Sistem Deteksi Kesalahan Penulisan Karya Ilmiah Dengan Metode Natural Language Processing Pada Ejaan KBBI

Authors

  • Bagus Dwi Prasetyo Universitas Muria Kudus, Jawa Tengah, Indonesia
  • Wibowo Harry Sugiharto Universitas Muria Kudus, Jawa Tengah, Indonesia
  • Muhammad Imam Ghozali Universitas Muria Kudus, Jawa Tengah, Indonesia

DOI:

https://doi.org/10.62411/tc.v25i1.15219

Abstract

Penulisan karya ilmiah yang sesuai dengan kaidah Bahasa Indonesia merupakan hal penting dalam dunia akademik. Namun, masih banyak ditemui kesalahan ejaan dan penggunaan kata tidak baku dalam dokumen ilmiah. Penelitian ini bertujuan untuk merancang dan mengembangkan sistem deteksi kesalahan penulisan karya ilmiah menggunakan metode Natural Language Processing (NLP) berbasis ejaan Kamus Besar Bahasa Indonesia (KBBI). Sistem dibangun menggunakan pendekatan Waterfall dengan implementasi berbasis web menggunakan Python dan library NLP seperti spaCy, Sastrawi, dan NLTK. Proses deteksi melibatkan tahapan case folding, tokenisasi, stemming, Named Entity Recognition (NER), pencocokan dengan database KBBI, serta koreksi ejaan menggunakan algoritma Levenshtein Distance. Evaluasi sistem dilakukan menggunakan dataset karya ilmiah yang telah dianotasi secara manual dan menghasilkan akurasi 92%, presisi 92%, recall 93%, dan F1-score 92%. Hasil tersebut menunjukkan bahwa sistem mampu mendeteksi kesalahan ejaan dengan baik dan memberikan saran perbaikan secara efektif. Penelitian ini diharapkan dapat membantu meningkatkan kualitas penulisan akademik di Indonesia. Kata Kunci – Natural Language Processing, KBBI, Deteksi Ejaan, Karya Ilmiah

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Published

2026-02-26

Issue

Vol. 25 No. 1 (2026): February 2026

Section

Articles

License

Copyright (c) 2026 Bagus Dwi Prasetyo, Wibowo Harry Sugiharto, Muhammad Imam Ghozali

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