Perkembangan teknologi Artificial Intelligence (AI) mendorong adopsi chatbot berbasis Natural Language Processing (NLP) untuk meningkatkan layanan informasi. Penelitian ini bertujuan mengimplementasikan chatbot menggunakan model Bidirectional Encoder Representations from Transformers (BERT) guna meningkatkan efisiensi layanan konsultasi di Praktek Dokter Gigi drg. Fahmi Nurdin. Metode pengembangan sistem yang digunakan adalah Agile, dengan tahapan mulai dari analisis kebutuhan, perancangan antarmuka, pelatihan model, hingga implementasi chatbot. Dataset berupa pertanyaan dan jawaban disusun dalam format JSON, kemudian digunakan untuk melatih model BERT dalam klasifikasi intent dan penyusunan respons. Hasil evaluasi menunjukkan bahwa chatbot mampu memberikan respon yang relevan dan akurat, serta mempercepat proses konsultasi awal pasien. Implementasi ini diharapkan dapat membantu mengatasi keterbatasan sumber daya manusia, meningkatkan kualitas layanan, dan memperkuat kepercayaan pasien terhadap layanan klinik.

The rapid advancement of Artificial Intelligence (AI) has driven the adoption of chatbots powered by Natural Language Processing (NLP) to improve the quality and accessibility of information services. This study focuses on the implementation of a chatbot utilizing the Bidirectional Encoder Representations from Transformers (BERT) model to enhance the efficiency of consultation services at the dental clinic of drg. Fahmi Nurdin. The system was developed using the Agile methodology, encompassing stages such as requirements analysis, user interface design, model training, and chatbot deployment. A dataset comprising question-and-answer pairs in JSON format was used to train the BERT model for intent classification and response generation. Evaluation results indicate that the chatbot is capable of delivering accurate and relevant responses, significantly improving the speed of initial consultations. This implementation is expected to help address human resource limitations, improve service quality, and strengthen patient trust in the clinic's consultation services.