Analysis of Customer Satisfaction Quality in the Painting & Welding Monitoring System with the Naïve Bayes Algorithm
DOI:
https://doi.org/10.59890/ijels.v3i1.169Keywords:
Customer Satisfaction, Painting And Welding Monitoring System, Naive Bayes AlgorithmAbstract
The painting and welding monitoring system represents an advancement intended to improve efficiency and quality in manufacturing procedures. This study evaluates customer satisfaction levels concerning the system by employing the Naive Bayes algorithm. Data was gathered through customer surveys, concentrating on essential factors such as reliability, ease of use, and the accuracy of the information delivered by the system. The Naive Bayes algorithm was applied to forecast customer satisfaction based on the collected survey data. The findings suggest that customer satisfaction levels can be determined with high precision, with reliability being the most significant factor. These results offer important insights for system developers to enhance the functionality of the painting and welding monitoring system.
References
AMELIA, L., SAVITRI, N., & AINI, S. N. U. R. (2021). ANALISIS STRATEGI DISTRIBUSI BAHAN BANGUNAN KE KONSUMEN PADA RETAIL ROEMAHKITA PT. SEMEN INDONESIA DISTRIBUTOR (SID).
Ariani, F., Amir, N. A., Rizal, K., Sitasi, C., & Sunge, A. S. (2018). Klasifikasi Penetapan Status Karyawan Dengan Menggunakan Metode Naïve Bayes. Paradigma, 20(2).
Chrishariyani, C. D. A. A. P., Rahman, Y., & Aini, Q. (2022). Kepuasan Pengguna Layanan Shopee Food Menggunakan Algoritma Naive Bayes. Jurnal Sistem Informasi Bisnis, 12(2), 98–105.
Ghifari, A., Ahmad, I., & Neneng, N. (2023). Sistem Monitoring Pekerjaan Pada PT Pelabuhan Indonesia (Persero) Regional 2 Panjang. Jurnal Informatika Dan Rekayasa Perangkat Lunak, 4(3), 260–269.
Hopipah, H. S., & Mayasari, R. (2021). Optimasi Backward Elimination untuk Klasifikasi Kepuasan Pelanggan Menggunakan Algoritme k-nearest neighbor (k-NN) and Naive Bayes. Technomedia Journal, 6(1 Agustus), 99–110.
Jollyta, D., Ramdhan, W., & Zarlis, M. (2020). Konsep data mining dan penerapan. Deepublish.
Mastarida, F., Sahir, S. H., Hasibuan, A., Siagian, V., Hariningsih, E., Fajrillah, F., Gustiana, Z., Tjiptadi, D. D., & Pakpahan, A. F. (2022). Strategi Transformasi Digital. Yayasan Kita Menulis.
Saputra, R. A., Taufik, A. R., Ramdhani, L. S., Oktapiani, R., & Marsusanti, E. (2018). Sistem Pendukung Keputusan Dalam Menentukan Metode Kontrasepsi Menggunakan Algoritma Naive Bayes. SNIT 2018, 1(1), 106–111.
Ulfah, M., Muharam, S., Maisyaroh, M., Saputri, R. Z., Gifari, N. A., & Mardatillah, A. (2023). SISTEM MANAJEMEN MUTU DALAM MENINGKATKAN KEPUASAN PELANGGAN. PRIMER: Jurnal Ilmiah Multidisiplin, 1(2), 190–197.
Wantoro, A. (2021). Sistem Monitoring Perawatan Dan Perbaikan Fasilitas Gardu PT PLN Area Kota Metro. Jurnal Tekno Kompak, 15(1), 116–130.
Zy, A., Sasongko, A. T., & Kamalia, A. Z. (2023). Penerapan Naïve Bayes Classifier, Support Vector Machine, dan Decision Tree untuk Meningkatkan Deteksi Ancaman Keamanan Jaringan. KLIK: Kajian Ilmiah Informatika Dan Komputer, 4(1), 610–617.