Penggunaan Chatbot pada Sistem Informasi Buku Berbasis Web Menggunakan Metode Natural Language Processing (NLP)
DOI:
https://doi.org/10.36050/0b2p5329Keywords:
chatbot, accurasy, natural language processing, book information system, libraryAbstract
Advances in information technology require fast and effective information retrieval services,
including in library book information systems. Manually searching for books in catalogs is often time
consuming and slow to respond, especially as the number of books in libraries increases. This problem
highlights the importance of creating an automated system that can provide information directly and timely.
This research aims to create a web-based chatbot that can provide information about books, using Natural
Language Processing (NLP) techniques to make information retrieval more effective. The research method
includes analyzing system requirements, designing the system structure, implementing a natural language
processing model with text preprocessing steps such as tokenization, case adjustment, and stemming, and
testing the system's capabilities. The system was developed using a prototype method to suit user needs.
Evaluation was conducted by testing the accuracy of answers and checking the level of user satisfaction.
Testing showed that the chatbot can provide information about the title, author, publisher, year of publication,
and location of a book with a 90% accuracy rate, measured by the extent to which the answers provided by the chatbot match the user's questions. Furthermore, user test results indicate that this method is more efficient in
saving search time than previous methods. So the use of NLP-based chatbots has been proven to help improve
the quality and speed of providing information about books in libraries.
References
[1] T. E. Putri and G. Ramadhan, “Penerapan Chatbot sebagai Alat Pembelajaran untuk Pengembangan Pendidikan Karakter,” 2024.
[2] T. E. Putri and G. Ramadhan, “Penerapan Chatbot sebagai Alat Pembelajaran untuk Pengembangan Pendidikan Karakter,” 2024.
[3] S. N. F. Aziz, N. D. Nathasia, and C. R. Hassolthine, “Pembuatan Aplikasi Chatbot Faq Untuk Motionbank,” Journal of Information System, Applied, Management, Accounting and Research, vol. 8, no. 1, p. 118, Feb. 2024, doi: 10.52362/jisamar.v8i1.1415.
[4] nauvaliana, “1454-3761-2-PB (1),” Feb. 2022.
[5] V. R. Prasetyo, N. Benarkah, and V. J. Chrisintha, “Implementasi Natural Language Processing Dalam Pembuatan Chatbot Pada Program Information Technology Universitas Surabaya,” Teknika, vol. 10, no. 2, pp. 114–121, Jul. 2021, doi: 10.34148/teknika.v10i2.370.
[6] Harifa nu’aeni, “FINAL PROJECT DEVELOPMENT OF A CHATBOT FOR THE DIGITAL LIBRARY INFORMATION SYSTEM (DIGILIB) USING HARIFA NU’AENI NIM 32602100141,” 2025.
[7] S. Nova, N. Khotimah, and M. Y. Aryati Wahyuningrum, “PEMANFAATAN CHATBOT MENGGUNAKAN NATURAL LANGUAGE PROCESSING UNTUK PEMBELAJARAN DASAR-DASAR GUI TKINTER PADA BAHASA PEMROGRAMAN PYTHON,” 2024.
[8] B. Hakim, “Analisa Sentimen Data Text Preprocessing Pada Data Mining Dengan Menggunakan Machine Learning,” JBASE - Journal of Business and Audit Information Systems, vol. 4, no. 2, Aug. 2021, doi: 10.30813/jbase.v4i2.3000.
[9] N. Febriyani, A. Mardiyah, S. Data, and U. PGRI Sumatera Barat, “Volume 2 ; Nomor 8,” Agustus, pp. 142–150, 2024, doi: 10.59435/gjmi.v2i8.810.
[10] A. Nazilah, H. Rahmawati, and Z. Ulkhaq, “IMPLEMENTASI CHATBOT UNTUK PELAYANAN AKADEMIK PADA STIMIK TUNAS BANGSA MENGGUNAKAN DIALOGFLOW API.”
[11] A. Dwiyono, A. Abdiansah, and M. Fachrurrozi, “Analisis Perbandingan Klasifikasi Intent Chatbot Menggunakan Deep Learning BERT, RoBERTa, dan IndoBERT,” Journal of Information System Research (JOSH), vol. 6, no. 1, pp. 595–606, Oct. 2024, doi: 10.47065/josh.v6i1.6051.
[12] I. Steven Andrew and S. Hariyanto, “Rancangan Bangun Aplikasi Chatbot untuk Proses Pemesanan di Digital Printing dengan Metode Natural Language Processing (NLP) menggunakan Python,” 2024. [Online]. Available: https://jurnal.buddhidharma.ac.id/index.php/algor/index
[13] R. Gupta, “Bidirectional encoders to state-of-the-art: a review of BERT and its transformative impact on natural language processing,” Информатика. Экономика. Управление - Informatics. Economics. Management, vol. 3, no. 1, pp. 0311–0320, Mar. 2024, doi: 10.47813/2782-5280-2024-3-1-0311-0320.
[14] A. A. Chandra, V. Nathaniel, F. R. Satura, F. Dharma Adhinata, and P. Studi, “Pengembangan Chatbot Informasi Mahasiswa Berbasis Telegram dengan Metode Natural Language Processing.”
[15] Mohammad luqi wiharto, “SISTEM CHATBOT SEBAGAI LAYANAN TUGAS AKHIR PROGRAM STUDI TEKNIK INFORMATIKA UNISSULA MENGGUNAKAN METODE Bidirectional Encoder Representations from Transformers (BERT),” 2024.
[16] “Implementasi Chatbot dengan pendekatan Natural Language Processing dan Naïve Bayes dalam meningkatkan layanan perusahaan _ Sebayang _ Kesatria _ Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)”.
[17] A. Nazilah, H. Rahmawati, and Z. Ulkhaq, “IMPLEMENTASI CHATBOT UNTUK PELAYANAN AKADEMIK PADA STIMIK TUNAS BANGSA MENGGUNAKAN DIALOGFLOW API.”
[18] D. Heryati, I. Zulkifli, R. M. Fajri, and S. Kom, “APLIKASI CHATBOT UNTUK PENERIMAAN MAHASISWA BARU UNIVERSITAS INDO GLOBAL MANDIRI MENGGUNAKAN DEEP LEARNING.”
[19] S. Petrus Wijaya and S. Hariyanto, “Perancangan Chatbot dengan Metode Natural Language Processing (NLP) dalam Proses Booking Order di Carwash,” 2021.
[20] Alfiyatu nurinayah, “HALAMAN JUDUL IMPLEMENTATION OF FINE-TUNING TO PREDICTE SOLUTION SENTENCES FROM PROBLEM SENTENCES IN SCIENTIFIC ARTICLES USING LARGE LANGUAGE MODELS (LLM) MODEL,” 2025.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 M. Rezki Hamdani, Nurjayadi, Unang Rio, Muhamad Jamaris (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
