Pengembangan Layanan Kependudukan Dan Pencatatan Sipil Menggunakan Algoritma Naïve Bayes
DOI:
https://doi.org/10.30865/jurikom.v9i4.4515Keywords:
Data Mining, Naïve Bayes, NBC, Disdukcapil Alor, Mobile ServiceAbstract
The “pick-up ball service†program or commonly called mobile services is a form of developing services for the Population and Civil Registration Service (disdukcapil), especially Alor NTT Regency, which aims to reach people who have difficulty getting services due to various obstacles, including due to long distances and difficult access to service centers. Disdukcapil is an implementing agency in the district that is obliged to ensure that all residents, both Indonesians and foreigners, are recorded in the population database, have a NIK, and population documents. In its implementation, not all villages can be served due to insufficient time and funds, so it is necessary to select villages that are worthy of being served by “ball pick up service†or mobile service programs. So far, the determination of villages that are worthy of the program is still determined manually so that it is inseparable from the element of subjectivity. In this study, a classification method was used in data mining using the naïve bayes algorithm. Naïve bayes is one of the algorithms that is able to predict well by calculating the probability of each class and comparing them, Naïve bayes is used to calculate probability values from training data so that it can predict which villages are feasible for the program. The study used 25 training data taken from previous dukcapil service data and data on the distance and difficulty of access to the village, which were tested and simulated using the naïve bayes algorithm of weka software. The purpose of this study was to obtain accurate information about the villages in Alor district that deserve the program. The results of the process using WEKA software found that from 25 tuples used as test data, it resulted in 100% accuracy .
References
Dirjen Dukcapil, “Visualisasi Data Kependudukan Kementerian Dalam Negeri - Dukcapil,†Dirjen Kependudukan dan Pencatatan Sipil, 2022. https://gis.dukcapil.kemendagri.go.id/peta/ (accessed Jun. 24, 2022).
Perpres, “Peraturan Presiden Republik Indonesia No. 63 Tahun 2020 tentang Penetapan Daerah Tertinggal Tahun 2020-2024,†no. 018390. Jakarta, pp. 1–8, 2020. [Online]. Available: https://jdih.setkab.go.id/PUUdoc/176108/Perpres_Nomor_63_Tahun_2020.pdf
Undang-Undang Republik Indonesia, “Undang Undang Republik Indonesia No. 23 Tahun 2006.†Jakarta, 2006.
M. Rasyida, “Naïve Bayes Classification untuk Penentuan Status Penduduk Miskin,†J. Inform. Kaputama(JIK), vol. 4, no. 2, pp. 175–180, 2020.
D. Ayuningsih and N. A. Hasibuan, “Sistem Pakar Mendiagnosa Kerusakan Pada Mesin Penggilingan Padi Menggunakan Metode Naive Bayes,†J. JURIKOM (Jurnal Ris. Komputer), vol. 5, no. 4, pp. 371–376, 2018.
A. Yudhana, I. Riadi, and F. Ridho, “DDoS Classification Using Neural Network and Naïve Bayes Methods For Network Forensics,†Int. J. Adv. Comput. Sci. Appl., vol. 9, no. 11, pp. 177–183, 2018, doi: 10.14569/IJACSA.2018.091125.
Y. Yuliyana and A. S. R. M. Sinaga, “Sistem Pakar Diagnosa Penyakit Gigi Menggunakan Metode Naive Bayes,†Fountain Informatics J., vol. 4, no. 1, p. 19, 2019, doi: 10.21111/fij.v4i1.3019.
T. Amijoyo, R. Umar, and A. Yudhana, “Bruteforce In The Hydra Process And Telnet Service Using The Naïve Bayes Method,†vol. 4, no. 36, pp. 319–326, 2020, [Online]. Available: https://iocscience.org/ejournal/index.php/mantik/index
H. Wahono and D. Riana, “Prediksi Calon Pendonor Darah Potensial Dengan Algoritma Naïve Bayes, K-Nearest Neighbors dan Decision Tree C4.5,†JURIKOM (Jurnal Ris. Komputer), vol. 7, no. 1, p. 7, 2020, doi: 10.30865/jurikom.v7i1.1953.
S. Saputra, A. Yudhana, and R. Umar, “Implementation of Naïve Bayes for Fish Freshness Identification Based on Image Processing,†vol. 5, no. 158, pp. 4–5, 2022.
M. F. Rifai, H. Jatnika, and B. Valentino, “Penerapan Algoritma Naïve Bayes Pada Sistem Prediksi Tingkat Kelulusan Peserta Sertifikasi Microsoft Office Specialist ( MOS ),†vol. 12, no. 2, pp. 131–144, 2019.
M. S. Mustafa, M. R. Ramadhan, and A. P. Thenata, “Implementasi Data Mining untuk Evaluasi Kinerja Akademik Mahasiswa Menggunakan Algoritma Naive Bayes Classifier,†Creat. Inf. Technol. J., vol. 4, no. 2, p. 151, 2018, doi: 10.24076/citec.2017v4i2.106.
