The Determination Analysis Of Telecommunications Customers Potential Cross-Selling With Classification Naive Bayes And C4.5

Abstract

Every company has various marketing strategies. Marketing strategies are offered to sell the company’s products to benefit if the company introduces new products. However, too many offers to customers that are not true, will only make marketing inefficient and ineffective. Data mining as a way to find patterns and relationships in data can be used to make valid predictions. To simplify the marketing strategy of PT. TELKOM then classifies the data that already exists in PT. TELKOM Jakarta. Telkom customers have the potential to become customers of new Indihome products, so marketing is carried out by PT. TELKOM has become more effective and efficient. By using data mining classification methods, namely the Naive Bayes Classifier algorithm and the C4.5 algorithm, patterns and relationships are obtained to simplify the marketing strategy of PT. TELKOM where in previous research, the results of classification of data mining research models with the Naive Bayes Classifier algorithm have an accuracy value of 85.08% and AUC 0.841 while in this study the C4.5 algorithm has an accuracy value of 88.61% and AUC 0.870. C4.5 is a model with good accuracy for customer classification data that has the potential for more effective and efficient in cross-selling marketing strategy..

Penerapan Algoritma C4.5 Untuk Memprediksi Keluhan Pelanggan Pada Apartemen Jakarta

“…Klasifikasi algoritma C4.5 mempertimbangkan kumpulan data klasifikasi [19]. Algoritma C4.5 memiliki akurasi tertinggi [20] [21]. Metode ini memiliki kemampuan kecepatan klasifikasi yang sangat tinggi [19].…”