SoulScripture: Chatbot using Bidirectional Encoder Representations from Transformers as a Medium of Spiritual Guidance

SoulScripture: Chatbot using Bidirectional Encoder Representations from Transformers as a Medium of Spiritual Guidance

Authors

  • Andhika Malik Department of Informatics, UIN Sunan Gunung Djati Bandung
  • Andhika Putra Gefadri Department of Informatics, UIN Sunan Gunung Djati Bandung
  • Elman Sidik Department of Informatics, UIN Sunan Gunung Djati Bandung
  • Alika Putie Syadrina Department of Informatics, UIN Sunan Gunung Djati Bandung

DOI:

https://doi.org/10.15575/kjrt.v2i1.822

Keywords:

AI, BERT, mental health , self-supervised learning, transformer, Quran, Hadith

Abstract

Mental health is an important aspect of human life. Many people face stress, anxiety, and distress daily without adequate support to manage these conditions. Islamic teachings from the Quran and Hadith provide wisdom as a source of inspiration and inner peace. However, accessing and understanding these teachings requires specialized knowledge and often the help of experts. With the advancement of machine learning, these teachings can be made more accessible and accurate. The SoulScripture app offers an innovative solution to support mental health by combining the wisdom of the Quran and Hadith through AI technology. Using the Bidirectional Encoder Representations from Transformers (BERT) model and Transformer architecture, the app can understand and provide relevant advice that anyone can access anytime. This research is significant because it offers a new approach to leveraging technology to support mental well-being, especially for communities underserved by conventional mental health services. The app was developed using self-supervised learning to understand the text of the Hadith without external annotation. This process involves several stages, such as user input, data preprocessing, and text analysis, to generate relevant answers. It is hoped that the SoulScripture application can serve as a source of inspiration and support for individuals in controlling stress and maintaining peace of mind, as well as supporting the achievement of the Sustainable Development Goals (SDGs) related to mental health.

References

D. Maulidiya, I. Musarofah, R. S. Hasnani, and A. N. Aeni, “Aplikasi HOT-Day: Hadits of the Day Sebagai Media Edukasi Pengamalan Hadits,” AL-HIKMAH (Jurnal Pendidikan dan Pendidikan Agama Islam), vol. 4, no. 1, pp. 57–65, 2022.

M. A. Bora, Ansarullah Lawi, I Made Sondra Wijaya, and Tia Andini Salsabilla, “Mengoptimalkan Kenyamanan Kognitif: Analisis Ergonomis terhadap Interaksi Pengguna dengan AI Chatbots,” Ranah Research : Journal of Multidisciplinary Research and Development, vol. 6, no. 4, pp. 710–723, Jun. 2024, doi: 10.38035/rrj.v6i4.869.

D. Spathis, I. Perez-Pozuelo, L. Marques-Fernandez, and C. Mascolo, “Breaking away from labels: The promise of self-supervised machine learning in intelligent health,” Patterns, vol. 3, no. 2, p. 100410, Feb. 2022, doi: 10.1016/j.patter.2021.100410.

P. E. Susanto, A. Kurniawardhan, D. H. Fudholi, and R. Rahmadi, “A Mobile Deep Learning Model on Covid-19 CT-Scan Classification,” International Journal of Artificial Intelligence Research, vol. 6, no. 2, Jul. 2022, doi: 10.29099/ijair.v6i1.257.

Z. Kaddari, Y. Mellah, J. Berrich, M. G. Belkasmi, and T. Bouchentouf, “Natural Language Processing: Challenges and Future Directions,” 2021, pp. 236–246. doi: 10.1007/978-3-030-53970-2_22.

J. Liu, X. Han, C. Deng, and J. Feng, “Robust Self-Supervised Learning with Contrast Samples for Natural Language Understanding,” in ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, Apr. 2024, pp. 10076–10080. doi: 10.1109/ICASSP48485.2024.10448238.

P. E. Susanto, A. Kurniawardhan, D. H. Fudholi, and R. Rahmadi, “A Mobile Deep Learning Model on Covid-19 CT-Scan Classification,” International Journal of Artificial Intelligence Research, vol. 6, no. 2, Jul. 2022, doi: 10.29099/ijair.v6i1.257.

H. K. K., A. K. Palakurthi, V. Putnala, and A. Kumar K., “Smart College Chatbot using ML and Python,” in 2020 International Conference on System, Computation, Automation and Networking (ICSCAN), IEEE, Jul. 2020, pp. 1–5. doi: 10.1109/ICSCAN49426.2020.9262426.

M. Muliyono and S. Sumijan, “Identifikasi Chatbot dalam Meningkatkan Pelayanan Online Menggunakan Metode Natural Language Processing,” Jurnal Informatika Ekonomi Bisnis, pp. 142–147, Sep. 2021, doi: 10.37034/infeb.v3i4.102.

M. Akmaluddin, “Diskursus Penelitian Al-quran dan Hadis dengan Ilmu Pengetahuan Modern,” in Seminar Nasional Hasil Penelitian dan Pengabdian Masyarakat UNIMUS 2017, Muhammadiyah University Semarang, 2020.

M. F. Afianto, Adiwijaya, and S. Al-Faraby, “Text Categorization on Hadith Sahih Al-Bukhari using Random Forest,” J Phys Conf Ser, vol. 971, p. 012037, Mar. 2018, doi: 10.1088/1742-6596/971/1/012037.

I. Taufik, M. Jaenudin, F. U. Badriyah, B. Subaeki, and O. T. Kurahman, “The search for science and technology verses in Qur’an and hadith,” Bulletin of Electrical Engineering and Informatics, vol. 10, no. 2, pp. 1008–1014, Apr. 2021, doi: 10.11591/eei.v10i2.2629.

J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805, 2018.

E. Gogoulou, “Using Bidirectional Encoder Representations from Transformers for Conversational Machine Comprehension,” 2019.

S. Alaparthi and M. Mishra, “Bidirectional Encoder Representations from Transformers (BERT): A sentiment analysis odyssey,” Jul. 2020.

P. Chaudhry, “Bidirectional Encoder Representations from Transformers for Modelling Stock Prices,” Int J Res Appl Sci Eng Technol, vol. 10, no. 2, pp. 896–901, Feb. 2022, doi: 10.22214/ijraset.2022.40406.

N. Yadav and A. K. Singh, “Bi-directional Encoder Representation of Transformer model for Sequential Music Recommender System,” in Forum for Information Retrieval Evaluation, New York, NY, USA: ACM, Dec. 2020, pp. 49–53. doi: 10.1145/3441501.3441503.

M. Vubangsi, T. R. Mangai, A. Olukayode, A. S. Mubarak, and F. Al-Turjman, “BERT-IDS: an intrusion detection system based on bidirectional encoder representations from transformers,” in Computational Intelligence and Blockchain in Complex Systems, Elsevier, 2024, pp. 147–155. doi: 10.1016/B978-0-443-13268-1.00021-2.

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Published

2024-08-15

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