Analisis Kinerja Decision Tree dan Naïve Bayes Pada Klasifikasi Tingkat Kepuasan Masyarakat
- Nora Fitaria Universitas Teknokrat Indonesia, Bandar Lampung, Indonesia
-
Dedi Darwis
*
Universitas Teknokrat Indonesia, Bandar Lampung,
Indonesia
(*) Corresponding Author
Abstract
The Public Satisfaction Survey (SKM) is an official instrument used by the government to evaluate public service performance as stipulated in Regulation of the Minister of State Apparatus Empowerment and Bureaucratic Reform (PermenPANRB) Number 14 of 2017. However, the use of SKM data in many government agencies is still limited to calculating satisfaction index values without further predictive analysis. This study aims to classify the level of satisfaction of service users of the Metro City Investment and Integrated Services Agency (DPMPTSP) using the Decision Tree and Naïve Bayes algorithms. The data used is SKM data from 2025 to the fourth quarter, consisting of 2,760 respondents, which consists of nine service elements (U1–U9) with satisfaction categories as class variables. The research process includes data pre-processing, classification modeling using RapidMiner, and model evaluation based on confusion matrix, accuracy, precision, and recall. The results showed that the Naïve Bayes algorithm produced an accuracy rate of 95.04%, higher than the Decision Tree, which obtained an accuracy of 84.46%, and had a better recall value in the dominant class (recall of the Satisfied class was 98.16%). These advantages demonstrate the efficiency of the Naïve Bayes probabilistic approach in handling categorical features in public service elements. This study proves that the application of Data mining techniques to SKM data can support data-based public service evaluation.
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References
{Kementerian Pendayagunaan Aparatur Negara dan Reformasi Birokrasi Republik Indonesia}, “Peraturan Menteri Pendayagunaan Aparatur Negara dan Reformasi Birokrasi Nomor 14 Tahun 2017 tentang Pedoman Penyusunan Survei Kepuasan Masyarakat Unit Penyelenggara Pelayanan Publik,” Kementerian PANRB, Jakarta, 2017. [Online]. Available: https://peraturan.bpk.go.id/Details/132600/permen-pan-rb-no-14-tahun-2017
DINAS PENANAMAN MODAL DAN PELAYANAN TERPADU SATU PINTU, “Laporan Pelaksanaan Survei Kepuasan Masyarakat ( Skm ) Tahun 2025 Pemerintah Kota Metro Dinas Penanaman Modal Dan Pelayanan Terpadu Satu Pintu (Dpmptsp) Tahun 2025,” Metro, 2025. [Online]. Available: https://mpp.metrokota.go.id/v1/layanan.php?id=12
A. Setiawan, A. Nugraha Saputra, Z. A. Dewi, Y. Nuryamin, and Ade Priyatna, “Perbandingan Akurasi Algoritma Decision Tree dan Naive Bayes untuk Prediksi Gagal Jantung,” J. Teknol. Komput. dan Sist. Inf. (SINTA 5), vol. 10, no. 2, pp. 134–142, 2025, doi: Prefix 10.23960/jtii.
A. P. Pristiawati, I. Permana, Z. Zarnelly, and F. Muttakin, “Klasifikasi Penerima Bantuan Beras Miskin Menggunakan Algoritma K-NN, NBC dan C4.5,” Build. Informatics, Technol. Sci., vol. 5, no. 1, pp. 336–344, 2023, doi: 10.47065/bits.v5i1.3617.
K. Aprilia, U. Khaira, and B. Ferdian Hutabarat, “Komparasi Algoritma Naive Bayes Dan Decision Tree Pada Klasifikasi Penerimaan Peserta Didik Baru,” Simtek J. Sist. Inf. dan Tek. Komput., vol. 10, no. 2, pp. 547–553, 2025, doi: 10.51876/simtek.v10i2.1694.
L. Winda Sari Siburian, D. Saripurna, S. Kusnasari, P. Studi Sistem Informasi, and S. Triguna Dharma, “Analisis Tingkat Kepuasan Masyarakat Terhadap Pelayanan Kantor Desa Dengan Menggunakan Algoritma C4.5,” vol. 3, no. 2, pp. 263–273, 2024, doi: https://doi.org/10.53513/jursi.v3i2.6023.
I. A. Wisky, E. Rianti, and A. Syahira, “Analisis Kepuasan Pelayanan Publik Menggunakan Metode Naïve Bayes Pada Dinas Kependudukan Dan Pencatatan Sipil Kabupaten Agam,” J. Sains Inform. Terap., vol. 4, no. 3, pp. 487–492, Oct. 2025, doi: 10.62357/joseamb.v2i2.
M. W. Khoiriyah, I. H. Santi, and R. D. Romadhona, “Analisis Algoritma C4.5 Dan Naïve Bayes Dalam Menentukan Tingkat Kepuasan Publik Di Rupbasan Kelas 2 Blitar,” JIP (Jurnal Inform. Polinema), vol. 11, Nov. 2024, doi: https://doi.org/10.33795/jip.v11i1.5831.
T. A. Putra and M. Permana, Inggih Zarnelly, “Klasifikasi Penerima Bantuan Program Indonesia Pintar (PIP) Pada Siswa SMK Menggunakan Algoritma KNN, NBC dan C4.5,” Technol. Sci., vol. 6, no. 4, pp. 2131–2138, 2025, doi: 10.47065/bits.v6i4.6395.
P. A. M. Zidane, R. W. P. P. Zer, and I. Gunawan, “Penerapan Data Mining Naïve Bayes Dalam Klasifikasi Kepuasan Mahasiswa Berlangganan WiFi Indihome,” J. MEDIA Inform. [JUMIN], vol. 3, pp. 112–118, 2022, doi: https://doi.org/10.55338/jumin.v3i2.488.
