KLASIFIKASI TEKS BERBASIS LONG SHORT-TERM MEMORY UNTUK CHATBOT KONSELING GANGGUAN KECEMASAN SOSIAL
Abstract
Chatbot adalah teknologi kecerdasan buatan yang dapat melakukan percakapan seperti manusia melalui teks atau suara. Sistem chatbot berupa tanya jawab dapat membantu kegiatan manusia, konsultasi terhadap suatu masalah bahkan menawarkan solusi untuk masalah medis. Setiap manusia memiliki tingkat kecemasan. Kecemasan yang berlebihan hingga gangguan kecemasan sosial dapat mengganggu aktivitas. Oleh karena itu, chatbot dapat menjadikan solusi menjadi pendengar dari sebuah masalah. Berdasarkan permasalahan diatas, pada penelitian ini dilakukan klasifikasi teks untuk konseling chatbot. Klasifikasi teks menggunakan Long Short-Term Memory (LSTM). Pemodelan teks pre-processing menggunakan modifikasi dataset ISEAR serta tanggapan dari beberapa orang. Metode LSTM yang diusulkan yaitu memetakan jawaban pengguna dari chatbot berdasarkan kategori label. Sistem dilatih menggunakan dataset berupa teks. Dataset dibuat berupa jawaban dari pengguna diberi label positif dan negatif dengan 70 training data. Hasil penelitian ini, model epoch 4 memiliki konfigurasi terbaik yaitu RMSprop learning rate 0,001 dengan test accuracy 85,71%. Sedangkan pada model epoch 6 memiliki konfigurasi terbaik yaitu RMSprop learning rate 0,01 dengan test accuracy 89,29%. Selain itu, parameter performansi pada epoch 4 rata-rata precision 97%, recall 97%, dan f1-score 97%. Kemudian parameter performansi pada epoch 6 rata-rata precision 97%, recall 97%, dan f1-score 97%.
Kata Kunci: chatbot, gangguan kecemasan sosial, long short-term memory.
References
A. M. Kring, S. L. Johnson, G. C. Davison, and J. M. Neale, Abnormal
Psychology, 12th Edition, 12th ed. Wiley, 2012.
E. P. N. Hasibuan, W. Srisayekti, and M. F. Moeliono, “GAMBARAN
KECEMASAN SOSIAL BERDASARKAN LIEBOWITZ SOCIAL ANXIETY
SCALE (LSAS) PADA REMAJA AKHIR DI BANDUNG,” Universitas
Padjadjaran, 2015.
B. Setiaji and F. W. Wibowo, “Chatbot Using a Knowledge in
Database: Human-to-Machine Conversation Modeling,” Proc. - Int.
Conf. Intell. Syst. Model. Simulation, ISMS, vol. 0, pp. 72–77, 2016,
doi: 10.1109/ISMS.2016.53.
T. Fujita, W. Bai, and C. Quan, “Long short-term memory networks for
automatic generation of conversations,” Proc. - 18th IEEE/ACIS Int.
Conf. Softw. Eng. Artif. Intell. Netw. Parallel/Distributed Comput.
SNPD 2017, pp. 483–487, 2017, doi: 10.1109/SNPD.2017.8022766.
T. A. Zuraiyah et al., “Mahasiswa Baru Menggunakan Recurrent
Neural Network,” pp. 91–101.
M. B. Stein and D. J. Stein, “Social anxiety disorder,” Lancet, vol. 371,
no. 9618, pp. 1115–1125, 2008, doi: 10.1016/S0140-
(08)60488-2.
X. Liu, “Artificial intelligence and modern sports education
technology,” Proc. - 2010 Int. Conf. Artif. Intell. Educ. ICAIE 2010, pp.
–776, 2010, doi: 10.1109/ICAIE.2010.5641441.
A. Khanna, B. Pandey, K. Vashishta, K. Kalia, B. Pradeepkumar, and T.
Das, “A Study of Today’s A.I. through Chatbots and Rediscovery of
Machine Intelligence,” Int. J. u- e-Service, Sci. Technol., vol. 8, no. 7,
pp. 277–284, 2015, doi: 10.14257/ijunesst.2015.8.7.28.
S. V. Doshi, S. B. Pawar, A. G. Shelar, and S. S. Kulkarni, “Artificial
Intelligence Chatbot in Android System using Open Source
Program-O,” Ijarcce, vol. 6, no. 4, pp. 816–821, 2017, doi:
17148/ijarcce.2017.64151.
A. M. Rahman, A. Al Mamun, and A. Islam, “Programming challenges
of chatbot: Current and future prospective,” in 2017 IEEE Region 10
Humanitarian Technology Conference (R10-HTC), Dec. 2017, pp. 75–
, doi: 10.1109/R10-HTC.2017.8288910.
S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural
Comput., vol. 9, no. 8, pp. 1735–1780, 1997, doi:
1162/neco.1997.9.8.1735.
C. Olah, “Understanding LSTM Networks,” 2015.
https://colah.github.io/posts/2015-08-Understanding-LSTMs/
(accessed Oct. 28, 2020).
M. Wildan, P. Aldi, and A. Aditsania, “Analisis dan Implementasi Long
Short Term Memory Neural Network untuk Prediksi Harga Bitcoin,”
e-Proceeding Eng., vol. 5, no. 2, pp. 3548–3555, 2018.
R. F. Rahmadzani, “Cara Kerja Long Short-Term Memory (LSTM),” Feb.
, 2021. https://rifqifai.com/cara-kerja-long-short-term-memory-
lstm/ (accessed Oct. 04, 2021).
disorder,” Lancet, vol. 371, no. 9618, pp. 1115–1125, 2008, doi: 10.1016/S0140-6736(08)60488-2.
X. Liu, “Artificial intelligence and modern sports education technology,” Proc. - 2010 Int. Conf. Artif. Intell. Educ. ICAIE 2010, pp. 772–776, 2010, doi: 10.1109/ICAIE.2010.5641441.
A. Khanna, B. Pandey, K. Vashishta, K. Kalia, B. Pradeepkumar, and T. Das, “A Study of Today’s A.I. through Chatbots and Rediscovery of Machine Intelligence,” Int. J. u- e-Service, Sci. Technol., vol. 8, no. 7, pp. 277–284, 2015, doi: 10.14257/ijunesst.2015.8.7.28.
S. V. Doshi, S. B. Pawar, A. G. Shelar, and S. S. Kulkarni, “Artificial Intelligence Chatbot in Android System using Open Source Program-O,” Ijarcce, vol. 6, no. 4, pp. 816–821, 2017, doi: 10.17148/ijarcce.2017.64151.
A. M. Rahman, A. Al Mamun, and A. Islam, “Programming challenges of chatbot: Current and future prospective,” in 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), Dec. 2017, pp. 75–78, doi: 10.1109/R10-HTC.2017.8288910.
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.
C. Olah, “Understanding LSTM Networks,” 2015. https://colah.github.io/posts/2015-08-Understanding-LSTMs/ (accessed Oct. 28, 2020).
M. Wildan, P. Aldi, and A. Aditsania, “Analisis dan Implementasi Long Short Term Memory Neural Network untuk Prediksi Harga Bitcoin,” e-Proceeding Eng., vol. 5, no. 2, pp. 3548–3555, 2018.
R. F. Rahmadzani, “Cara Kerja Long Short-Term Memory (LSTM),” Feb. 21, 2021. https://rifqifai.com/cara-kerja-long-short-term-memory-lstm/ (accessed Oct. 04, 2021).
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