S. Suprianto, “Implementasi Algoritma Naive Bayes Untuk Menentukan Lokasi Strategis Dalam Membuka Usaha Menengah Ke Bawah di Kota Medan (Studi Kasus: Disperindag Kota Medan),†J. Sist. Komput. dan Inform., vol. 1, no. 2, pp. 125–130, 2020, doi: 10.30865/json.v1i2.1939.
mohamad jajuli nurul rohmawati, sofi defiyanti, “Implementasi Algoritma K-Means Dalam Pengklasteran Mahasiswa Pelamar Beasiswa,†Jitter 2015, vol. I, no. 2, pp. 62–68, 2015.
K. S. H. K. Al Atros, A. R. Padri, O. Nurdiawan, A. Faqih, and S. Anwar, “Model Klasifikasi Analisis Kepuasan Pengguna Perpustakaan Online Menggunakan K-Means dan Decission Tree,†JURIKOM (Jurnal Ris. Komputer), vol. 8, no. 6, pp. 323–329, 2022, doi: 10.30865/jurikom.v8i6.3680.
I. Riadi, R. Umar, and F. D. Aini, “Analisis Perbandingan Detection Traffic Anomaly Dengan Metode Naive Bayes Dan Support Vector Machine (Svm),†Ilk. J. Ilm., vol. 11, no. 1, pp. 17–24, 2019, doi: 10.33096/ilkom.v11i1.361.17-24.
F. Sodik and I. Kharisudin, “Analisis Sentimen dengan SVM , NAIVE BAYES dan KNN untuk Studi Tanggapan Masyarakat Indonesia Terhadap Pandemi Covid-19 pada Media Sosial Twitter,†Prisma, vol. 4, pp. 628–634, 2021.
R. Umar, I. Riadi, and Purwono, “Perbandingan Metode SVM, RF dan SGD untuk Penentuan Model Klasifikasi Kinerja Programmer pada Aktivitas Media Sosial,†J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 4, no. 2, pp. 329–335, 2020.
R. Umar, I. Riadi, and D. A. Faroek, “Classification Based on Machine Learning Methods for Identification of Image Matching Achievements,†J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 6, pp. 198–206, 2022.
R. Umar, I. Riadi, and D. A. Faroek, “A Komparasi Image Matching Menggunakan Metode K-Nearest Neightbor (KNN) dan Support Vector Machine (SVM),†J. Appl. Informatics Comput., vol. 4, no. 2, pp. 124–131, 2020, doi: 10.30871/jaic.v4i2.2226.
A. Nakra and M. Duhan, “Comparative Analysis of Bayes Net Classifier, Naive Bayes Classifier and Combination of both Classifiers using WEKA,†Int. J. Inf. Technol. Comput. Sci., vol. 11, no. 3, pp. 38–45, 2019, doi: 10.5815/ijitcs.2019.03.04.
A. S. Musliman, A. Fadlil, and A. Yudhana, “Identification of White Blood Cells Using Machine Learning Classification Based on Feature Extraction,†JOIN (Jurnal Online Inform., vol. 6, no. 1, p. 63, 2021, doi: 10.15575/join.v6i1.704.
A. Yudhana, D. Sulistyo, and I. Mufandi, “GIS-based and Naïve Bayes for nitrogen soil mapping in Lendah, Indonesia,†Sens. Bio-Sensing Res., vol. 33, p. 100435, 2021, doi: 10.1016/j.sbsr.2021.100435.
Alvina Felicia Watratan, Arwini Puspita. B, and Dikwan Moeis, “Implementasi Algoritma Naive Bayes Untuk Memprediksi Tingkat Penyebaran Covid-19 Di Indonesia,†J. Appl. Comput. Sci. Technol., vol. 1, no. 1, pp. 7–14, 2020, doi: 10.52158/jacost.v1i1.9.
Y. Apryani et al., “Implementasi Sistem Pakar dengan Algoritma Naive Bayes dengan Laplace Correction untuk Diagnosis Tuberkulosis Paru,†Inf. (Jurnal Inform. dan Sist. Informasi), vol. 13, no. 1, pp. 61–79, 2021.
M. Rizki et al., “Perbaikan Algoritma Naive Bayes Classifier Menggunakan Teknik Laplacian Correction,†J. Teknol., vol. 21, no. 1, pp. 39–45, 2021.
I. G. I. Suardika, “Prediksi Tingkat Kelulusan Mahasiswa Tepat Waktu Menggunakan Naive Bayes: Studi Kasus Fakultas Ekonomi Dan Bisnis Universitas Pendidikan Nasional,†J. Ilmu Komput. Indones., vol. 4, no. 2, pp. 37–44, 2019, doi: 10.23887/jik.v4i2.2775.
