ANALISIS KLASIFIKASI KEPUASAN PELANGGAN TERHADAP PELAYANAN CUSTOMER SERVICE UNTUK PENINGKATAN LAYANAN MENGGUNAKAN DATA MINING DENGAN DECISION TREE
Abstract
This study analyzes customer satisfaction with customer service using data mining techniques and the Decision Tree algorithm. The data was obtained from customer questionnaires completed after transactions and were processed through pre-processing, attribute labeling, and missing value handling. The dataset was split into 80% training data and 20% testing data to build and evaluate a classification model with two target categories: satisfied and dissatisfied. The modeling results show that the Consideration Label is the most dominant factor in determining customer satisfaction, while the Suggestion Label serves as a supporting attribute. Model evaluation produced an accuracy of 58%, with precision, recall, and F1-score for the dissatisfied class of 0.62, 0.66, and 0.64, respectively, and for the satisfied class of 0.53, 0.49, and 0.51, respectively. Based on these results, the Decision Tree method can be used to classify customer satisfaction, although further improvement in model performance is still needed to obtain more optimal predictions.
Downloads
References
[2] D. S. P. E. S. Budi, A. R. Kadafi, and R. F. Yasdi Kharismawan, “Analisa Kepuasan Pelanggan Terhadap Layanan Aplikasi E-Commerce Menggunakan Algoritma C4. 5,” RESOLUSI : Rekayasa Teknik Informatika dan Informasi, vol. 4, no. 6, pp. 530–542, 2024.
[3] Fardeen and Ricky Eka Putra, “Perbandingan Analisis Sentimen Untuk Prediksi Kepuasan Pelanggan Kedai Kopi Di Kofind Menggunakan Algoritma SVM Dan Naive Bayes,” Journal of Informatics and Computer Science, vol. 6, no. 4, pp. 1039–1048, 2025, [Online]. Available: https://ejournal.unesa.ac.id/index.php/jinacs/article/view/66547%0Ahttps://ejournal.unesa.ac.id/index.php/jinacs/article/download/66547/49772
[4] R. Qodrat Kiswara, M Safii, Sundari Retno Andani, Muhammad Ridwan Lubis, “Implementasi Algoritma C4.5 Untuk Mengukur Tingkat Kepuasan Mahasiswa yang Berlangganan Wifi Indihome,” TIN: Terapan Informatika Nusantara, vol. 4, no. 9, pp. 620–627, 2024, doi: 10.47065/tin.v4i9.4918.
[5] Muzakki Hafizh Setiono, “KOMPARASI ALGORITMA DECISION TREE, RANDOM FOREST, SVM DAN K-NN DALAM KLASIFIKASI KEPUASAN PENUMPANG MASKAPAI PENERBANGAN,” Inti Nusa Mandiri, vol. 14, no. 2, pp. 133–138, 2020.
[6] R. Tamami, D. Yanti, and R. Anugrah, “Analisis Kepuasan Pelanggan Menggunakan Algoritma C4.5 Pada Aplikasi Betukang.Id,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 8, no. 6, pp. 12507–12511, 2024, doi: 10.36040/jati.v8i6.11937.
[7] B. Zaman, L. margaretta Huizen, and M. B. Ardima, “Prediksi Kepuasan Mahasiswa Terhadap Pelayanan Akademik Menggunakan Model Decision Tree,” Jurnal Transformatika, vol. 21, no. 2, pp. 46–55, 2024, doi: 10.26623/transformatika.v21i2.8214.
[8] N. Azwanti and N. E. Putria, “Analisis Kepuasan Customer pada Sdtechnology Computer dengan Algoritma Decision Tree,” Jurnal Desain Dan Analisis Teknologi, vol. 3, no. 2, pp. 137–148, 2024, doi: 10.58520/jddat.v3i2.62.
[9] I. Syafii, A. A. Ribhi, L. Y. Astutik, G. K. S. Budiono, and A. S. Pamela, “Analisis Prediksi Customer Repeat Order menggunakan Algoritma Decision Tree pada Perusahaan Transportasi,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 4, no. 4, pp. 1372–1378, 2024, doi: 10.57152/malcom.v4i4.1538.
[10] M. Abdurohman, R. Husna, I. Ali, G. Dwilestari, and N. Rahaningsih, “Penerapan Model Klasifikasi Dalam Tingkat Kepuasan Layanan Publik Kelurahan Karyamulya Dengan Menggunakan Algoritma Decision Tree,” INFORMATION MANAGEMENT FOR EDUCATORS AND PROFESSIONALS : Journal of Information Management, vol. 6, no. 1, p. 81, 2022, doi: 10.51211/imbi.v6i1.1678.
[11] U. H. Muhammad Fihir, Martanto, “Klasifikasi Tingkat Kepuasan Pelanggan Kopi Kenangan Menggunakan Metode Decision Tree Pada Aplikasi Kopi Kenangan,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 7, no. 6, pp. 3830–3833, 2023.
[12] A. Wafi and L. C. Munggaran, “Meningkatkan Akurasi Analisis Kepuasan Pengguna Mobile Banking Melalui Perbandingan Algoritma Decision Tree Naive Bayes dan K-Nearest-Neighbour,” vol. 4, no. 1, pp. 18–25, 2026.
[13] F. Muzakki, I. Ubaydillah, N. R. Assyiami, and S. Soleha, “Penerapan Algoritma C4.5 Untuk Prediksi Penyakit Jantung Menggunakan Rapidminer,” Jurnal Komputer Antartika, vol. 2, no. 2, pp. 71–79, 2024, doi: 10.70052/jka.v2i2.304.
[14] S. A. Pratiwi, A. Fauzi, S. Arum, P. Lestari, and Y. Cahyana, “Prediksi Persediaan Obat Pada Apotek Menggunakan Algoritma Decision Tree,” Media Online, vol. 4, no. 4, pp. 2381–2388, 2024, doi: 10.30865/klik.v4i4.1681.
[15] M. R. Fanani and D. S. Sintia, “Klasifikasi Kesiapan Anak Masuk Sekolah Dasar menggunakan Algoritma Naïve Bayes dan Algoritma C4.5,” Innovative: Journal Of Social Science Research, vol. 4, no. 3, pp. 10547–10555, 2024, doi: 10.31004/innovative.v4i3.10425.

This work is licensed under a Creative Commons Attribution 4.0 International License.





















.png)
.png)
