Customer Satisfaction Analysis of PLN Mobile Services Using the Naïve Bayes Classifier Method
Keywords:
Customer Satisfaction Analysis, PLN Mobile App, Classification, Naive Bayes Classifier Method
Abstract
This research was conducted to determine customer satisfaction with the PLN Mobile application service by employing the Nave Bayes Classifier algorithm in order to precisely determine the level of customer satisfaction with the PLN Mobile application service based on customer data collected through the PLN Mobile application. Using a questionnaire issued to consumers who use the PLN Mobile application, the author collects data for this study. The purpose of the author's research is to acquire the outcomes of data analysis using the Nave Bayes Classifier equation. Class "Yes (Satisfied)" and Class "No (Not Satisfied)" were established by the authors of this study to measure consumer satisfaction with PLN Mobile application services. To determine the classification of satisfaction, the author employs the factors of gender, age, and occupation, in addition to sixteen of the author's own assertions. Based on studies utilizing the Nave Bayes Classifier, the authors obtained a precision of 90.48%.
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References
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[15] I. B. G. Sarasvananda, I. G. K. A. G. Wiguna, and S. Styawati, “Pendekatan Metode Extreme Programming untuk Pengembangan Sistem Informasi Manajemen Surat Menyurat pada LPIK STIKI,” J. Inform. Univ. Pamulang, vol. 6, no. 2, pp. 258–267, 2021.
[2] R. Afthoni, M. Hamdhani, A. Ardianto, A. F. Karimah, and H. Patria, “Pemanfaatan Algoritma Machine Learning untuk Segmentasi Pelanggan Berbasis Data Konsumsi Listrik di PT PLN XYZ,” in Seminar Nasional Teknik dan Manajemen Industri, 2021, vol. 1, no. 1, pp. 222–231.
[3] M. Siddik, H. Hendri, R. N. Putri, Y. Desnelita, and G. Gustientiedina, “Klasifikasi Kepuasan Mahasiswa Terhadap Pelayanan Perguruan Tinggi Menggunakan Algoritma Naïve Bayes,” INTECOMS J. Inf. Technol. Comput. Sci., vol. 3, no. 2, pp. 162–166, Nov. 2020, doi: 10.31539/INTECOMS.V3I2.1654.
[4] K. Aeni, M. Kom, M. Faisal Asy’ari, and S. Kom, “Prediksi Kepuasan Layanan Akademik Menggunakan Algoritma Naïve Bayes,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 7, no. 3, pp. 601–609, Dec. 2020, doi: 10.35957/JATISI.V7I3.603.
[5] Y. T. Samuel and K. Dewi, “Penggunaan Metode NAÏVE BAYES Dalam Mengukur Tingkat Kepuasan Pengguna Terhadap Online System Universitas Advent Indonesia,” TeIKa, vol. 9, no. 2, pp. 147–153, Oct. 2019, doi: 10.36342/teika.v9i02.2162.
[6] B. Hermanto and A. Romadhoni, “Penerapan Metode Naïve Bayes Untuk Prediksi Kepuasan Pelanggan ( Studi Kasus Bengkel Win Motor ),” J. SIGMA, vol. 9, no. 4, pp. 86–96, Jun. 2019.
[7] I. K. A. G. W. Arya, G. W. Wiguna, and I. G. I. S. I. Sudipa, “Sentiment Analysis Using Backpropagation Method To Recognize The Public Opinion,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 16, no. 4.
[8] M. Wu, Y. Huang, and J. Duan, “Investigations on Classification Methods for Loan Application Based on Machine Learning,” Proc. - Int. Conf. Mach. Learn. Cybern., vol. 2019-July, pp. 1–6, 2019, doi: 10.1109/ICMLC48188.2019.8949252.
[9] A. Purnamasari and S. Assegaff, “Metode Naive Bayes Pada Kecamatan Pasar Jambi,” vol. 4, no. 4, pp. 480–491, 2019.
[10] I. M. D. P. Asana, I. G. I. Sudipa, A. A. T. W. Mayun, N. P. S. Meinarni, and D. V. Waas, “Aplikasi Data Mining Asosiasi Barang Menggunakan Algoritma Apriori-TID,” INFORMAL Informatics J., vol. 7, no. 1, pp. 38–45, 2022.
[11] I. M. D. P. Asana, I. K. A. G. Wiguna, K. J. Atmaja, and I. P. A. Sanjaya, “FP-Growth Implementation in Frequent Itemset Mining for Consumer Shopping Pattern Analysis Application,” J. Mantik, vol. 4, no. 3, pp. 2063–2070, 2020.
[12] M. M. Saritas and A. Yasar, “Performance analysis of ANN and Naive Bayes classification algorithm for data classification,” Int. J. Intell. Syst. Appl. Eng., vol. 7, no. 2, pp. 88–91, 2019.
[13] I. K. A. G. Wiguna, D. P. D. K. Dewi, and I. G. I. Sudipa, “Implementasi OLAP pada Data Kerja Praktik dan Tugas Akhir Menggunakan Framework Modular Cube JS,” INFORMAL Informatics J., vol. 6, no. 3, pp. 142–153, 2021, doi: https://doi.org/10.19184/isj.v6i3.27614.
[14] I. G. I. Sudipa, I. N. A. Arsana, and M. L. Radhitya, “Penentuan tingkat pemahaman mahasiswa terhadap social distancing menggunakan algoritma C4. 5,” SINTECH (Science Inf. Technol. J., vol. 3, no. 1, pp. 1–7, 2020.
[15] I. B. G. Sarasvananda, I. G. K. A. G. Wiguna, and S. Styawati, “Pendekatan Metode Extreme Programming untuk Pengembangan Sistem Informasi Manajemen Surat Menyurat pada LPIK STIKI,” J. Inform. Univ. Pamulang, vol. 6, no. 2, pp. 258–267, 2021.
Published
2022-11-22
How to Cite
Wiguna, I. K. A. G., Mustafida, A. N., Santika, P. P., Ariantini, M. S., & Sudipa, I. G. I. (2022). Customer Satisfaction Analysis of PLN Mobile Services Using the Naïve Bayes Classifier Method. INFOKUM, 10(5), 52-58. Retrieved from http://seaninstitute.org/infor/index.php/infokum/article/view/850
Section
Articles
