Optimalisasi Chatbot Berbasis TinyLLaMA dengan Fine - Tuning LoRA pada Sistem Layanan Agensi Property
https://doi.org/10.26594/teknologi.v16i1.6433
Abstract
Industri properti di Indonesia menghadapi tantangan dalam menyediakan layanan konsultasi yang cepat, responsif, dan efisien kepada calon pembeli. Model pemasaran tradisional dinilai kurang mampu memenuhi kebutuhan interaksi real-time yang semakin tinggi di era digital. Penelitian ini bertujuan mengembangkan chatbot konsultan properti berbahasa Indonesia menggunakan model TinyLLaMA-1.1B-Chat yang diadaptasi melalui metode fine-tuning Low-Rank Adaptation (LoRA) pada lapisan query projection (q_proj) dan value projection (v_proj). Dataset sintetis dikonstruksi menggunakan pendekatan Template-Based Data Generation yang mencakup tujuh kategori intent, yaitu simulasi KPR, dokumen legal, biaya dan pajak, spesifikasi properti, proses transaksi, konsultasi keputusan, serta risiko dan disclaimer. Eksperimen dilakukan pada tiga konfigurasi model dengan variasi nilai rank LoRA untuk menganalisis pengaruhnya terhadap kualitas dialog. Evaluasi dilakukan menggunakan metrik BLEU-4 serta kuesioner pengguna. Hasil eksperimen menunjukkan bahwa Skenario 3 dengan konfigurasi rank r=24 dan lora_alpha=48 menghasilkan performa terbaik secara kuantitatif dengan skor BLEU-4 sebesar 16.57, mengungguli Skenario 1 (15.27) dan Skenario 2 (13.39). Hasil evaluasi pengguna mengindikasikan bahwa chatbot mampu menangani pertanyaan rutin secara efektif dengan performa yang sebanding dengan agen manusia, sehingga pendekatan hybrid antara chatbot dan agen manusia direkomendasikan untuk mengoptimalkan layanan secara menyeluruh.
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