Implementation of the Long Short-Term Memory Algorithm in a Chatbot for Dengue Fever Information and Education Services

Heru Budianto, Fahmi Yusuf, Dede Irawan, Muhamad Akhirul Sidik, Aiena Nurhasanah, Sabrina Mauldya, Silmi Nur Afifah

Abstract


Dengue fever is one of the most common infectious diseases in Indonesia. However, information regarding its prevention and treatment remains fragmented, with limited accessibility and availability. This study developed a chatbot integrating the Long Short-Term Memory (LSTM) algorithm to answer various questions about dengue fever, including its symptoms, prevention, and treatment. The dataset used consists of questions and answers related to dengue fever, sourced from both primary and secondary data. The data undergoes a series of preprocessing steps before being used for model development, training, and evaluation. The test results indicate that the developed model achieved an accuracy of 100% during validation with a loss function value of 0.0221. These findings demonstrate that the LSTM-based chatbot can provide accurate and relevant responses, making it an effective tool for educating the public in an interactive and efficient manner. This implementation is expected to offer an innovative solution for increasing public awareness of dengue fever prevention and management.

Keywords


demam berdarah;Long Short-Term Memory;Chatbot;layanan

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References


E. Asih, V. T. Putri, N. Lusida, A. Mallongi, N. Latifah, F. Fajrini, and H. SG., “Analisis Variasi Iklim dengan Kejadian Demam Berdarah Dengue (DBD) di Kota Tangerang Selatan,” J. Kedokt. dan Kesehat., vol. 19, no. 1, p. 33, 2023, doi: 10.24853/jkk.19.1.33-41.

D. Nurmumpuni, B. Kurniawan, and Suharmanto, “Efektivitas Program Pemberantasan Penyakit Demam Berdarah Dengue,” J. Penelit. Perawat Prof., vol. 6, no. 3, pp. 1377–1386, 2024.

C. Rahmawati, B. Leny Nopitasari, A. Kusuma Wardani, B. Nurbaety, B. Lenysia Puspita Anjani, M. Permata Hati, N. Furqani, A. Rahman Wahid, I. Hendriyani, A. Pradiningsih, Y. Fitriana, D. Haryadi Ittiqo, “Edukasi Mencegah Penyakit Demam Berdarah Dengue (DBD) pada Masyarakat Lingkungan Dasan Sari Ampenan,” J. Pengabdi. Masy. Berkemajuan, vol. 6, no. 4, pp. 3038–3042, 2022.

L. Indahsari, K. Kusnadi, and T. E. Putri, “Rancang Bangun LINE Chatbot Informasi dan Edukasi Kesehatan Mental menggunakan Algoritma Jaro Winkler,” J. Eksplora Inform., vol. 10, no. 2, pp. 68–79, 2021, doi: 10.30864/eksplora.v10i2.428.

M. Mustaqim, A. Gunawan, Y. B. Pratama, and I. Zaliman, “Pengembangan Chatbot Layanan Publik menggunakan Machine Learning dan Natural Languange Processing,” J. Inf. Technol. Soc., vol. 1, no. 1, pp. 1–4, 2023.

L. Cui, “SuperAgent : A Customer Service Chatbot for E-commerce Websites,” pp. 97–102, 2017.

J. Lee and W. Maeng, “Designing a Chatbot for Survivors of Sexual Violence : Exploratory Study for Hybrid Approach Combining Rule-based Chatbot and ML-based Chatbot,” in Asian CHI Symposium, 2021, pp. 160–166, doi: 10.1145/3429360.3468203.

M. R. Suherlan, A. Asriyanik, and A. Pambudi, “UMMIBOT sebagai Media Layanan Informasi Penerimaan Mahasiswa Baru Universitas Muhammadiyah Sukabumi,” J. Inform. Terpadu, vol. 9, no. 2, pp. 82–91, 2023, doi: 10.54914/jit.v9i2.893.

Sutarni, E. Prasetyo, and L. E. Sudiati, “Evaluasi Kinerja Chatbot dengan Integrasi Algoritma RNN dan LSTM dalam Optimalisasi Respon Percakapan pada Sistem PMB,” SOSCIED, vol. 7, no. 2, 2024.

G. A. Pratama, N. C. Wiboeo, A. Pratama, “Implementasi Chatbot Konsultasi Karier berbasis Deep Feed-Forward Neural Network menggunakan,” J. Tek. Inform. dan Sist. Inf., vol. 11, no. 3, pp. 248–260, 2024.

S. P. Afrisia, F. M. Hana, and W. C. Wahyudin, “Implementasi Metode Long Short Term Memory ( LSTM ) pada Chatbot Kesehatan Mental Mahasiswa,” Sainteks, vol. 21, no. 2, pp. 107–116, 2024, doi: 10.30595/sainteks.v21i2.23869.

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.

Y. S. H. Langgeng, E. I. Setiawan, S. Imron, and J. Santoso, “Long Short-Term Memory-based Chatbot for Vocational Registration Information Services,” J. Appl. Data Sci., vol. 4, no. 4, pp. 414–430, 2023, doi: 10.47738/jads.v4i4.128.

I. D. Raharjo and E. R. Subhiyakto, “Implementing Long Short Term Memory (LSTM) in Chatbots for Multi Usaha Raya,” Adv. Sustain. SCI. Eng. Technol., vol. 6, no. 4, pp. 1–8, 2024, doi: 10.26877/asset.v6i4.934.

A. Adil, Metode Penelitian Kuantitatif dan Kualitatif: Teori dan Praktik, no. January. 2023.

F. Y. Fiddin, A. Komarudin, and M. Melina, “Chatbot Informasi Penerimaan Mahasiswa Baru menggunakan Metode FastText dan LSTM,” J. Appl. Comput. Sci. Technol., vol. 5, no. 1, pp. 33–39, 2024, doi: 10.52158/jacost.v5i1.648.




DOI: https://doi.org/10.32520/stmsi.v14i2.5060

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