Klasifikasi Helpdesk Menggunakan Metode K-Nearest Neighbor dan TF-ABS

Authors

DOI:

https://doi.org/10.33633/tc.v20i4.5094

Keywords:

Helpdesk, Klasifikasi, KNN, Pembobotan, TF-ABS

Abstract

Helpdesk merupakan aplikasi yang bermanfaat bagi pengguna nya untuk memperoleh informasi mengenai layanan yang ada pada sebuah perusahaan atau instansi pemerintah. Proses disposisi tiket helpdesk secara manual dapat menimbulkan kesalahan dalam menentukan unit tujuan tiket serta memperpanjang masa penyelesaian tiket karena adanya waktu tunggu untuk mendisposisikan tiket menuju unit yang sesuai. Klasifikasi teks helpdesk sangat diperlukan untuk mendisposisikan tiket secara tepat dan cepat ke unit yang berwenang menangani tiket. Teks helpdesk diklasifikasi ke dalam 8 kategori unit tujuan yaitu Setditjen, Dit.Humas, Dit.PKNSI, Dit.KND, Dit.BMN, Dit.Penilaian, Dit.PNKNL, dan Dit.Lelang. Klasifikasi menggunakan metode K-Nearest Neighbor (KNN) dengan jumlah tetangga terdekat (k) yaitu k=1,3,5,7,9,11,13,15,17,19 serta metode pembobotan TF-ABS untuk proses seleksi fitur. Jumlah fitur yang digunakan untuk klasifikasi sebanyak 5%, 10%, 15%, 20%, 25% dan 30% dari jumlah seluruh dokumen. Akurasi klasifikasi tertinggi sebesar 90,04% diperoleh pada k=3 dan jumlah fitur sebanyak 15%, sedangkan akurasi terendah 84,54% pada k=19 dan jumlah fitur sebanyak 30%. Hasil klasifikasi helpdesk menggunakan KNN dan TF-ABS dapat menghasilkan akurasi cukup baik.

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Published

2021-11-22

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Vol. 20 No. 4 (2021): November 2021

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