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All Journal Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Scientific Journal of Informatics
Anggito Wicaksono
Universitas Singaperbangsa Karawang
Computer Science & IT

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Uji Performa Teknik Klasifikasi untuk Memprediksi Customer Churn Anggito Wicaksono; Anita Anita; Tesa Nur Padilah
Bianglala Informatika Vol 9, No 1 (2021): Bianglala Informatika 2021
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (761.553 KB) | DOI: 10.31294/bi.v9i1.9992

Abstract

Perkembangan industri telekomunikasi sangatlah cepat, hal ini dapat dilihat dari perilaku masyarakat yang menggunakan internet dalam berkomunikasi. Perilaku ini menyebabkan banyaknya perusahaan telekomunikasi dan meningkatnya internet service provider yang dapat menimbulkan persaingan antar provider. Pelanggan memiliki hak dalam memilih provider yang sesuai dan dapat beralih dari provider sebelumnya yang diartikan sebagai customer churn. Peralihan ini dapat menyebabkan berkurangnya pendapatan bagi perusahaan telekomunikasi sehingga penting untuk ditangani. Tujuan dari penelitian ini yaitu untuk mengetahui algoritme klasifikasi terbaik dan sesuai pada permasalahan customer churn. Penelitian ini dilakukan berdasarkan metode CRISP-DM sebagai alur penelitian dengan menerapkan tiga algoritme klasifikasi yaitu Logistic Regression, Decision Tree, dan Random Forest, yang dibantu dengan metode feature selection yaitu Backward Elimination untuk mengurangi variabel yang tidak signifikan. Hasil dari penelitian ini memperoleh bahwa algoritme Logistic Regression dengan Backward Elimination merupakan algoritme terbaik dengan nilai akurasi sebesar 82,23%, recall 57,22%, dan AUC sebesar 0,853 yang termasuk pada pemodelan good classification.
Pengaruh Jumlah Record Dataset Terhadap Algoritma Klasifikasi Berdasarkan Data Customer Churn Anita; Anggito Wicaksono; Tesa Nur Padilah
Jurnal Ilmiah Informatika Vol. 6 No. 1 (2021): Jurnal Ilmiah Informatika
Publisher : Department of Science and Technology Ibrahimy University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/jimi.v6i1.1223

Abstract

Telecommunication is one of the fastest growing industrial sectors so that there are more telecommunication companies. This can create various threats if the company does not use the strategy properly. Customer churn refers to the level of customer reduction which is one of the threats to reducing the company's revenue. This is an important issue for developing companies to evaluate in order to reduce the potential for churn that occurs. The initial stage that needs to be done is to predict customers who have the potential to switch from the company, one of which is the data mining approach. Classification is a data mining technique that can predict the class of datasets with various existing classification algorithms. The purpose of this study is to identify the effect of the number of dataset records on several classification algorithms. This research was conducted based on the CRISP-DM method by applying three classification algorithms, namely Logistic Regression, Naïve Bayes, and Decision Tree C4.5. The results showed that the greater the number of records in the dataset, the higher the accuracy value will be obtained. In dataset-1, logistic regression is a better algorithm based on an accuracy value of 80.09%, while naïve Bayes is superior based on an AUC value of 0.733 and an execution time of 0.00798 seconds. In dataset-2, it is found that decision tree is an algorithm that is more suitable than logistic regression and naïve Bayes algorithms, with an accuracy of 91.9% and an AUC value of 0.846 which is included in the good classification criteria. However, in execution time, the naïve Bayes algorithm only takes a processing time of 0.00403 seconds.
Co-Authors Anita Anita Anita Tesa Nur Padilah