Prediction Analysis of Customer Satisfaction Levels at Company XXX Using the Classification Method

Main Article Content

Evi Purnamasari
Ni Wayan Priscila Yuni Praditya
Dwi Asa Verano

Abstract

Service in companies operating in the service system plays a very important role, including in one of the companies in the city of Palembang which we call Company XXX. The level of customer satisfaction with service at Company XXX needs to be considered in order to find out how satisfied customers are with the service system provided by Company XXX. On this occasion the researcher aims to analyze and predict the level of customer satisfaction at Company XXX using the C4.5 classification method. Customer satisfaction is an important factor in maintaining customer loyalty and improving company performance. Using historical customer data for the last 1 year, we apply the C4.5 algorithm to predict customer satisfaction levels. The research results show that the C4.5 method has quite high prediction accuracy, which reaches 83%. It is hoped that the findings from this research can help XXX Company identify the factors that influence customer satisfaction and be able to take strategic steps to improve the quality of service.

Article Details

How to Cite
Evi Purnamasari, Priscila Yuni Praditya, N. W., & Dwi Asa Verano. (2024). Prediction Analysis of Customer Satisfaction Levels at Company XXX Using the Classification Method. Jurnal Informasi Dan Teknologi, 6(2), 159-164. https://doi.org/10.60083/jidt.v6i2.541
Section
Articles

