Analysis of Community Satisfaction with the Service Systems in Civil Registry Service Office, South Buru Regency using the TAM (Technology Acceptance Model) Method

Juneth Manuputty(1*), Irwan Sembiring(2), Kristoko D Hartomo(3),

(1) Universitas Kristen Satya Wacana, Jl. Dr. O. Notohamidjodjo, Kota Salatiga, INDONESIA, 50715
(2) Universitas Kristen Satya Wacana, Jl. Dr. O. Notohamidjodjo, Kota Salatiga, INDONESIA, 50715
(3) Universitas Kristen Satya Wacana, Jl. Dr. O. Notohamidjodjo, Kota Salatiga, INDONESIA, 50715
(*) Corresponding Author
DOI: https://doi.org/10.23917/khif.v9i2.22595

Abstract

A good service system will satisfy the community, making that community’s contentment the deciding factor or the key factor in determining how successful an organization is in providing the service. This research aims to analyze the service system that has been available in the Department of Population and Civil Registration of Buru South district through the public satisfaction survey as well as to understand the services system that should be improved to minimize public dissatisfaction with the procedures provided by using the machine learning model, namely Random Forest Classifier technique to obtain a prediction of the satisfaction of the public with the services provided and perform validity testing on the prediction results obtained from the Random forest classifier technique using the Technology Acceptance Model. (TAM). The results of the trials carried out there are 3 determining factors to be able to increase public dissatisfaction namely the complaint service, the service process and the behavior of the officer supported by the validity test results using TAM with the results showing that the 3 services are valid means to be a factor that can be used to increase the public satisfaction with the result obtained from the T-computed value greater than T-table with the value for the Complaint Service 4.4794, service process 2.1345 and the Officer Behavior 1.9675 of the value of the table 1.6517.

Keywords

Public satisfaction; Machine Learning; TAM (Technology Acceptance Model).

References

Sugihartono, T., and Putra, R. R. C., "Analisis Kepuasan Pengguna Menggunakan Technology Acceptance Model pada Sistem Pelayanan Publik," Satin-Sains Dan Teknologi Informasi, vol. 6, no. 2, pp. 97-105, 2020.

Pratama, H. S. P., and Rakhmadani, D. P., "Penerapan Metode Technology Acceptance Model (TAM) Dalam Penggunaan Aplikasi Linkaja," JURIKOM (Jurnal Riset Komputer), vol. 9, no. 2, pp. 176-186, 2022.

Hanum, F., Rambe, B. H., Harahap, N. J., Prayoga, Y., and Pohan, M. Y. A., "The Important Role of Adopting the Use of Technology in Universities after COVID-19: Application of the Technology Acceptance Model," ECOBISMA (Jurnal Ekonomi, Bisnis dan Manajemen), vol. 10, no. 1, pp. 74-80, 2023.

Chairunnisa, T. L. T., Rosmika, E., and Azulaidin, A., "Analisis Kepuasan Masyarakat Terhadap Penggunaan Aplikasi SiBisa dengan Pendekatan TAM," Ekonomi, Keuangan, Investasi dan Syariah (EKUITAS), vol. 3, no. 2, pp. 174-180, 2021.

Oralytics. "Data Science is Multidisciplinary." [Online]. Available: https://oralytics.com/2012/06/13/data-science-is-multidisciplinary/.

D. E. Goldberg and J. H. Holland, "Genetic algorithms and machine learning," Machine Learning, vol. 3, no. 2, pp. 95-99, 1988.

A. Ahmad, "Mengenal artificial intelligence, machine learning, neural network, dan deep learning," Yayasan Cahaya Islam, Jurnal Teknologi Indonesia, vol. 3, 2017.

A. Roihan, P. A. Sunarya, and A. S. Rafika, "Pemanfaatan Machine Learning dalam Berbagai Bidang," Jurnal Khatulistiwa Informatika, vol. 5, no. 1, pp. 490845, 2020.

F. D. Davis, "Perceived usefulness, perceived ease of use, and user acceptance of information technology," MIS Quarterly, pp. 319-340, 1989.

C. Janiesch, P. Zschech, and K. Heinrich, "Machine learning and deep learning," Electron Markets, vol. 31, pp. 685–695, 2021. doi: 10.1007/s12525-021-00475-2.

S. Mulyono, W. A. Syafei, and R. Kusumaningrum, "Analisa tingkat penerimaan pengguna terhadap aplikasi SIMPUS dengan metode technology acceptance model (TAM)," JOINS (Journal of Information System), vol. 5, no. 1, pp. 147–155, 2020. doi: 10.33633/joins.v5i1.3277.

G. N. Ayuni and D. Fitrianah, "Penerapan metode Regresi Linear untuk prediksi penjualan properti pada PT XYZ," Jurnal Telematika, vol. 14, no. 2, pp. 79-86, 2019.

P. D. W. G. Yuniahans, R. Parlika, R. S. Arhinza, V. F. Majid, and M. G. Alifian, "Uji Validitas Aplikasi Si-Book Menggunakan SPSS dengan Kombinasi Metode R-Tabel dan Cohen’s Kappa," Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika, vol. 16, no. 2, pp. 121-133, 2022.

M. Radhi, A. Amalia, D. R. H. Sitompul, S. H. Sinurat, and E. Indra, "Analisis Big Data Dengan Metode Exploratory Data Analysis (EDA) dan Metode Visualisasi Menggunakan Jupyter Notebook," Jurnal Sistem Informasi Dan Ilmu Komputer Prima (JUSIKOM PRIMA), vol. 4, no. 2, pp. 23-27, 2021.

M. Azhari, Z. Situmorang, and R. Rosnelly, "Perbandingan Akurasi, Recall, dan Presisi Klasifikasi pada Algoritma C4.5, Random Forest, SVM dan Naive Bayes," Jurnal Media Informatika Budidarma, vol. 5, no. 2, pp. 640-651, 2021.

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