Prediksi Loyalitas Pelanggan Indihome Menggunakan Algoritma C4.5

Sustiyanisa, Gitasiwi (2021) Prediksi Loyalitas Pelanggan Indihome Menggunakan Algoritma C4.5. Technical Report. Pustakawan, Perpustakaan Institut Teknologi Telkom Purwokerto. (Unpublished)

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Abstract

Loyalty is a form of decision-making behavior in purchasing goods or services continuously from one of the selected companies. Telkom Witel Purwokerto is a branch of PT Telkom Indonesia (Persero) Tbk (Telkom) which has a threat of decreasing customer loyalty so that it can harm revenue generation. The purpose of compiling this report is to classify indihome customers who have the potential to become loyal customers. This report can be used as a reference in developing product innovations, improving service quality, and promo innovations or activities that can involve indihome customers at Telkom Witel Purwokerto. The data collection used includes observation and discussion methods. Observation is direct observation to find and collect information in the form of data provided by the field supervisor. Discussions were carried out theoretically and practically directly with teammates and field supervisors. The purpose of this study is to make it easier to predict customer loyalty with a high level of accuracy using influential variables. Data processing is carried out using the C4.5 Algorithm as a tool to calculate the entropy and gain values based on the variable length of subscription, average billing, description and customer level which can be a decision tree to predict customer loyalty and produce the most influential variables with the gain value. the highest on the length of the subscription of 1.44991676. Based on the results of the calculation osf the gain value above, the influential variable is the length of subscription so that Telkom Witel Purwokerto can focus more on interacting with old customers to ask complaints and inputs that are felt during the subscription. Suggestions for Telkom Witel Purwokerto to offer attractive product promotions to retain old customers. Keywords: C4.5 Algorithm, Data Mining, Loyalty

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