Abstract
Tax Service Office is a work unit of the Directorate General of Taxation that carries out services in the field of taxation to the public, both registered and unregistered taxpayers, within the working area of the Directorate General of Taxes. The number of Primary Tax Service Offices in Indonesia, one of which is the Primary Tax Service Office in Bekasi, has various ways to increase the satisfaction of taxpayers for the services provided. This study aims to determine the accuracy of taxpayers' satisfaction using data mining techniques using the Decision Tree C4.5 Algorithm with Particle Swarm Optimization (PSO) feature selection, validation uses cross validation techniques while accuracy is measured by the confussion matrix, which is to determine the level of service satisfaction conducted by distributing questionnaires to taxpayers in the Primary Tax Service Office in Bekasi as many as 500 questionnaires. The results show the accuracy value of Taxpayers' service satisfaction at the Pratama Tax Service Office using the Decision Tree C4.5 Algorithm with a feature selection of Particle Swarm Optimization (PSO) of 98,85%, Precission of 98,85% and Recall of 100%.
Highlights
Tax Service Office is a work unit of the Directorate General of Taxation that carries out services in the field of taxation to the public, both registered and unregistered taxpayers, within the working area of the Directorate General of Taxes
The number of Primary Tax Service Offices in Indonesia, one of which is the Primary Tax Service Office in Bekasi, has various ways to increase the satisfaction of taxpayers for the services provided
This study aims to determine the accuracy of taxpayers' satisfaction using data mining techniques using the Decision Tree C4.5 Algorithm with Particle Swarm Optimization (PSO) feature selection, validation uses cross validation techniques while accuracy is measured by the confussion matrix, which is to determine the level of service satisfaction conducted by distributing questionnaires to taxpayers in the Primary Tax Service Office in Bekasi as many as 500 questionnaires
Summary
Untuk menangani masalah klasifikasi data termasuk Penelitian yang dilakukan Noor yaitu Optimasi Model KNN, Naïve Bayes, SVM (Support Vector Machine) Klasifikasi C4.5 dan Particle Swarm Optimization tetapi untuk penelitian kali ini akan digunakan untuk Prediksi Siswa Bermasalah memiliki hasil algoritma Decision Tree C4.5 dimana memiliki tingkat akurasi 99.08% [9]. Telah dilakukan Perbandingan NB dan NB-PSO untuk Penelitian oleh Mujab tentang Pencarian Model menentukan tingkat penjualan kendaraan dimana Terbaik antara Algoritma C4.5 dan C4.5 berbasis menghasilkan Hasil akurasi klasifikasi dengan metode Particle Swarm Optimization untuk Prediksi Promosi Naïve Bayes menghasilkan nilai akurasi 92,11%, nilai Deposito menghasilkan nilai akurasi sebesar 89,26% Presisi: 86,57% dan nilai Panggilan: 97,12%.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
