ANALISIS SENTIMEN PELANGGAN LAYANAN INTERNET TERHADAP INDIHOME TELKOM MENGGUNAKAN ALGORITMA NAIVE BAYES DAN K-NEAREST NEIGHBOR

PRATAMA, DIVAN ALIF (2022) ANALISIS SENTIMEN PELANGGAN LAYANAN INTERNET TERHADAP INDIHOME TELKOM MENGGUNAKAN ALGORITMA NAIVE BAYES DAN K-NEAREST NEIGHBOR. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

In suppressing the spread of COVID-19, the Indonesian government has established a PSBB policy or Large-Scale Social Restrictions. Many aspects and areas are affected by the policy, including education and offices. APJII or the Association of Indonesian Internet Service Providers explained that there was an increase in the number of internet users in Indonesia from last year's penetration of 64% to 73.7%. One of the reasons for this increase is the COVID-19 pandemic. On the Twitter platform, they often find various kinds of public responses they give about the services of Internet Service Providers, both negative and positive. In this study, sentiment analysis was conducted to determine public opinion on the performance of Internet Service Providers. The method used is the Naïve Bayes and K-Nearest Neighbor classification algorithm assisted by the RapidMiner tool. The experimental results show that K-Nearest Neighbor provides the highest accuracy values of 98% and 97% for the two data tested, both Indihome and Firstmedia. Key words: Naive Bayes. K-Nearest Neighbor (KNN), Twiter,indihome,Rapidminer. Dalam menekan penyebaran COVID-19, pemerintah Indonesia telah menetapkan kebijakan PSBB atau Pembatasan Sosial Berskala Besar. Banyak aspek dan bidang yang terkena dampak kebijakan tersebut termasuk pendidikan dan perkantoran. APJII atau Asosiasi Penyelenggara Jasa Internet Indonesia menjelaskan terjadi peningkatan jumlah pengguna internet di Indonesia dari penetrasi tahun lalu sebesar 64% menjadi 73,7%. Salah satu penyebab peningkatan ini adalah pandemi COVID-19. Di platform Twitter, mereka sering menemukan berbagai macam tanggapan publik yang mereka berikan tentang layanan dari Internet Service Provider, baik negatif maupun positif. Dalam penelitian ini, analisis sentimen dilakukan untuk mengetahui opini publik terhadap kinerja Internet Service Provider. Metode yang digunakan adalah algoritma klasifikasi Naïve Bayes dan K-Nearest Neighbor dibantu oleh alat RapidMiner. Hasil eksperimen menunjukkan bahwa K-Nearest Neighbor memberikan nilai akurasi tertinggi sebesar 98% dan 97% untuk kedua data yang diuji, baik Indihome maupun Firstmedia. Kata kunci: Naive Bayes, K-Nearest Neighbor (KNN), Twiter, indihome, Rapidminer

Item Type:	Thesis (S1)
Call Number CD:	FIK/INFO. 22 116
NIM/NIDN Creators:	41518010173
Uncontrolled Keywords:	Naive Bayes, K-Nearest Neighbor (KNN), Twiter, indihome, Rapidminer
Subjects:	000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika > 004.6 Interfacing and Communications/Tampilan Antar Muka (Interface) dan Jaringan Komunikasi Komputer > 004.67 Wide Area Network (WAN)/Wide Area Network > 004.678 Internet (World Wide Web)/Internet
Divisions:	Fakultas Ilmu Komputer > Informatika
Depositing User:	WADINDA ROSADI
Date Deposited:	17 Oct 2022 04:18
Last Modified:	17 Oct 2022 04:18
URI:	http://repository.mercubuana.ac.id/id/eprint/70502

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