Penerapan Algoritma Naïve Bayes Untuk Pengelompokkan Predikat Peserta Uji Kemahiran Berbahasa Indonesia
DOI:
https://doi.org/10.30865/mib.v6i2.3956Keywords:
UKBI, Naive Bayes, Classification, AdaboostAbstract
Indonesian Language Proficiency Test is a proficiency test to measure a person's language proficiency in communicating using Indonesian, both Indonesian speakers and foreign speakers. The UKBI rating has 7 rating categories consisting of special, very excellent, excellent, intermediate, poor, marginal, and limited. The number of participants who take UKBI at the Riau Province Language Center has more than 1000 but no one has managed the data into new knowledge. One of the efforts that can be done with the data is classification. The Naïve Bayes Classification Algorithm is a classification algorithm that is very effective (getting the right results) and efficient (the reasoning process is carried out by utilizing existing inputs in a relatively fast way). In order to obtain good accuracy results, the Naive Bayes Algorithm is combined with the Adaboost feature selection with a 70:30 and 80:20 test scheme. The results of the research carried out resulted in the highest accuracy value, namely 89% which combined the Naive Bayes algorithm with the Adaboost feature selection with 70:30 data splittingReferences
S. Hudaa, “Peningkatan Keterampilan Berbahasa Indonesia Masyarakat Dengan Simulasi Tes UKBI,†SALINGKA, Maj. Ilm. Bhs. dan Sastra, vol. 16, pp. 47–55, 2020.
A. Suryadin, “Comparative Study Of Indonesian Language Skill Between PGSD And PJKR Students At STKIP Muhammadiyah Bangka Belitung,†vol. 355, no. Pfeic, pp. 106–109, 2019.
P. M. Pratama, “Peningkatan Kemahiran Berbahasa Indonesia melalui Program Klinik Bahasa UKBI Adaptif,†vol. 7, no. 2, pp. 160–167, 2021.
S. H. W. Admaja Dwi Herlambang, “Algoritma Naïve Bayes Untuk Klasifikasi Sumber Belajar Berbasis Teks Pada Mata Pelajaran Produktif Di Smk Rumpun Teknologi Informasi Dan Komunikasi,†vol. 6, no. 4, pp. 431–436, 2019, doi: 10.25126/jtiik.201961323.
A. L. Shelly Janu Setyaning Tyas, Mita Febianah, Farkhatus Solikhah and W. A. A. Kamil, “Nalisis Perbandingan Algoritma Naive Bayes Dan C.45 Dalam Klasifikasi Data Mining Untuk Memprediksi Kelulusan,†vol. 8, no. 1, pp. 96–103, 2021.
A. Saleh, “Penerapan Data Mining Dengan Metode Klasifikasi Naive Bayes Untuk Memprediksi Kelulusan Mahasiswa Dalam Mengikuti English Proficiency Test (Studi Kasus : Universitas Potensi Utama),†Konf. Nas. Sist. Informasi, Univ. Klabat,Manado,Indonesia, Vol. 2015, no. June, pp. 1–6, 2015, [Online]. Available: https://www.researchgate.net/publication/304271255_PENERAPAN_DATA_MINING_DENGAN_METODE_KLASIFIKASI_NAIVE_BAYES_UNTUK_MEMPREDIKSI_KELULUSAN_MAHASISWA_DALAM_MENGIKUTI_ENGLISH_PROFICIENCY_TEST_Studi_Kasus_Universitas_Potensi_Utama.
M. F. Rifai, H. Jatnika, and B. Valentino, “Penerapan Algoritma Naïve Bayes Pada Sistem Prediksi Tingkat Kelulusan Peserta Sertifikasi Microsoft Office Specialist (MOS),†Petir, vol. 12, no. 2, pp. 131–144, 2019, doi: 10.33322/petir.v12i2.471.
B. Kurniawan, M. A. Fauzi, and A. W. Widodo, “Klasifikasi Berita Twitter Menggunakan Metode Improved Naïve Bayes,†J. Pengemb. Teknol. Inf. dan Ilmu Komput. Univ. Brawijaya, vol. 1, no. 10, pp. 1193–1200, 2017.
Yuyun, Nurul Hidayah, and Supriadi Sahibu, “Algoritma Multinomial Naïve Bayes Untuk Klasifikasi Sentimen Pemerintah Terhadap Penanganan Covid-19 Menggunakan Data Twitter,†J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 4, pp. 820–826, 2021, doi: 10.29207/resti.v5i4.3146.
I. Ardiyanto, “Introduction to Data Science and Machine Learning Tools : Python , Jupyter , and Google Colab.â€
A. F. Cahyanti, “Penentuan Model Terbaik pada Metode Naive Bayes Classifier dalam Menentukan Status Gizi Balita dengan Mempertimbangkan Independensi Parameter,†vol. 4, no. 1, pp. 28–35, 2015.
A. Bisri, “Penerapan Adaboost untuk Penyelesaian Ketidakseimbangan Kelas pada Penentuan Kelulusan Mahasiswa dengan Metode Decision Tree,†vol. 1, no. 1, 2015.
B. Wijonarko and T. Komputer, “PERBANDINGAN ALGORITMA DATA MINING NAIVE BAYES DAN BAYES,†vol. 14, no. 1, pp. 21–26, 2020.
G. F. Kelvin Hennry Loudry Malelak, I Made Dwi Ardiada, “IMPLEMENTASI KLASIFIKASI NAIVE BAYES DALAM MEMPREDIKSI LAMA DALAM MEMPREDIKSI LAMA STUDI MAHASISWA ( STUDI KASUS : UNIVERSITAS DHYANA PURA ),†SINTECH, vol. 4 No 2, no. October, pp. 202–209, 2021, doi: 10.31598/sintechjournal.v4i2.964.
P. Terlaris and P. Penjualan, “Penerapan Algoritma Naïve Bayes untuk Rekomendasi Pakaian Wanita,†J. Inform., vol. 10, pp. 195–207, 2020.
F. F. Zain, “Effectiveness of Naïve Bayes Weighted SVM Method in Movie Review Classification,†vol. 5, no. 2, pp. 108–114, 2019.
C. Dan and U. Klasifikasi, “ANALISIS DAN KOMPARASI ALGORITMA NAÃVE BAYES DAN C4.5 UNTUK KLASIFIKASI LOYALITAS PELANGGAN MNC PLAY KOTA SEMARANG,†vol. 18, no. 2, pp. 161–172, 2021.
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