SISTEM PAKAR HYBRID CF-NAIVE BAYES UNTUK DIAGNOSIS GANGGUAN P&ID DISTILASI BENZENA
DOI:
https://doi.org/10.53026/prosidingsntem.v5i1.688Keywords:
Sistem Pakar, Certainty Factor, Naïve Bayesian, Distilasi, P&IDAbstract
Diagnosis gangguan pada P&ID sistem distilasi benzena merupakan tantangan kompleks yang membutuhkan keahlian teknis mendalam. Penelitian ini bertujuan mengembangkan prototipe sistem pakar hybrid yang mengintegrasikan metode Certainty Factor (CF) dan Naive Bayes. Kombinasi ini dipilih untuk menggabungkan kekuatan aturan pakar (CF) yang tegas dalam mereplikasi alur pikir ahli dengan kemampuan Naive Bayes dalam mengelola ketidakpastian data gejala yang tidak lengkap. Metode penelitian mencakup perancangan knowledge base dengan 114 gejala dan 60 aturan, yang diperkuat dengan integrasi prinsip-prinsip Kister's Distillation Operation untuk fenomena kritis seperti flooding dan pinching. Hasil validasi sistem menggunakan 30 test case menunjukkan akurasi hybrid keseluruhan mencapai 86,7%, secara signifikan mengungguli metode baseline tunggal yaitu Certainty Factor (83,3%) maupun Naive Bayes (80,0%). Secara khusus, akurasi diagnosis pada 15 kasus yang diperkuat analisis Kister (Studi Kasus Literatur) terbukti sangat tinggi, mencapai 93,3%. Kesimpulannya, prototipe sistem pakar hybrid ini terbukti efektif dan konsisten dalam mentransfer pengetahuan ahli yang kompleks ke dalam platform digital untuk diagnosis gangguan P&ID.
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