relation: https://digilib.uin-suka.ac.id/id/eprint/53631/
title: KOMPARASI ALGORITMA NAIVE BAYES DAN K-NEAREST  NEIGHBORS UNTUK KLASIFIKASI JUDUL BERITA
creator: Najmunda Zia Akmal, NIM.: 18106050051
subject: Tehnik Informatika
description: The existence of news that has existed for a long time makes the amount of  produced news very large and will continue to grow. In addition, news themes are  also very diverse, where almost everything can be discussed in the news. The  variety of themes makes news-making companies have to group the news, which  is still done manually. The application of machine learning algorithms can be a  solution to group news automatically.  This study aims to compare the value of accuracy, precision, recall, f1,  AUC, and Train-Prediction Time of the Naive Bayes and KNN algorithms in the  classification of news titles that consist of 4 news categories.  ` The results showed that the Naive Bayes algorithm worked better than KNN  with accuracy value, f1, AUC, and Train-Prediction Time are 0.840, 0.840, 0.964,  0.090 second versus 0.796, 0.797, 0.944, 0.944 second for data with one word as  token; 0.851, 0.851, 0.968, 0.107 second versus 0.801, 0.802, 0.945, 0.927 second  for data with 1 and 2 words as tokens; and 0.853, 0.853, 0.968, 0.115 second  versus 0.795, 0.796, 0.944, 1.095 second for data with 1, 2, and 3 words as  tokens.
date: 2022-08-29
type: Thesis
type: NonPeerReviewed
format: text
language: id
identifier: https://digilib.uin-suka.ac.id/id/eprint/53631/1/18106050051_BAB-I_IV-atau-V_DAFTAR-PUSTAKA.pdf
format: text
language: id
identifier: https://digilib.uin-suka.ac.id/id/eprint/53631/2/18106050051_BAB-II_sampai_SEBELUM-BAB-TERAKHIR.pdf
identifier:   Najmunda Zia Akmal, NIM.: 18106050051  (2022) KOMPARASI ALGORITMA NAIVE BAYES DAN K-NEAREST NEIGHBORS UNTUK KLASIFIKASI JUDUL BERITA.  Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.