Improving Government Helpdesk Service With an AI-Powered Chatbot Built on the Rasa Framework
Helpdesk services are an important component in supporting Information Technology (IT) services. The helpdesk operates as the initial interface for managing and resolving concerns. Helpdesk helps user to get solutions when facing problems while using an IT service. This research focuses on the impac...
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| Format: | Article |
| Language: | English |
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Ikatan Ahli Informatika Indonesia
2025-04-01
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| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| Subjects: | |
| Online Access: | https://jurnal.iaii.or.id/index.php/RESTI/article/view/6293 |
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| _version_ | 1850139580346400768 |
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| author | Wirat Moko Hadi Sasmita Surya Sumpeno Reza Fuad Rachmadi |
| author_facet | Wirat Moko Hadi Sasmita Surya Sumpeno Reza Fuad Rachmadi |
| author_sort | Wirat Moko Hadi Sasmita |
| collection | DOAJ |
| description | Helpdesk services are an important component in supporting Information Technology (IT) services. The helpdesk operates as the initial interface for managing and resolving concerns. Helpdesk helps user to get solutions when facing problems while using an IT service. This research focuses on the impact of artificial intelligence (AI)-powered chatbots on the performance of the initial response of government helpdesk services. The chatbot is designed to improve service performance by quickly identifying and classifying reported issues and automatically responding to messages, enabling faster responses. The research proposed a new System Design of a helpdesk system with an AI-based chatbot. The data used comes from Telegram group chat logs, exported in JSON format. We find that the Rasa NLU model with DIET Classifier successfully achieved an accuracy rate of 0.825 in classifying intents, with the precision value of 0.838, recall of 0.829, and F1 score of 0.821 using a Rasa model with cross-validation, where folds is 5 in evaluation. And initial response time was highly improved after using chatbot artificial intelligence from more than 3 hours on the telegram group helpdesk based to an average of 2.15 seconds. These research results suggest AI-Chatbot-based ability to assist the helpdesk team in handling user queries and reports, and improving initial time response. |
| format | Article |
| id | doaj-art-b2c95f1f547947dbb0fadce751936a2e |
| institution | OA Journals |
| issn | 2580-0760 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj-art-b2c95f1f547947dbb0fadce751936a2e2025-08-20T02:30:13ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602025-04-019239340310.29207/resti.v9i2.62936293Improving Government Helpdesk Service With an AI-Powered Chatbot Built on the Rasa FrameworkWirat Moko Hadi Sasmita0Surya Sumpeno1Reza Fuad Rachmadi2Institut Teknologi Sepuluh NopemberInstitut Teknologi Sepuluh NopemberInstitut Teknologi Sepuluh NopemberHelpdesk services are an important component in supporting Information Technology (IT) services. The helpdesk operates as the initial interface for managing and resolving concerns. Helpdesk helps user to get solutions when facing problems while using an IT service. This research focuses on the impact of artificial intelligence (AI)-powered chatbots on the performance of the initial response of government helpdesk services. The chatbot is designed to improve service performance by quickly identifying and classifying reported issues and automatically responding to messages, enabling faster responses. The research proposed a new System Design of a helpdesk system with an AI-based chatbot. The data used comes from Telegram group chat logs, exported in JSON format. We find that the Rasa NLU model with DIET Classifier successfully achieved an accuracy rate of 0.825 in classifying intents, with the precision value of 0.838, recall of 0.829, and F1 score of 0.821 using a Rasa model with cross-validation, where folds is 5 in evaluation. And initial response time was highly improved after using chatbot artificial intelligence from more than 3 hours on the telegram group helpdesk based to an average of 2.15 seconds. These research results suggest AI-Chatbot-based ability to assist the helpdesk team in handling user queries and reports, and improving initial time response.https://jurnal.iaii.or.id/index.php/RESTI/article/view/6293chatbotrasahelpdesknatural language understanding |
| spellingShingle | Wirat Moko Hadi Sasmita Surya Sumpeno Reza Fuad Rachmadi Improving Government Helpdesk Service With an AI-Powered Chatbot Built on the Rasa Framework Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) chatbot rasa helpdesk natural language understanding |
| title | Improving Government Helpdesk Service With an AI-Powered Chatbot Built on the Rasa Framework |
| title_full | Improving Government Helpdesk Service With an AI-Powered Chatbot Built on the Rasa Framework |
| title_fullStr | Improving Government Helpdesk Service With an AI-Powered Chatbot Built on the Rasa Framework |
| title_full_unstemmed | Improving Government Helpdesk Service With an AI-Powered Chatbot Built on the Rasa Framework |
| title_short | Improving Government Helpdesk Service With an AI-Powered Chatbot Built on the Rasa Framework |
| title_sort | improving government helpdesk service with an ai powered chatbot built on the rasa framework |
| topic | chatbot rasa helpdesk natural language understanding |
| url | https://jurnal.iaii.or.id/index.php/RESTI/article/view/6293 |
| work_keys_str_mv | AT wiratmokohadisasmita improvinggovernmenthelpdeskservicewithanaipoweredchatbotbuiltontherasaframework AT suryasumpeno improvinggovernmenthelpdeskservicewithanaipoweredchatbotbuiltontherasaframework AT rezafuadrachmadi improvinggovernmenthelpdeskservicewithanaipoweredchatbotbuiltontherasaframework |