CD Skripsi

Optimalisasi Aplikasi Chatbot Feminacare Menggunakan Model Seallm

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ABSTRACT
Feminacare, a women's health consultation application with a chatbot, previously used the LSTM model which had an accuracy of 61% but often gave less relevant responses. This research proposes the use of SeaLLM model with Retrieval-Augmented Generation (RAG) approach to improve the accuracy of the chatbot. Based on three trials in the evaluation of 120 questions, the chatbot obtained a mean accuracy of 87%, with a precision of 93%, recall of 89%, and F1-score of 91%. Compared to previous models, this approach produced more relevant and accurate responses. Overall, this research proves that the application of SeaLLM with RAG can improve the effectiveness of chatbots in providing women's health information. However, further improvements are still needed, especially in handling more complex questions by expanding the dataset.
Keywords: Chatbot, SeaLLM, Retrieval-Augmented Generation (RAG), Women's Health, Feminacare.

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

2107112734

Penerbit

Pekanbaru : Universitas Riau – F.TEKNIK – INFORMATIKA., 2025

Deskripsi Fisik

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Bahasa

Indonesia

ISBN/ISSN

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Klasifikasi

2107112734

Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

TEKNIK INFORMATIKA

Info Detail Spesifik

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Pernyataan Tanggungjawab

daus

Versi lain/terkait

Lampiran Berkas

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