulasi penalti terhadap pelanggaran parameter primer. Dengan panjang sekuens target 2.608 bp, ruang pencarian kasar mencapai 153.758.619 kemungkinan, sehingga pencarian exhaustive tidak efisien. Evaluasi dilakukan menggunakan dua set seed, tren best fitness dan median fitness, diversity top-K berbasis normalized Hamming distance, serta random search baseline dengan budget evaluasi maksimum yang sama. Hasil menunjukkan GA mencapai objective optimum pada 19 dari 20 replikasi (95%). Pada seed set kedua, seluruh seed mencapai objective optimum dengan rata-rata 480–580 evaluasi kandidat, sedangkan random search membutuhkan sekitar 10.280 evaluasi. Dengan demikian, kontribusi utama GA terletak pada efisiensi dan keterarahan pencarian.
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