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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) eProceedings of Applied Science
Guslinar Perdana, Erda
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Pengembangan Aplikasi Radio Digital dan Obat Herbal BSK Media Agata Suryonugroho, Paulus; Guslinar Perdana, Erda
eProceedings of Applied Science Vol. 9 No. 3 (2023): Juni 2023
Publisher : eProceedings of Applied Science

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Abstract

Abstract—BSK Media is a radio home for several nearby radio stations and provides on air and off air content. BSK Media previously had a website, but the site did not run well because the system did not work well and there was no herbal medicine sales segment sold by BSK Media. Therefore, the author designed an android-based application with existing features, namely the radio play feature, sales of herbal medicines carried out by BSK Media, as well as providing news information to users and applications that will be used by admins to manage data through mobile applications. It is hoped that with this application, users can listen to streaming radio from various places and get herbal medicines from BSK Media products, and BSK Media can manage data related to input data. This application is tested using black box testing. The purpose of the test is to find out the application is running as expected.Keywords: Herbal Medicine, Digital Radio, Android
Semantic search-enhanced healthcare chatbot for hospital information management system using vector database and transformer models Guslinar Perdana, Erda; Nugraha, Arya Adhi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4600-4613

Abstract

Healthcare chatbots are increasingly used to assist hospital staff, yet most existing systems rely on rule-based or generic machine learning (ML) approaches that lack the ability to comprehend natural language queries, while proprietary deep learning systems often incur high licensing costs. This work addresses this gap by proposing a cost-effective and scalable semantic vector retrieval solution for user intent recognition in a hospital information management system (HIMS) helpdesk chatbot. The MPNet based transformer model is employed to convert user inquiries and predefined intents into feature vectors, enabling highly accurate natural language understanding through cosine similarity retrieval within a dedicated vector database. The proposed vector search method was validated via an ablation study, achieving an accuracy of 0.70 for intent recognition, which demonstrates a significant performance gain of 28.0 percentage points over a traditional keyword-based search baseline. Usability testing across developer and doctor groups yielded an average score of 7.78 on a 10-point Likert scale. This study concludes that integrating semantic vector retrieval with a vector database is highly effective for recognizing specialized clinical intents, offering a more accurate solution that significantly reduces the manual helpdesk workload and enhances 24-hour assistance in healthcare.
Co-Authors Agata Suryonugroho, Paulus Nugraha, Arya Adhi