Chatbot Application “Konco Ngobrol” in Ngapak Javanese Using LSTM Model Approach
DOI:
https://doi.org/10.34148/teknika.v14i2.1258Keywords:
Chatbot, Flask, Fuzzy Matching, LSTM, Ngapak JavaneseAbstract
Regional languages are an important part of cultural identity, increasingly marginalized in the digital era. Ngapak Javanese, a local dialect, is declining in use in Banyumas, Tegal, Purbalingga, and nearby areas. This research aims to develop an interactive text-based chatbot named Konco Ngobrol that uses Ngapak Javanese as the main conversational language. This chatbot is built using the Long Short-Term Memory (LSTM) model approach, which excels at understanding conversation context in the form of sequential data. The dataset is composed of many sentence patterns with a mixed language style of Indonesian and Javanese Ngapak. The research stages include data collection, pre-processing (tokenization, padding, and label encoding), building and training the LSTM model, model evaluation, prediction using a combination of LSTM and fuzzy matching, and deployment into a Flask-based application with an interactive chat interface. The evaluation results show that the LSTM model can achieve an accuracy of up to 99.04% with a low loss value, and can provide contextually appropriate responses. The fallback system also successfully handled unknown inputs well through fuzzy logic. The Konco Ngobrol chatbot not only serves as a medium for entertainment and communication but also as a tool for preserving digital culture by reintroducing local dialects into the modern technology ecosystem.
Downloads
References
[1] Arsanti, “Pudarnya Pesona Bahasa Indonesia di Media Sosial (Sebuah Kajian Sosiolinguistik Penggunaan Bahasa Indonesia),” Ling. Fr. Bahasa, Sastra, dan Pengajarannya, vol. 4, no. 1, pp. 1–12, 2020, [Online]. Available: http://103.114.35.30/index.php/lingua/article/view/4314
[2] T. R. Gatikasari Mujiastuti, “Identitas Dialek Banyumasan Sebagai Konstruksi Budaya Studi Penggunaan Dialek Banyumasan Di Kalangan Penutur Asli Banyumas Yang Berada Di Semarang,” pp. 1–23, 2023.
[3] C. Nugroho and I. P. Kusuma, “Identitas Budaya Banyumasan dalam Dialek Ngapak,” J. Ilmu Komun., vol. 21, no. 2, p. 333, 2023, doi: 10.31315/jik.v21i2.4556.
[4] Isrofiah Laela Khasanah and Heri Kurnia, “Melestarikan Budaya Banyumasan Melalui Dialek Bahasa Ngapak,” Kulturist. J. Bhs. dan Budaya, vol. 7, no. 2, pp. 43–53, 2023, doi: 10.22225/kulturistik.7.2.7135.
[5] A. G. Pawestri, “Membangun Identitas Budaya Banyumasan Melalui Dialek Ngapak Di Media Sosial,” J. Pendidik. Bhs. dan Sastra, vol. 19, no. 2, pp. 255–266, 2020, doi: 10.17509/bs_jpbsp.v19i2.24791.
[6] E. Adamopoulou and L. Moussiades, An Overview of Chatbot Technology, vol. 584 IFIP. Springer International Publishing, 2020. doi: 10.1007/978-3-030-49186-4_31.
[7] H. Jelodar, Y. Wang, R. Orji, and S. Huang, “Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach,” IEEE J. Biomed. Heal. Informatics, vol. 24, no. 10, pp. 2733–2742, 2020, doi: 10.1109/JBHI.2020.3001216.
[8] A. Elcholiqi and A. Musdholifah, “Chatbot in Bahasa Indonesia using NLP to Provide Banking Information,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 14, no. 1, p. 91, 2020, doi: 10.22146/ijccs.41289.
[9] H. Mustafidah, S. Suwarsito, and T. Pinandita, “Natural Language Processing for Mapping Exam Questions to the Cognitive Process Dimension,” Int. J. Emerg. Technol. Learn., vol. 17, no. 13, pp. 4–16, 2022, doi: 10.3991/ijet.v17i13.29095.
[10] S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, 1997, doi: 10.1162/neco.1997.9.8.1735.
[11] F. Zakariya, J. Zeniarja, and S. Winarno, “Pengembangan Chatbot Kesehatan Mental Menggunakan Algoritma Long Short-Term Memory,” J. Media Inform. Budidarma, vol. 8, no. 1, p. 251, 2024, doi: 10.30865/mib.v8i1.7177.
[12] H. A. F. Muhyidin and L. Venica, “Pengembangan Chatbot untuk Meningkatkan Pengetahuan dan Kesadaran Keamanan Siber Menggunakan Long Short-Term Memory,” J. Inform. dan Rekayasa Perangkat Lunak, vol. 5, no. 2, p. 152, 2023, doi: 10.36499/jinrpl.v5i2.8818.
[13] H. Rumapea, “Deteksi Kemiripan Artikel Melalui Keywords Dengan Metode Fuzzy String Matching Dalam Natural Language Processing,” METHOMIKA J. Manaj. Inform. dan Komputerisasi Akunt., vol. 5, no. 1, pp. 60–66, 2021, doi: 10.46880/jmika.vol5no1.pp60-66.
[14] F. Aulia, A. Farisi, R. S. Perdana, P. P. Adikara, U. Brawijaya, and P. Korespondensi, “Klasifikasi Intensi Dengan Metode Long Short-Term Memory Intent Classification With Long Short-Term Memory Method in Indonesian Language Chatbot,” vol. 11, no. 5, pp. 1051–1058, 2024, doi: 10.25126/jtiik.2024118000.
[15] A. Fariz Zulhilmi, R. Setya Perdana, and P. Korespondensi, “Pengenalan Entitas Bernama Menggunakan Bi-Lstm Pada Chatbot Bahasa Indonesia Named Entity Recognition Using Bi-LSTM in Indonesian Language Chatbot,” J. Teknol. Inf. dan Ilmu Komput., vol. 10, no. 7, pp. 1425–1430, 2023, doi: 10.25126/jtiik.2024117968.
[16] P. B. Wintoro, H. Hermawan, M. A. Muda, and Y. Mulyani, “Implementasi Long Short-Term Memory pada Chatbot Informasi Akademik Teknik Informatika Unila,” Expert J. Manaj. Sist. Inf. dan Teknol., vol. 12, no. 1, p. 68, 2022, doi: 10.36448/expert.v12i1.2593.
[17] F. Deby Fambayun, G. Asrofi Buntoro, and F. Masykur, “Penerapan Algoritma Neural Network Pada Chatbot Bahasa Jawa Tingkat Tutur Krama Alus,” J. Tek. Inform., vol. 14, no. 1, pp. 40–46, 2022.
[18] Zhaozhen Xu, “Prevent the Vanishing Gradient Problem with LSTM,” Baeldung. Accessed: Jan. 07, 2025. [Online]. Available: https://www.baeldung.com/cs/lstm-vanishing-gradient-prevention
[19] Ethan Nam, “Understanding the Levenshtein Distance Equation for Beginners,” medium.com. Accessed: May 01, 2025. [Online]. Available: https://medium.com/@ethannam/understanding-the-levenshtein-distance-equation-for-beginners-c4285a5604f0
[20] M. S. M. Rudwan and J. V. Fonou-Dombeu, “Hybridizing Fuzzy String Matching and Machine Learning for Improved Ontology Alignment,” Futur. Internet, vol. 15, no. 7, 2023, doi: 10.3390/fi15070229.
[21] H. Gadge, “A Chatbot for Medical Purpose using Deep Learning,” vol. 10, no. 05, pp. 441–447, 2021.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Teknika
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
