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Ekstraksi Informasi pada Dokumen Teks Menggunakan Metode Named-Entity Recognition untuk Sistem Autofill Formulir Lowongan SIM Magang MyITS StudentConnect

Authors: Hadinata, K. C. (Kevin); Navastara, D. A. (Dini); Fabroyir, H. (Hadziq);

Ekstraksi Informasi pada Dokumen Teks Menggunakan Metode Named-Entity Recognition untuk Sistem Autofill Formulir Lowongan SIM Magang MyITS StudentConnect

Abstract

Sistem Informasi Manajemen (SIM) Magang MyITS StudentConnect adalah suatu platform yang dibuat untuk memenuhi kebutuhan penyebaran informasi magang dalam lingkungan mahasiswa Institut Teknologi Sepuluh Nopember (ITS). Dalam perkembangannya, diperlukan suatu sistem yang efektif dan efisien untuk pengisian informasi lowongan yang akan diunggah oleh pihak Pengembangan Kewirausahaan dan Karir (PK2) dalam lingkungan ITS. Oleh karena itu, suatu sistem autofill (sistem pengisian otomatis) dirasa perlu untuk dapat meningkatkan efisiensi dalam pengisian lowongan magang. Sistem ini bekerja dengan cara memindai dokumen lowongan magang yang diunggah dan mengisikan informasi yang didapat dari dokumen lowongan tersebut dalam format lowongan yang sesuai dengan modul SIM Magang myITS Student Connect.Untuk melakukan ekstraksi informasi dari dokumen yang diunggah, dilakukan pemindaian data teks menggunakan teknik Optical Character Recognition (OCR). Lalu, untuk klasifikasi, digunakan metode Named-Entity Recognition (NER), yang merupakan salah satu metode Natural Language Processing yang dapat mengklasifikasikan informasi berdasarkan entitasnya. Hasil ekstraksi informasi tersebut kemudian dimasukkan ke dalam kolom-kolom form yang tersedia sesuai dengan format modul Magang myITS StudentConnect. Hasil dari penelitian ini diharapkan dapat meningkatkan kinerja dan efisiensi SIM Magang dalam melakukan pendistribusian informasi terkait magang yang tersedia untuk kalangan mahasiswa-mahasiswa ITS. Dilakukan pengamatan performa terhadap ketepatan analisa NER dengan menggunakan data latih berupa poster lowongan magang sebanyak 24 buah. Setelah itu, didapatkan hasil berupa optimizer Adam dengan epochs sebanyak 1000 yang dapat bekerja dengan performa paling baik dengan nilai precision 0.53023, recall 0.56755, dan f1-score 0.54565.

Country
Indonesia
Related Organizations
Keywords

Sistem Pengisian Otomatis, Optical Character Recognition, Indonesia, Named Entity Recognition, Natural Language Processing

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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