Pemanfaatan Machine Learning untuk Pengelompokan dan Prediksi Target Tambah Daya Listrik Pelanggan Prabayar (Studi Kasus : PT PLN ULP Watang Sawitto)

Authors

  • Rizqa Afthoni Institut Teknologi Sepuluh Nopember
  • Diana Purwitasari Institut Teknologi Sepuluh Nopember
  • Agus Budi Raharjo Institut Teknologi Sepuluh Nopember

DOI:

https://doi.org/10.33633/tc.v21i3.6476

Keywords:

Tambah daya, K-means, Gradient Boosting, Artificial Neural Network

Abstract

Perkembangan teknologi sistem informasi dan ilmu pengetahuan khususnya dalam bidang pemasaran membuat para pelaku usaha berupaya untuk meningkatkan competitive advantage mereka dengan mengerahkan sumber daya yang dimiliki oleh perusahaan. Perusahaan dituntut untuk berinovasi dalam mengelola perusahaannya agar dapat bertahan dalam dunia persaingan. Kemampuan untuk memprediksi pelanggan prabayar yang berpotensi tambah daya listrik merupakan salah satu strategi pendukung untuk keberhasilan program pemasaran tambah daya pelanggan berdasarkan karakteristik konsumsi listriknya. Berdasarkan hal tersebut, penelitian ini mengajukan metode prediksi pelanggan prabayar dengan memanfaatkan algoritma pengelompokan (Clustering) dan klasifikasi. Data yang diolah adalah data pelanggan prabayar tarif rumah tangga yang memiliki fitur variabel daya listrik pelanggan (VA), frekuensi beli token listrik, total pemakaian kWh, total rupiah pembelian token, selisih daya VA pelanggan, jam nyala, periode hari pembelian token listrik, dan riwayat tambah daya listrik pelanggan. Pengelompokan dilakukan dengan menerapkan algoritma K-means. Dari hasil tersebut, model prediksi dibangun sesuai target setiap klaster dengan memanfaatkan dua metode, Gradient Boosting dan Artificial Neural Network. Evaluasi prediksi model terbaik dilakukan dengan menerapkan tiga skenario proporsi data latih dan data uji, yang selanjutnya diukur menggunakan matrik akurasi dan Cohen Kappa. Hasil eksperimen menghasilkan empat klaster berdasarkan karakteristik konsumsi listriknya. Gradient Boosting memberikan hasil yang terbaik untuk semua klaster, untuk klaster 1 menghasilkan nilai AUC 0.784, klaster 2 menghasilkan nilai AUC 0.941, klaster 3 menghasilkan nilai 0.884 dan klaster 4 menghasilkan nilai AUC 0.903.

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Published

2022-08-23

Issue

Vol. 21 No. 3 (2022): Agustus 2022

Section

Articles

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Copyright (c) 2022 Agus Budi Raharjo, Rizqa Afthoni, Diana Purwitasari

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