Comparison of Accuracy between Naive Bayes and Decision Tree Methods for Property Tax (PBB-P2) Compliance in Tebing Tinggi City

Zulham Sitorus; Sugeng Pranoto; Sulis Sutiono; Sarifuddin

doi:10.61306/jitcse.v1i2.57

Authors

Zulham Sitorus Universitas Pembangunan Panca Budi
Sugeng Pranoto Universitas Pembangunan Panca Budi
Sulis Sutiono Universitas Pembangunan Panca Budi
Sarifuddin Universitas Pembangunan Panca Budi

DOI:

https://doi.org/10.61306/jitcse.v1i2.57

Keywords:

Comparison, Accuracy, Naïve Bayes, Decision Tree, Tax Compliance

Abstract

This research aims to compare the accuracy of the Naïve Bayes and Decision Tree methods in predicting Land and Building Tax (PBB-P2) compliance in Tebing Tinggi city. The data used includes tax and payment determination for 2022 and 2023. The methods applied include data preprocessing, use of an inconvenience matrix for evaluation, as well as measuring accuracy with various data sharing ratios (80:20, 75:25, 70:30, 60:40, and 50:50). The research results show that the Decision Tree model consistently has much higher accuracy compared to the Naïve Bayes model, with accuracy reaching 99% at all data split ratios, while Naïve Bayes shows accuracy between 54% and 56%. The confusion matrix supports this finding by showing that the Decision Tree model has higher True Positives and True Negatives, and lower False Positives and False Negatives compared to Naïve Bayes. In conclusion, the Decision Tree method is more effective in classifying tax compliance compared to Naïve Bayes so that it is a more optimal choice for a tax compliance classification system based on the accuracy and performance obtained from this research.

References

Alfiah, N. (2021). Klasifikasi Penerima Bantuan Sosial Program Keluarga Harapan Menggunakan Metode Naive Bayes. Respati, 16(1), 32. https://doi.org/10.35842/jtir.v16i1.386

Coo, N., Suhardiyah, M., & Kurnia, Y. (2021). Pengaruh Kesadaran Wajib Pajak dan Sanksi Pajak Terhadap Kepatuhan Wajib PajaK pbb di Kelurahan Menanggal Surabaya. Journal of Sustainability Business Research, 2(1), 399–407.

Endang Etriyanti. (2021). Perbandingan Tingkat Akurasi Metode Knn Dan Decision Tree Dalam Memprediksi Lama Studi Mahasiswa. Jurnal Ilmiah Binary STMIK Bina Nusantara Jaya Lubuklinggau, 3(1), 6–14. https://doi.org/10.52303/jb.v3i1.40

Hanila, S., & Alghaffaru, M. A. (2023). Pelatihan Penggunaan Artificial Intelligence (AI) Terhadap Perkembangan Teknologi Pada Pembelajaran Siswa Sma 10 Sukarami Kota Bengkulu. Jurnal Dehasen Mengabdi, 2(2), 221–226. https://doi.org/10.37676/jdm.v2i2.4890

Isman, Andani Ahmad, & Abdul Latief. (2021). Perbandingan Metode KNN Dan LBPH Pada Klasifikasi Daun Herbal. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(3), 557–564. https://doi.org/10.29207/resti.v5i3.3006

Khoiriah, S. U., Lubis, L. K. L. U., & Anas, D. K. N. (2023). Analisis Perkembangan Sistem Manajemen Pendidikan di Era Society 5.0. JISPENDIORA: Jurnal Ilmu Sosial, Pendidikan Dan Humaniora, 2(2), 117–132. https://doi.org/10.56910/jispendiora.v2i2.650

Kolatung, J. F. (2021). Analisis Tingkat Kepatuhan Wajib Pajak Bumi Dan Bangunan Di Kota Manado. Jurnal EMBA: Jurnal Riset Ekonomi, Manajemen, Bisnis Dan Akuntansi, 9(2), 1006–1014.

Maulana, B. A., & Noriska, N. K. S. (2023). The Use of Machine Learning in the Analysis of Pawnshop Digital Service on Customer Satisfaction. Indonesian Journal of Contemporary Multidisciplinary Research, 2(4), 681–702. https://doi.org/10.55927/modern.v2i4.4612

Mugiarto, M., Agustina, I., & Suryaman, W. (2023). Adoption Of Asset Application Technology And Employee Competence In Improving Employee Performance Of State-Owned Asset Management In Sopd Cimahi City. Management Studies and Entrepreneurship Journal, 4(5), 4823–4835. http://journal.yrpipku.com/index.php/msej

Rahman, S., Sembiring, A., Siregar, D., Khair, H., Gusti Prahmana, I., Puspadini, R., & Zen, M. (2023). Python : Dasar Dan Pemrograman Berorientasi Objek. In Penerbit Tahta Media.

Rahmansyah Nur, Ningsih Sari, Lantana Dhieka Avrilia, Suryaningtyas Adisti, Wirawan Putri, Wijaya Sifonne Adi, P. D. N. (2023). Komparasi Metode Knn, Naive Bayes, Decision Tree, Ensemble, Linear Regression Terhadap Analisis Performa Pelajar Sma. INNOVATIVE: Journal Of Social Science Research Volume, 3(2), 13880–13892.

Rizkita, K., & Supriyanto, A. (2020). Komparasi kepemimpinan pendidikan di Indonesia dan Malaysia dalam upaya peningkatan mutu pendidikan. Jurnal Akuntabilitas Manajemen Pendidikan, 8(2), 155–164. https://doi.org/10.21831/jamp.v8i2.32362

Sabu, junius menase sau, & Tang, sefnat aristarkus. (2023). Analisis Pengaruh Pajak Bumi Dan Bangunan Perdesaan Dan Perkotaan, Pajak Hotel Dan Pajak Restoran Terhadap Pendapatan Asli Daerah Kabupaten Alor. Jurnal Ilmiah Wahana Pendidikan, 9(20), 368–382. http://www.nber.org/papers/w16019

Wibisono, A. D., Dadi Rizkiono, S., & Wantoro, A. (2020). Filtering Spam Email Menggunakan Metode Naive Bayes. TELEFORTECH : Journal of Telematics and Information Technology, 1(1). https://doi.org/10.33365/tft.v1i1.685

Yudana, F. R., Suyanto, M., & Nasiri, A. (2023). Model Klasifikasi Untuk Menentukan Kesiapan Kerja Mahasiswa Dan Kelulusan Tepat Waktu Dengan Metode Machine Learning. IJITECH: Indonesian Journal of Information Technology, 1(1), 1–12. https://doi.org/10.37680/ijitech.v1i1.xx

Yulian, S., Noorihsan, F., Setiawan, N. Y., & Saputra, M. C. (2023). Analisis Sentimen Ulasan Google Review New Star Cineplex Pasuruan menggunakan Artificial Neural Network (ANN). Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 7(2), 564–573. http://j-ptiik.ub.ac.id