ANALISA PELANGGAN CABUT LAYANAN INTERNET RUMAHAN MENGGUNAKAN ALGORITMA RANDOM FOREST DAN LOGISTIC REGRESSION

FABIAN, WILDAN AQMARIQ (2023) ANALISA PELANGGAN CABUT LAYANAN INTERNET RUMAHAN MENGGUNAKAN ALGORITMA RANDOM FOREST DAN LOGISTIC REGRESSION. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

Analysis of customer churn in the world industry of home internet service providers (Fiber To The Home/FTTH) is an important matter for further research. Churn customers are service users who tend to stop subscribing to a service. Recently, the home internet service market has increased sharply, with the current high demand, of course there will be market competition in home internet service provider services. Therefore it is very important to analyze customers who are likely to switch to competitors in the near future. In this study the authors will try to build customer churn predictions using the CRISP-DM research method for home internet service providers using data mining and machine learning techniques, namely logistic regression and Random Forest. A comparison will be made of the efficiency of the two algorithms based on the service user dataset. Keywords: FTTH, RANDOM FOREST, LOGISTIC REGRESSION, churn, CRISPDM Analisa pelanggan churn di dunia industry penyedia jasa layanan internet rumahan (Fiber To The Home/ FTTH) merupakan hal yang penting untuk dilakukan penilitian lebih lanjut. Pelanggan churn sendiri merupakan pengguna layanan yang cenderung berhenti berlangganan terhadap suatu layanan. Belakangan pasar layanan internet rumahan meningkat tajam, dengan demand yang tinggi saat ini tentu akan ada persaingan pasar dalam jasa penyedia layanan internet rumahan. Maka dari itu sangat penting untuk menganalisa pelanggan yang kemungkinan akan beralih ke pesaing dalam waktu dekat. Dalam penilitian ini penulis akan mencoba membangun prediksi pelanggan churn menggunakan metode penilitian CRISP-DM untuk perusahaan penyedia jasa layanan internet rumahan menggunakan teknik data mining dan machine learning yaitu logistic regression and Random Forest. Akan dilakukan perbandingan terhadap efisiensi kedua algoritma berdasarkan dataset pengguna layanan.Kata Kunci: FTTH, RANDOM FOREST, LOGISTIC REGRESSION, churn, CRISP-DM

Item Type:	Thesis (S1)
Call Number CD:	FIK/INFO. 23 037
Call Number:	SIK/15/23/038
NIM/NIDN Creators:	41518120018
Uncontrolled Keywords:	FTTH, RANDOM FOREST, LOGISTIC REGRESSION, churn, CRISP-DM
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 500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 510 Mathematics/Matematika > 518 Numerical Analysis/Analisis Numerik, Analisa Numerik > 518.1 Algorithms/Algoritma
Divisions:	Fakultas Ilmu Komputer > Informatika
Depositing User:	MILA RISKA
Date Deposited:	05 May 2023 03:45
Last Modified:	05 May 2023 03:45
URI:	http://repository.mercubuana.ac.id/id/eprint/75549

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