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Achmad Choiron
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Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi
ISSN : 25023470     EISSN : 25810367     DOI : 10.25139
Inform: Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi is One of the journals published by the Informatics Engineering Department Dr. Soetomo University, was established in January 2016. Inform a double-blind peer-reviewed journal, the aim of this journal is to publish high-quality articles dedicated to the field of information and communication technology, Published 2 times a year in January and July. Inform with p-ISSN:2502-3470 and e-ISSN:2581-0367 has been accredited by the Ministry of Research and Technology of the National Research and Innovation Agency of the Republic of Indonesia Number 85/M/KPT/2020 dated April 1, 2020. Accreditation is valid for 5 years Vol.3 No.2 2018 to Vol.8 No.1 2023. Focus and Scope that is Scientific research related to information and communication technology fields, including Software Engineering, Information Systems, Human-Computer Interaction, Architecture and Hardware, Computer Vision, Pattern Recognition, Computer Application and Artificial intelligence, Game Technology, and Computer Graphics, but not limited to informatics scope.
Articles 1 Documents
Search results for , issue "Vol. 10 No. 1 (2025)" : 1 Documents clear
Entity Extraction and Annotation for Job Title and Job Descriptions Using Bert-Based Model Fitri Ana Wati, Seftin; Fitri, Anindo Saka; Putra, Herlambang Haryo; Widodo, Suryo; Aziiza, Arizia Aulia
Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi Vol. 10 No. 1 (2025)
Publisher : Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25139/inform.v10i1.7367

Abstract

This research paper investigates Named Entity Recognition (NER) within Indonesia’s job vacancy domain, employing state-of-the-art Bert-based models. The study presents a detailed data collection and preprocessing methodology, followed by the Bert-based model’s fine-tuning for enhanced NER. The dataset comprises 48,673 job vacancies collected from the JobStreet website in July 2023, specifically focusing on multi-entity recognition, including job titles and job descriptions. An original annotation algorithm was developed using Python and Laravel for precise entity recognition. In addition, this paper provides an extensive literature review of NER and Bert-based models and discusses their relevance in the context of the Indonesian job market. The outcomes highlight the efficacy of our BERT-based model, attaining an average accuracy of 78.5%, a precision of 79.7%, a recall of 81.1%, and an F1 score of 80.8% in the Named Entity Recognition (NER) task. The study concludes by discussing the implications, limitations, and future directions, underscoring the model’s potential applicability in streamlining job matching and recruitment processes in Indonesia and beyond. This research contributes to the field by providing a robust framework for NER in job vacancies, highlighting the potential for improved job matching, and proposing enhancements for future model development and application in other languages and regions.

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