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Pendekatan Translasi Otomatis Catatan Medis Indonesia untuk Ekstraksi Informasi dan Pemetaan Medis berbasis cTAKES–UMLS Kasan, Iwan; Heryawan, Lukman; Qolina, Ellya; Aliyah, Aliyah
TIN: Terapan Informatika Nusantara Vol 6 No 10 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i10.9471

Abstract

Unstructured medical notes in SOAP format are crucial assets for clinical analysis; however, their automated processing in the Indonesian language remains a significant challenge due to limited support from global NLP technologies. This study evaluates the integration of Apache cTAKES and the Unified Medical Language System (UMLS) to extract medical information from Indonesian electronic health records. The primary obstacle lies in the cTAKES architecture, which is optimized for English, causing direct application to Indonesian texts to yield a very low detection rate (Recall) of only 17.9%. As a pragmatic solution to bridge this linguistic barrier, this research proposes a preprocessing pipeline based on automatic translation using the Google Translate API prior to the cTAKES extraction process. The evaluation was conducted on a dataset of 50 SOAP-format medical records identifying 840 medical entities. Experimental results demonstrate that the automatic translation approach significantly improves entity detection, achieving a Recall of 90.2% and an F1-Score of 93.4%. Despite challenges such as information loss from local medical abbreviations and translation ambiguities, this study proves that automatic translation serves as an effective transitional strategy in resource-limited environments. This approach not only supports clinical information extraction but also enables the automatic mapping of medical terminology to international standards such as ICD-10, SNOMED-CT, and RxNorm to foster national health data interoperability.
Artificial Intelligence and Machine Learning in Education: A Systematic Literature Review of Transformative Trends and Future Directions Aliyah, Aliyah
Fusion : Journal of Research in Engineering, Technology and Applied Sciences Vol. 3 No. 1 (2026): Fusion - April
Publisher : PT. Faaslib Serambi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66341/fusion.v3i1.282

Abstract

The transformation of education in the digital era has been significantly accelerated by the integration of Artificial Intelligence (AI) and Machine Learning (ML), fundamentally reshaping how learning is designed, delivered, and assessed. This study aims to systematically identify emerging trends, key benefits, prevailing challenges, and future directions of AI and ML applications in education through a Systematic Literature Review (SLR) approach. The reviewed literature was sourced from leading academic databases, including Scopus, IEEE Xplore, and ScienceDirect, covering publications from 2015 to 2025.  The findings reveal that AI and ML technologies have been widely implemented in various educational domains, particularly in adaptive learning systems, automated assessment mechanisms, and intelligent virtual assistants that facilitate personalized learning experiences. Despite these advancements, several critical challenges persist, notably digital inequality, data privacy concerns, and the limited technological literacy among educators, which hinder the effective adoption of these technologies. Furthermore, the study highlights that the future of education will increasingly rely on the integration of intelligent systems that enable data-driven, flexible, and learner-centered environments. The insights derived from this SLR are expected to provide valuable guidance for policymakers, educators, and technology developers in formulating adaptive and sustainable educational strategies in the era of artificial intelligence.