T. S. Lestari, I. Ismaniah, and W. Priatna, “Particle Swarm Optimization for Optimizing Public Service Satisfaction Level Classification,” J. Nas. Pendidik. Tek. Inform., vol. 13, no. 1, Mar. 2024, doi: 10.23887/janapati.v13i1.69612.
P. R. Rosmila, R. Risqiati, and A. S. Darmawan, “Komparasi Algoritma Naïve Bayes Dan Decision Tree Untuk Menganalisis Kepuasan Masyarakat Dpmptsp Kabupaten Batang,” Inf. Syst. J., vol. 8, no. 02, pp. 109–118, Dec. 2025, doi: 10.24076/infosjournal.2025v8i02.2364.
Y. Umaidah and U. Enri, “Prediction Of Public Service Satisfaction Using C4.5 And Naïve Bayes Algorithm,” J. PILAR Nusa Mandiri, vol. 17, no. 2, 2021, doi: https://doi.org/10.33480/pilar.v17i2.2403.
P. W. Rahayu et al., Buku Ajar Data Mining. Jambi: PT Sonpedia Publishing Indonesia, 2024. [Online]. Available: www.buku.sonpedia.com
A. A. Purnama and Y. R. Sipayung, “Sentiment Analysis of Public Service Using Naïve Bayes Classifier,” J. Inf. Syst. Informatics, vol. 7, no. 3, pp. 2439–2457, Sep. 2025, doi: 10.51519/journalisi.v7i3.1207.
Suparyanto, “Klasifikasi Kepuasan Layanan Akademik Di STMIK El Rahma Menggunakan Metode Algoritma Naive Bayes,” J. Inform. Komputer, Bisnis dan Manaj., vol. 20, no. 2, p. 100, 2022, doi: https://doi.org/10.61805/fahma.v20i2.37.
W. D. Septiani and M. Marlina, “Comparison of Decision Tree, Naïve Bayes, and Neural Network Algorithm for Early Detection of Diabetes,” J. Pilar Nusa Mandiri, vol. 17, no. 1, pp. 73–78, 2021, doi: 10.33480/pilar.v17i1.2213.
D. A. Eryana, Filius Deivivi, M. Ilham Baehaqi, Arya Wicaksono, Zalfa Alykha Puspita, and Herwis Gultom, “STUDI KOMPARATIF NAIVE BAYES DAN DECISION TREE PADA DATASET BUNGA IRIS: EVALUASI AKURASI DAN EFISIENSI,” J. Chem. Inf. Model., vol. 53, p. 160, 1989, doi: https://doi.org/10.56486/jris.vol5no2.898.
D. Fatmawati, W. Trisnawati, Y. Jumaryadi, and G. Triyono, “Kajian Ilmiah Informatika dan Komputer Klasifikasi Tingkat Kepuasan Penggunaan Layanan Teknologi Informasi Menggunakan Decision Tree,” KLIK Kaji. Ilm. Inform. dan Komput., vol. 3, no. 6, pp. 1056–1062, 2023, doi: 10.30865/klik.v3i6.803.
A. Natuzzuhriyyah, N. Nafisah, and R. Mayasari, “Klasifikasi Tingkat Kepuasan Mahasiswa Terhadap Pembelajaran Secara Daring Menggunakan Algoritma Naïve Bayes,” J. Inform. Sunan Kalijaga, vol. 6, no. 3, pp. 161–170, 2021, doi: https://doi.org/10.14421/jiska.2021.6.3.161-170.
O. Tulak Bamba, Nur Vadila, Sri Fitrawati, V. W. Tedang, and Asrawati, “Naive Bayes dan Decision Tree: Studi Kasus Klasifikasi Kepuasan Pelanggan E-Commerce,” SIMKOM, vol. 10, no. 2, pp. 254–262, Jul. 2025, doi: 10.51717/simkom.v10i2.897.
D. Amalia Lestari, Sugiyono, Y. Akbar, and A. Z. Hidayat, “Klasifikasi Tingkat Kepuasan Masyarakat terhadap Pelayanan Pembuatan KTP Elektronik di Dinas Dukcapil Semper Barat Menggunakan Metode Naïve Bayes,” Jakarta, 2025. doi: https://doi.org/10.63447/jimik.v6i3.1598.
Y. A. Singgalen, “Penerapan Metode CRISP-DM dalam Klasifikasi Data Ulasan Pengunjung Destinasi Danau Toba Menggunakan Algoritma Naïve Bayes Classifier (NBC) dan Decision Tree (DT),” J. MEDIA Inform. BUDIDARMA, vol. 7, no. 3, p. 1551, Jul. 2023, doi: 10.30865/mib.v7i3.6461.
M. Abdurohman, R. Husna, I. Ali, G. Dwilestari, and N. Rahaningsih, “Penerapan Model Klasifikasi Dalam Tingkat Kepuasan Layanan Publik Kelurahan Karyamulya Menggunakan Algoritma Decision Tree,” Inf. Manag. Educ. Prof., vol. 6, no. 1, pp. 81–90, 2021, doi: https://doi.org/10.51211/imbi.v6i1.1678.
Tasya Nadina Firyal, Hamdani Hamdani, and Anindita Septiarini, “Penerapan Metode Naïve Bayes Untuk Klasifikasi Kepuasan Masyarakat Terhadap Kualitas Pelayanan,” J. Rekayasa Teknol. Inf., vol. Vol.8 No.2, Dec. 2024, doi: http://dx.doi.org/10.30872/jurti.v8i2.
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