Perangkingan Dokumen Berbahasa Arab berdasarkan Query dengan Metode Klasifikasi Naïve Bayes dan K-Nearest Neighbor

Authors

  • Usfita Kiftiyani Informatics Engineering, UIN Sunan Kalijaga Yogyakarta
  • Suprapto Suprapto Teknik Informatika, Universitas Brawijaya
  • Novanto Yudistira Teknik Informatika, Universitas Brawijaya

DOI:

https://doi.org/10.33633/tc.v19i4.3939

Keywords:

perangkingan dokumen, naïve bayes, k-nearest neighbor

Abstract

Penelitian tentang perangkingan dokumen pada temu kembali informasi saat ini mudah ditemukan, hal ini terkait perkembangan keilmuan dibidang penggalian informasi yang bergerak sangat cepat. Namun, Walaupun sudah penelitian yang menggunakan Bahasa Arab sebagai objek masih terbatas. Karena keterbatasan penggunaan dokumen Bahasa Arab untuk penelitian bidang penggalian informasi maka penulis mencoba melakukan pendekatan sederhana, yaitu dengan mengimplementasikan metode klasifikasi naïve bayes dan k-Nearest Neighbor (k-NN). Tujuan dari penelitian ini adalah untuk mengetahui apakah metode klasifikasi terutama naïve bayes dan k-NN dapat digunakan untuk melakukan perangkingan, dan juga membandingkan akurasi dari kedua metode tersebut. Berdasarkan penelitian yang dilakukan, didapatkan hasil bahwa perangkingan dengan metode klasifikasi dapat dilakukan dengan tingkat akurasi metode Naïve Bayes lebih baik dibandingkan dengan metode k-NN dengan rata-rata nilai F1 Measure mencapai 72%, rata-rata nilai precision mencapai 75%, dan rata-rata nilai recall mencapai 80%. Sedangkan hasil dari metode k-NN diperoleh rata-rata nilai F1 Measure mencapai 70%, rata-rata nilai precision mencapai 76%, dan rata-rata nilai recall mencapai 79%. Namun penelitian ini masih kurang dari segi teknik yang dilakukan, yaitu dengan menghilangkan proses stemming. Sehngga penulis memberikan saran untuk penelitian selanjutnya supaya bisa dilakukan proses stemming dan menggunakan metode perangkingan yang lebih baru.

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Published

2020-11-26

Issue

Vol. 19 No. 4 (2020): November 2020

Section

Articles

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