Violita, Anggraini (2022) Teknik Clustering Data Mining Dalam Klusterisasi Pelanggan Pasang Baru (PSB) Produk Indihome. Project Report. Institut Telkom Telkom Purwokerto. (Unpublished)
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Abstract
Classification is a method of data mining used to organize data systematically or or rules that have been established. One of Indonesia's telecommunications companies that provide data-access service with a high speed and stable using the fiber optic transmission channel is PT Telkom Indonesia, Which gradually manage ordering services on customers using fiber-optic cables to install on new networks and convert become fiber optic access to houses commonly called indihome services. The fiber optic network Indihome is known by name Fiber Te The Home (FTTH). Indihome offer Triple Play Service or 3play consists of home phones (voice), Internet (Internet on fiber or high speed Internet), and interactive television services (usee tv). The purpose of this report is to classify customer data using data mining that can help solve the current problem using the naive bayes classifier method. The classification using naive bayes may classify data based on simple probability and be designed to act on assumptions between entities not interdependent and can solve problems quickly from various classifications, as proved from results accurate classification in this case study is shown in the good classification category. The result of research using a naive bayes model, was acquired a 9 class attribute based on customer category and the type of performance. Performance Value of naive bayes Operational's is Accuracy = 73.47%, classification error = 26.53%, and kappa - 0484. The number of classification with the largest percentage of the class is a prime cluster are found in the second segment with the type of indihome 3p new useetv entry hd and useetv indihome hd.
Item Type: | Monograph (Project Report) |
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Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
Divisions: | Faculty of Industrial Engineering and Design > Industrial Engineering |
Depositing User: | staff repository |
Date Deposited: | 31 Aug 2022 04:54 |
Last Modified: | 31 Aug 2022 04:54 |
URI: | http://repository.ittelkom-pwt.ac.id/id/eprint/7878 |
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