Penggunaan Text Modeling Untuk Identifikasi Kesalahan Penulisan Kata Pada Teks Pidato Bupati Banggai Sulawesi Tengah
DOI:
https://doi.org/10.30865/mib.v5i3.3051Keywords:
NLP, Writing Mistake, Typo Correction, Writing Correction, Natural Language ProcessingAbstract
Typing errors or typography are errors made when typing a document or text, typing errors can occur due to mechanical failure or slipping of the hand or finger. Generally, typing errors are something that often occurs when someone is typing and is considered normal, but this typing error in some contexts can change the meaning of the word or even the meaning of the sentence itself, This causes the need for correction again after someone has finished typing, but the correction process is mostly still manually so the results of the correction depend on how carefully someone makes corrections and how many documents will be corrected. Therefore we need a system that can make corrections quickly and accurately, the correction process can be done by various methods, one of which is using the text modeling method. In this study, the test data used 10 documents of the Banggai Regent's important speech, Central Sulawesi. The text modeling method can be combined with other supporting methods such as word2vec, where word2vec will be used as a recommendation for corrected words. This study creates a system that can correct word errors in important speech documents of the Banggai Regent, Central Sulawesi by using text modeling and Word2Vec methods, the results obtained from the system that has been made are the system has good performance and gets maximum test resultsReferences
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A. R. Kusuma, “Penerapan Keterampilan Berbicara Dalam Pidato,†2019.
Susilowati, “Teknik Retorika Dalam Naskah Pidato Nadiem Makarim Pada Hari Guru Nasional 2019,†Trias Polit., vol. 4, no. 1, pp. 1–14, 2020.
“Kesalahan tipografi - Wikipedia bahasa Indonesia, ensiklopedia bebas.†https://id.wikipedia.org/wiki/Kesalahan_tipografi (accessed Jun. 07, 2021).
M. S. H. Simarangkir, “Studi Perbandingan Algoritma - Algoritma Stemming Untuk Dokumen Teks Bahasa Indonesia,†J. Inkofar, vol. 1, no. 1, pp. 40–46, 2017, doi: 10.46846/jurnalinkofar.v1i1.2.
K. W. Church, “Emerging Trends: Word2Vec,†Nat. Lang. Eng., vol. 23, no. 1, pp. 155–162, 2017, doi: 10.1017/S1351324916000334.
L. Wu et al., “Word Mover’s Embedding: From Word2Vec to Document Embedding.†Accessed: Jun. 07, 2021. [Online]. Available: https://github.
A. Prasidhatama and K. M. Suryaningrum, “Perbandingan Algoritma Nazief & Adriani Dengan Algoritma Idris Untuk Pencarian Kata Dasar,†J. Teknol. dan Manaj. Inform., vol. 4, no. 1, pp. 1–4, 2018, doi: 10.26905/jtmi.v4i1.1773.
I. P. M. Wirayasa, I. M. A. Wirawan, and I. M. A. Pradnyana, “Algoritma Bastal: Adaptasi Algoritma Nazief & Adriani Untuk Stemming Teks Bahasa Bali,†J. Nas. Pendidik. Tek. Inform., vol. 8, no. 1, p. 60, 2019, doi: 10.23887/janapati.v8i1.13500.
D. Wahyudi, T. Susyanto, and D. Nugroho, “Implementasi dan analisis algoritma stemming nazief & adriani dan porter pada dokumen berbahasa indonesia,†J. Ilm. SINUS, vol. 15, no. 2, pp. 49–56, 2017.
“Word2Vec. Word2Vec Model Tutorial (part 1) | by Arif R | Medium.†https://arifromadhan19.medium.com/word2vec-95c5df46e045 (accessed Jun. 07, 2021).
H. Judul, “UJI AKURASI APLIKASI AUGMENTED REALITY PEMBELAJARAN HURUF ALFABET BAHASA ISYARAT INDONESIA (BISINDO) PADA VUFORIA MENGGUNAKAN CONFUSION MATRIX.â€
D. Steveson, H. Agung, and F. Mulia, “Plagiarisme Detection Applications For Tasks and Problems in School Using Rabin Karp Algorithm,†Th, May 2018. Accessed: Jun. 07, 2021. [Online]. Available: http://journal.ubm.ac.id/jalu.
“View of ANALISIS SENTIMEN OPINI PUBLIK MENGENAI COVID-19 PADA TWITTER MENGGUNAKAN METODE NAÃVE BAYES DAN KNN.†http://ejournal.nusamandiri.ac.id/index.php/inti/article/view/1347/661 (accessed Jun. 07, 2021).
M. S. Anwar, I. M. I. Subroto, and S. Mulyono, “Sistem Pencarian E-Journal Menggunakan Metode Stopword Removal Dan Stemming,†Pros. Konf. Ilm. Mhs. UNISSULA 2, pp. 58–70, 2019, [Online]. Available: http://lppm-unissula.com/jurnal.unissula.ac.id/index.php/kimueng/article/viewFile/8420/3887.
D. Sebagai et al., “ALGORITMA STEMMING TEKS BAHASA MASSENREMPULU BERBASIS ATURAN TATA BAHASA TUGAS AKHIR.â€
T. Setiawan, “Korpus dalam kajian penerjemahan,†2017.
B. Bahasa Kalimantan Barat and D. Ari Asfar Balai Bahasa Kalimantan Barat, “CIRI-CIRI BAHASA MELAYU PONTIANAK BERBASIS KORPUS LAGU BALEK KAMPONG CHARACTERISTICS OF PONTIANAK MALAY LANGUAGE BASED ON THE BALEK KAMPONG SONG CORPUS.â€
J. Nurjaman, R. Ilyas, F. Kasyidi, J. Informatika, U. Jenderal, and A. Yani, “Pengukuran Kesamaan Semantik Pasangan Kalimat Sitasi Menggunakan Convolutional Neural Network,†pp. 26–27, 2020.
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