PERBANDINGAN OPTIMASI ALGORITMA RANDOM FOREST MENGGUNAKAN TEKNIK BOOSTING TERHADAP KASUS KLASIFIKASI CHURN PELANGGAN DI INDUSTRI TELEKOMUNIKASI

Noeril Agian Septa Dinata, Ginanjar Abdurrahman, Nur Qodariyah Fitriyah

Abstract


Industri telekomunikasi berkembang sangat pesat dan perusahaan telekomunikasi terus melakukan berbagai inovasi untuk mendukung persaingan bisnis yang benar-benar sengit dan semakin sulit mendapatkan pelanggan. Persaingan ini menghasilkan churn pelanggan. Churn pelanggan yang tinggi adalah salah satu tingkat kegagalan perusahaan, oleh karena itu churn harus dikurangi. Algoritma Random Forest dipilih karena memiliki kemampuan untuk mengklasifikasikan data yang tidak lengkap dan dapat menangani data sampel yang besar. Tujuan khusus dari penelitian ini adalah untuk membandingkan kinerja algoritma Random Forest dengan optimasi menggunakan teknik boosting (XGBoost dan AdaBoost). Pada penelitian menggunakan Upsampled untuk mengatasi data yang tidak seimbang dan metode interquartile range dalam mengatasi pencilan. Hasil penelitian ini menujjukkan bahwa optimasi algoritma Random Forest menggunakan boosting AdaBoost menghasilkan kinerja yang paling optimal dengan hasil akurasi (99.13%), presisi (98.31%), recall (100%) dan f1-score (99.15%).


Keywords


Telekomunikasi; Churn; Optimasi; Random Forest; Boosting; XGBoost; AdaBoost

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DOI: https://doi.org/10.32528/jasie.v5i1.18820

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