FEBRIAN, WAHYU (2025) OPTIMASI CHATBOT AI MENGGUNAKAN FRAMEWORK OLLAMA. S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
In the digital era, automated data processing based on AI chatbots has become a crucial component in various industries. This research aims to develop a chatbot capable of accepting CSV files as input and producing data in CSV format as output. The chatbot was developed using the Python programming language and the Ollama platform, which integrates large language models (LLMs). The method used involves a natural language processing (NLP) algorithm based on a transformer architecture. Data preprocessing is applied to recognize patterns in CSV files and map them to a structure understandable by the chatbot. Entity recognition and intent classification techniques are used to understand the context of user requests, while chunking and vectorization are used to optimize the information extraction process. Keywords: LLM, Chatbot, Ollama, Python. Dalam era digital, otomatisasi pemrosesan data berbasis chatbot AI menjadi komponen penting di berbagai industri. Penelitian ini bertujuan mengembangkan chatbot yang mampu menerima file CSV sebagai masukan dan menghasilkan data dalam format CSV sebagai keluaran. Chatbot dikembangkan menggunakan bahasa pemrograman Python dan platform Ollama yang mengintegrasikan model bahasa besar (LLM). Metode yang digunakan melibatkan algoritma pemrosesan bahasa alami (Natural Language Processing/NLP) berbasis arsitektur transformer. Proses prapemrosesan data diterapkan untuk mengenali pola dalam file CSV dan memetakannya ke struktur yang dapat dipahami chatbot. Teknik entity recognition dan intent classification digunakan untuk memahami konteks permintaan pengguna, sedangkan chunking dan vectorization digunakan untuk mengoptimalkan proses ekstraksi informasi. Kata Kunci: LLM, Chatbot, Ollama, Python.
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