References

[1] I. G. Ngurah, S. Wijaya, E. Triandini, E. Tifanie, and G. Kabnani, “E-commerce website service quality and customer loyalty using WebQual 4 . 0 with importance performances analysis , and structural equation model : An empirical study in Shopee,” J. Ilm. Teknol. Sist. Inf., vol. 7, no. July, pp. 107–124, 2021.
[2] M. Javaid, A. Haleem, and R. Pratap, “BenchCouncil Transactions on Benchmarks , Standards and Evaluations ChatGPT for healthcare services : An emerging stage for an innovative perspective,” BenchCouncil Trans. Benchmarks, Stand. Eval., vol. 3, no. 1, p. 100105, 2023, doi: 10.1016/j.tbench.2023.100105.
[3] A. Sestino, M. Irene, L. Piper, and G. Guido, “Technovation Internet of Things and Big Data as enablers for business digitalization strategies,” Technovation, vol. 98, no. August, p. 102173, 2020, doi: 10.1016/j.technovation.2020.102173.
[4] B. Mahesh, “Machine Learning Algorithms - A Review,” Int. J. Sci. Res., no. January 2019, 2020, doi: 10.21275/ART20203995.
[5] N. Almumtazah, N. Azizah, Y. L. Putri, I. Negeri, and S. Ampel, “Prediksi jumlah mahasiswa baru menggunakan metode regresi linier sederhana,” J. Ilm. Mat. dan Terap., vol. 18, no. 1, pp. 31–40, 2021, doi: https://doi.org/10.22487/2540766X.2021.v18.i1.15465.
[6] F. Fahreni, V. Mardina, I. Indriaty, and R. Ramaidani, “Examination of Gel Hand Sanitizer from Mangrove Leaves and Patchouli Oil Against Sthapylococcus Aureus,” Int. J. Eng. Sci. Inf. Technol., vol. 1, no. 4, 2021, doi: 10.52088/ijesty.v1i4.139.
[7] C. Lovelock, SERVICES MARKETING. 2022.
[8] I. G. Dharma Utamayasa, “Efect Physical Activity and Nutrition During The Covid-19 Pandemic,” Int. J. Eng. Sci. Inf. Technol., vol. 1, no. 1, 2021, doi: 10.52088/ijesty.v1i1.58.
[9] E. C. Ates, G. E. Bostanci, and M. S. Guzel, “Big Data , Data Mining , Machine Learning , and Deep Learning Concepts in Big Data , Data Mining , Machine Learning , and Deep Learning Concepts in Crime Data,” vol. 8(2), no. December, pp. 293–319, 2020, doi: 10.26650/JPLC2020-813328.
[10] A. S. Borodulin, “Phase-Sensitive OTDR Using Pattern Recognition Methods,” sensors Artic., vol. 23, no. 582, 2023, doi: https://doi.org/10.3390/s23020582.
[11] E. Purnamasari, “Prediksi Perkembangan Nilai Impor Komoditas Utama,” J. Inf. dan Teknol. Vol., vol. 5, no. 1, pp. 165–172, 2023, doi: 10.37034/jidt.v5i1.271.
[12] E. Purnamasari, D. P. Rini, and Sukemi, “Prediction of the Student Graduation’s Level using C4.5 Decision Tree Algorithm,” ICECOS 2019 - 3rd Int. Conf. Electr. Eng. Comput. Sci. Proceeding, pp. 192–195, 2019, doi: 10.1109/ICECOS47637.2019.8984493.
[13] M. M. S and A. Yasar, “Performance Analysis of ANN and Naive Bayes Classification Algorithm for International Journal of Intelligent Systems and Applications in Engineering Performance Analysis of ANN and Naive Bayes Classification Algorithm for Data Classification,” Int. J. Intell. Syst. Appl. Eng., no. January 2019, pp. 88–91, 2021, doi: 10.1039/b000000x.
[14] Hartono, E. Ongko, and D. Abdullah, “HFLTS-DEA model for benchmarking qualitative data,” Int. J. Adv. Soft Comput. its Appl., vol. 11, no. 2, 2019.
[15] E. Purnamasari, D. Palupi Rini, P. Studi Magister Ilmu Komputer, F. Ilmu Komputer, and U. Sriwijaya Palembang, “Seleksi Fitur menggunakan Algoritma Particle Swarm Optimization pada Klasifikasi Kelulusan Mahasiswa dengan Metode Naive Bayes,” J. RESTI (Rekayasa Sist. Dan Teknol. Informasi), vol. 1, no. 3, pp. 469–475, 2020, doi: https://doi.org/10.29207/resti.v4i3.1833.
[16] E. A. Rady and A. S. Anwar, “Informatics in Medicine Unlocked Prediction of kidney disease stages using data mining algorithms,” Informatics Med. Unlocked, vol. 15, no. March, pp. 1–7, 2019, doi: 10.1016/j.imu.2019.100178.
[17] M. Jannah et al., “Prediksi Penjualan Produk Pada PT Bintang Sriwijaya Palembang Menggunakan K-Nearest Neighbour,” J. Softw. Eng. Comput. Intell., vol. 01, no. 02, pp. 80–89, 2023.
[18] S. Albahra et al., “Seminars in Diagnostic Pathology Artificial intelligence and machine learning overview in pathology & laboratory medicine : A general review of data preprocessing and basic supervised concepts,” vol. 40, no. February, pp. 71–87, 2023, doi: 10.1053/j.semdp.2023.02.002.
[19] E. N. Boice et al., “Training Ultrasound Image Classification Deep-Learning Algorithms for Pneumothorax Detection Using a Synthetic Tissue Phantom Apparatus,” J. Imaging, vol. 8, no. 249, pp. 1–13, 2022, doi: https://doi.org/10.3390/jimaging8090249.
[20] J. Wu, X. C. Hao, Z. L. Xiong, and H. Lei, “Hyperparameter Optimization for Machine Learning Models Based on Bayesian Optimization,” J. Electron. Sci. Technol., vol. 17, no. 1, pp. 26–40, 2019, doi: 10.11989/JEST.1674-862X.80904120.
[21] I. J. Holb and V. Eva, “Classification Assessment Tool : A program to measure the uncertainty of classification models in terms of class-level metrics ´ rd Szab o,” vol. 155, no. April 2023, 2024, doi: 10.1016/j.asoc.2024.111468.
[22] B. T. Jijo and A. M. Abdulazeez, “Classification Based on Decision Tree Algorithm for Machine Learning,” vol. 02, no. 01, pp. 20–28, 2021, doi: 10.38094/jastt20165.
[23] S. Tangirala, “Evaluating the Impact of GINI Index and Information Gain on Classification using Decision Tree Classifier Algorithm *,” vol. 11, no. 2, pp. 612–619, 2020.
[24] M. Fokkema, J. Edbrooke-childs, and M. Wolpert, “Generalized linear mixed-model ( GLMM ) trees : A flexible decision-tree method for multilevel and longitudinal data,” Psychother. Res., vol. 31, no. 3, pp. 329–341, 2021, doi: 10.1080/10503307.2020.1785037.
[25] F. Avellaneda, “Efficient Inference of Optimal Decision Trees,” Proc. AAAI Conf. Artif. Intell., pp. 3195–3202, 2018, doi: https://doi.org/10.1609/aaai.v34i04.5717.