Implementation of AIChatbots for Education and Introduction to Blockchain Products
DOI:
https://doi.org/10.58777/ise.v3i2.525Keywords:
AI Chatbot, Blockchain, Sharia Products, WebUI, OllamaAbstract
The development of information technology in the era of the Industrial Revolution 4.0 has encouraged service providers to continue to improve the quality of their digital services, including PT. Telkom Access through the MyIndiHome application. This application makes it easier for customers to access various services, but there are still complaints regarding features and ease of use. This study aims to analyze the sentiment of MyIndiHome application users using the Support Vector Machine (SVM) method to evaluate customer perception and satisfaction levels. The data collection method was carried out through Simple Classification Results (based on 70 respondents): Interviews with nine purposively selected informants, Distribution of questionnaires to 61 interview respondents of MyIndiHome application users, which were then analyzed using pre-processing and classification techniques to distinguish sentiment into positive and negative. The analysis results show that most users have a positive perception of the application, especially in terms of ease of reporting interruptions and the completeness of features. However, there are still obstacles to ease of use, especially for users less familiar with technology. The SVM method has proven effective in classifying user sentiment even with limited data. This study recommends improving the user interface and education features to optimize the overall use of the MyIndiHome application.
References
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., …
Amodei, D. (2020). Language Models are Few-Shot Learners (arXiv:2005.14165). arXiv. https://doi.org/10.48550/arXiv.2005.14165
Debets, T., Banihashem, S. K., Joosten-Ten Brinke, D., Vos, T. E. J., Maillette de Buy Wenniger, G., & Camp, G. (2025). Chatbots in education: A systematic review of objectives, underlying technology and theory, evaluation criteria, and impacts. Computers & Education, 234, 105323. https://doi.org/10.1016/j.compedu.2025.105323
Dogan, O., & Gurcan, O. F. (2024). Enhancing E-Business Communication with a Hybrid Rule-Based and Extractive-Based Chatbot. Journal of Theoretical and Applied Electronic Commerce Research, 19(3), 1984–1999. https://doi.org/10.3390/jtaer19030097
Hikmah, A., Azmi, F., & Nugrahaeni, R. A. (2023). Implementasi Natural Language Processing Pada Chatbot Untuk Layanan Akademik. eProceedings of Engineering, 10(1), Article 1. https://openlibrarypublications.telkomuniversity.ac.id/index.php/engineering/article/view/19335
Huda, N. (2021). Nalisis Penyebab Keterlambatan Pemusnahan Berkas Rekam Medis Inaktif Di Puskesmas Jenggawah. J-REMI : Jurnal Rekam Medik dan Informasi Kesehatan, 3(1). https://doi.org/10.25047/j-remi.v3i1.2473
Israwati Hamsar, Nur Febrianti, Amelia Uswatun Khasanah, Annajmi Rauf, & Elma Nurjannah. (2024). Analisis Pengaruh Chatbot AI terhadap Pengalaman Mahasiswa Menggunakan E-commerce. Journal of Vocational, Informatics and Computer Education, 82–89. https://doi.org/10.61220/voice.v2i2.20247
Kuhail, M. A., Alturki, N., Alramlawi, S., & Alhejori, K. (2023). Interacting with educational chatbots: A systematic review. Education and Information Technologies, 28(1), 973–1018. https://doi.org/10.1007/s10639-022-11177-3
Labadze, L., Grigolia, M., & Machaidze, L. (2023). Role of AI chatbots in education: Systematic literature review. International Journal of Educational Technology in Higher Education, 20(1), 1–17. https://doi.org/10.1186/s41239-023-00426-1
Mageira, K., Pittou, D., Papasalouros, A., Kotis, K., Zangogianni, P., & Daradoumis, A. (2022). Educational AI Chatbots for Content and Language Integrated Learning. Applied Sciences, 12(7), 3239. https://doi.org/10.3390/app12073239
Manning, C., & Schütze, H. (1999). Foundations of Statistical Natural Language Processing. MIT Press.
Nakamoto, S. (2008). Bitcoin: A Peer-to-Peer Electronic Cash System. Satoshi Nakamoto Institute.
Oka, M., Oktaviandi Syeira, C. P., & Hadiapurwa, A. (2022). Penggunaan Teknologi Blockchain Dalam Bidang Pendidikan. Produktif Jurnal Ilmiah Pendidikan Teknologi Informasi, 5(2), 437–442. https://doi.org/10.35568/produktif.v5i2.1259
Okonkwo, C. W., & Ade-Ibijola, A. (2021). Chatbots applications in education: A systematic review. Computers and Education: Artificial Intelligence, 2, 100033. https://doi.org/10.1016/j.caeai.2021.100033
OpenAI. (2020). Language models are few-shot learners. https://openai.com/index/language-models-are-few-shot-learners/
Rahman, M. K., Ismail, N. A., Hossain, M. A., & Hossen, M. S. (2025). Students' mindsets toward adopting AI chatbots and their impact on the effectiveness of online learning in higher education. Future Business Journal, 11(1), 30. https://doi.org/10.1186/s43093-025-00459-0
Raj, S. (2018). Building Chatbots with Python (1st ed.). Apress Berkeley, CA. https://link.springer.com/book/10.1007/978-1-4842-4096-0
Sisephaputra, B., Judijanto, L., Apriyanto, A., Lukman, L., Migunani, M., Umar, N., Sepriano, S., Khairunnisa, K., & Wati, D. C. (2024). Generative Artificial Intelligence (GenAI): Pengetahuan Dasar GenAI Beserta Penerapannya. PT. Green Pustaka Indonesia.
Zidifaldi, D., Abdullah, A., Sari, K., & Fakhruzi, I. (2022). Pemanfaatan iot sebagai sistem deteksi dini kebakaran dengan sensor api dan sensor suhu berbasis arduino. Jurnal Digital Teknologi Informasi, 5(2), 66. https://doi.org/10.32502/digital.v5i2.4338
Downloads
Published
How to Cite
Issue
Section
Copyright (c) 2025 Bento Sariando Saragih, Achmad Faizi, Frisma Handayanna

This work is licensed under a CC Attribution-ShareAlike 4.0
Views: 0
|
Downloaded: 0









