Pengembangan Chatbot RAG dengan Hybrid Retrieval untuk Informasi Penyakit Menular di Indonesia
DOI:
https://doi.org/10.54065/artificial.1992Keywords:
Chatbot Kesehatan, Hybrid Retrieval, Large Language Model, Penyakit Menular, Retrieval-Augmented GenerationAbstract
Ketersediaan informasi mengenai penyakit menular yang mudah diakses dan relevan menjadi salah satu kebutuhan dalam pemanfaatan teknologi digital di bidang kesehatan. Namun, chatbot berbasis Large Language Model (LLM) dapat menghasilkan respons yang kurang sesuai dengan sumber pengetahuan apabila tidak didukung mekanisme pengambilan informasi yang relevan. Penelitian ini bertujuan mengembangkan chatbot berbasis Retrieval-Augmented Generation (RAG) dengan pendekatan hybrid retrieval untuk menyediakan informasi penyakit menular di Indonesia secara kontekstual. Penelitian menggunakan pendekatan pengembangan sistem dengan basis pengetahuan yang bersumber dari Halodoc dan Alodokter, mencakup delapan penyakit menular, yaitu TBC, DBD, HIV/AIDS, influenza, campak, cacar air, malaria, dan COVID-19. Proses retrieval menggabungkan pencarian semantik menggunakan Sentence Transformer dan pencarian berbasis kata kunci menggunakan TF-IDF, kemudian konteks yang diperoleh digunakan sebagai masukan dalam proses pembentukan respons oleh LLM. Hasil implementasi menunjukkan bahwa chatbot berhasil dikembangkan dalam bentuk aplikasi berbasis web dan mampu menerima pertanyaan serta menghasilkan respons sesuai dengan konteks informasi yang tersedia. Pengujian terhadap pertanyaan mengenai delapan penyakit menunjukkan bahwa sistem dapat memproses pertanyaan dan menampilkan respons, sedangkan pengujian backend menunjukkan proses sistem berjalan tanpa kesalahan. Dengan demikian, integrasi RAG dan hybrid retrieval dapat digunakan sebagai pendekatan untuk membangun chatbot informasi penyakit menular yang lebih terarah berdasarkan basis pengetahuan tertentu. Pengembangan selanjutnya diperlukan dengan memperluas cakupan sumber dan penyakit serta memperkuat evaluasi kualitas retrieval dan respons secara kuantitatif.
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