Email this article Implementation of Chatbot System on Tourism Objects in Banyumas Regency with AIML and Chatterbot
Abstract
Information technology can be applied to all fields, including tourism. Tourism object information media can be implemented into the chatbot system to make the information search process more flexible. Currently, searching for tourist spot information is often done manually; this makes tourist information services limited in time, while the need for tourism information must always be available. This research aims to build a chatbot system using Artificial Intelligence Markup Language (AIML) and ChatterBot methods. Both methods are accessed from libraries in Python using input in the form of natural language that has been processed into certain patterns. The pattern determination process is carried out by classifying a collection of questions on the chatbot using the Support Vector Machines (SVM) method. Then the classification is divided into five attributes, namely address, ticket price, facilities, description, and access. The SVM model built obtained an accuracy rate of 88%. Based on the testing results on both models that have been tested, the approach with AIML results in an accuracy rate in answering questions correctly of 90%, while ChatterBot is 40%.
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R. M. S. Adi and S. Sudianto, “Prediksi Harga Komoditas Pangan Menggunakan Algoritma Long Short-Term Memory ( LSTM ),” vol. 4, no. 2, pp. 1137–1145, 2022, doi: 10.47065/bits.v4i2.2229.
S. Sudianto, “Analisis Kinerja Algoritma Machine Learning Untuk Klasifikasi Emosi,” vol. 4, no. 2, pp. 1027–1034, 2022, doi: 10.47065/bits.v4i2.2261.
Sudianto, Y. Herdiyeni, and L. B. Prasetyo, “Machine learning for sugarcane mapping based on segmentation in cloud platform,” presented at the The 3rd International Conference on Engineering, Technology and Innovative Researches, Purwokerto, Indonesia, 2023, p. 020001. doi: 10.1063/5.0132180.
S. Sudianto and R. D. Wahyuningrum, “Identifikasi Sebaran Nitrogen pada Tanaman Padi Berbasis Pengetahuan Fenologi dan Remote Sensing,” Jurnal Nasional Pendidikan Teknik Informatika (Janapati), vol. 11, no. 3, pp. 166–175, 2022.
Sudianto, Y. Herdiyeni, A. Haristu, and M. Hardhienata, “Chilli quality classification using deep learning,” in 2020 International Conference on Computer Science and Its Application in Agriculture, ICOSICA 2020, 2020. doi: 10.1109/ICOSICA49951.2020.9243176.
S. Sudianto, J. A. Marseli, N. Nugroho, R. W. A. Rumpoko, and Z. Akhmad, “Comparison of Support Vector Machines and K-Nearest Neighbor Algorithm Analysis of Spam Comments on YouTube Covid Omicron,” JTI, vol. 15, no. 2, pp. 110–118, doi: https://doi.org/10.15408/jti.v15i2.24996.
W. Afandi, S. N. Saputro, A. M. Kusumaningrum, H. Ardiansyah, M. H. Kafabi, and S. Sudianto, “Klasifikasi Judul Berita Clickbait menggunakan RNN-LSTM,” Jurnal Pengembangan IT, vol. 7, no. 2, pp. 85–89, 2022.
S. Sudianto, A. D. Sripamuji, I. Ramadhanti, R. R. Amalia, J. Saputra, and B. Prihatnowo, “Penerapan Algoritma Support Vector Machine dan Multi-Layer Perceptron pada Klasisifikasi Topik Berita,” vol. 11, no. 2, pp. 84–91, 2022.
S. Sudianto, P. Wahyuningtias, H. W. Utami, U. A. Raihan, and H. N. Hanifah, “Comparison Of Random Forest And Support Vector Machine Methods On Twitter Sentiment Analysis ( Case Study : Internet Selebgram Rachel Vennya Escape From Quarantine ) Perbandingan Metode Random Forest Dan Support Vector Machine Pada Analisis Sentimen Twitt,” Jutif, vol. 3, no. 1, pp. 141–145, 2022.
S. Chandra Ayunda Apta, N. Trivetisia, N. A. Winanti, D. P. Martiyaningsih, T. W. Utami, and S. Sudianto, “Analisis Komparasi Algoritma Machine Learning untuk Sentiment Analysis (Studi Kasus: Komentar YouTube ‘Kekerasan Seksual’),” Jurnal Pengembangan IT, vol. 7, no. 2, pp. 80–84, 2022.
Muhajjirsyah, Mursyidah, and Jamilah, “Pembuatan Chatbot Bahasa Aceh Menggunakan Artificial Intelligence Markup Language,” Jurnal Infomedia : Teknik Informatika, Multimedia, dan Jaringan, vol. 4, no. 1, pp. 42–49, 2019.
D. Suryani and E. L. Amalia, “Aplikasi Chatbot Objek Wisata Jawa Timur Berbasis AIML.” 2017.
R. Z. Ramadhani, H. Rusdianto, and V. Yahya, “Penerapan Chatbot Menggunakan Aiml Berbasis Android Pada Smk,” Jurnal Teknik Informatika, vol. 1, no. 1, pp. 27–33, 2019.
W. Afandi, S. N. Saputro, A. M. Kusumaningrum, H. Ardiansyah, M. H. Kafabi, and S. Sudianto, “Klasifikasi Judul Berita Clickbait menggunakan RNN-,” Jurnal Pengembangan IT, vol. 7, no. 2, pp. 85–89, 2022.
I. Muhammad and Z. Yan, “Supervised Machine Learning Approaches: a Survey,” ICTACT Journal on Soft Computing, vol. 05, no. 03, pp. 946–952, 2015, doi: 10.21917/ijsc.2015.0133.
B. Setiaji, E. Utami, and H. Al Fatta, “Membangun Chatbot Berbasis AIML dengan Arsitektur Pengetahuan Modular,” Semnasteknomedia Online, vol. 1, no. 1. pp. 18–15, 2013.
N. Science, G. Bishwabidyalay, and S. Al Mamun, “Review of Integrated Applications with AIML Based Chatbot,” 1st International Conference on Computer & Information Engineering, pp. 87–90, 2015.
O. V. Deryugina, “Chatterbots,” Scientific and Technical Information Processing, vol. 37, no. 2, pp. 143–147, 2010, doi: 10.3103/S0147688210020097.
DOI: https://doi.org/10.32528/elkom.v5i2.18615
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