Klasifikasi Loyalitas Pengguna Sistem E-Learning Menggunakan Net Promoter Score dan Machine Learning

Didi Supriyadi, Sisilia Thya Safitri, Rona Nisa Sofia Amriza, Daniel Yeri Kristiyanto

Abstract


E-Learning merupakan salah satu produk layanan berbasis teknologi informasi yang dikembangkan dengan tujuan untuk meningkatkan kualitas pembelajaran pada perguruan tinggi. Kesuksesan implementasi sistem e-learning tidak lepas dari peran aktif dan kesetiaan pengguna (customer loyalty) untuk memberikan penilaian maupun feedback untuk peningkatan kualitas layanan yang meliputi efektivitas, efisiensi dan kepuasan dari kegunaan e-learning secara terus menerus. Kepuasan pelanggan berdampak positif terhadap retensi pelanggan, hingga pembelian produk atau jasa lanjutan pelanggan dan kepuasan pelanggan dianggap sebagai faktor utama loyalitas pelanggan. Kegunaan e-learning dapat diukur menggunakan kerangka kerja System Usability Scale (SUS). Sedangkan untuk mengetahui tingkat loyalitas pengguna e-learning dapat menggunakan pendekatan Net Promoter Scale (NPS). Penelitian ini bertujuan untuk membandingkan algoritma Decision Trees, Naïve Bayes, dan K-Nearest Neighbor (KNN) untuk klasifikasi tingkat loyalitas pengguna e-learning dengan pendekatan kategori berdasarkan NPS. Dataset terdiri atas 100 data yang berasal dari penilaian kepuasan pengguna dari dosen dan mahasiswa sebagai pengguna e-learning. Dataset dibagi menjadi 80:20 untuk data training dan data testing. Penerapan metode 10-fold cross validation pada pengujian ketiga model algoritma berhasil menghindarkan model dari kondisi underfitting maupun overfitting. Pengujian kinerja dari tiap – tiap model algoritma machine learning menggunakan confusion matrix yang meliputi parameter accuracy, sensitivity, dan precision.  Hasil pengujian menunjukkan bahwa algoritma Decision Trees memiliki tingkat akurasi terbaik yaitu sebesar 95%, diikuti dengan Naïve Bayes dengan tingkat akurasi sebesar 90% dan KNN dengan tingkat akurasi sebesar 85%.

Keywords


E-Learning; Customer Loyalty; NPS; Decision Trees; Naïve Bayes; KNN

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DOI: http://dx.doi.org/10.26418/jp.v8i1.49300

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