Demographic Attribute Selection Model For Prediction Of Election Participation Using Decision Tree
Article Sidebar
Main Article Content
Abstract
Implementing a democratic general election is expected to produce people's representatives who can channel the people's aspirations. Demographic data is information that discusses a group of people with several related attributes and involves many factors. In this study, we will relate the relationship between the implementation of elections and the condition of demographic data with a benchmark for the form of public participation in the election. By utilizing 2019 election data and Bangkalan Regency demographic data from the Central Statistics Agency (BPS), it is expected to determine the relationship between the two conditions of the dataset on the form of public participation at the polling station (TPS) level. By starting with the Preprocessing step, it implement a classification method with the Decision Tree (DT) algorithm to predict community presence at the polling station level. There are three versions of the dataset that will be used in modeling, namely initial data that has not been selected for attributes (version 1), data that has been chosen using correlation-based attribute selection (version 2), and data that has been selected using chi-square attributes ( version 3). The results show version 1 with a prediction of 81%, followed by version 2 with a prediction of 81%, and the last is version 3 with a prediction of 70%. The detachment model's formation with the selection attribute has a different impact, and the relationship between the election dataset and demographics has a significant effect, as indicated by the prediction results of version 2.
Downloads
Article Details
[2] A. Chowiyah, “Penerapan Data Mining Mengguankan Metode Klasifikasi Naive Bayes untuk Memprediksi Partisipasi pemilihan Gubernur dan Wakil Gubernur di Desa Jemirahan Kecamatan Jabon,” Univ. Muhammadiyah Sidoarjo, 2019.
[3] A. Hakim, “Prediksi Kehadiran Masyarakat Dalam Pemilihan Umun Dengan Menggunakan Metode Naïve Bayes,” Pros. SeNTIK, vol. 3, no. 1, 2019.
[4] I. Sutoyo, “Implementasi Algoritma Decision Tree Untuk Klasifikasi Data Peserta Didik,” J. Pilar Nusa Mandiri, vol. 14, no. 2, pp. 217–224, 2018.
[5] D. Nurrahman, “Algoritma klasifikasi c4. 5 berbasis particle swarm optimization untuk prediksi hasil pemilihan legislatif dprd karawang,” II, pp. 28–37, 2017.
[6] N. P. Volkova, N. O. Rizun, and M. V Nehrey, “Data science: opportunities to transform education,” 2019.
[7] A. K. Fauziyyah, “Analisis sentimen pandemi Covid19 pada streaming Twitter dengan text mining Python,” J. Ilm. SINUS, vol. 18, no. 2, pp. 31–42, 2020.
[8] S. García, J. Luengo, and F. Herrera, Data preprocessing in data mining, vol. 72. Springer, 2015.
[9] T. T. Hanifa, S. Al-Faraby, F. Informatika, and U. Telkom, “Analisis Churn Prediction pada Data Pelanggan PT. Telekomunikasi dengan Logistic Regression dan Underbagging,” vol, vol. 4, pp. 3210–3225, 2017.
[10] H. Muhamad, C. A. Prasojo, N. A. Sugianto, L. Surtiningsih, and I. Cholissodin, “Optimasi Naïve Bayes Classifier Dengan Menggunakan Particle Swarm Optimization Pada Data Iris,” J. Teknol. Inf. dan Ilmu Komput. (JTIIK), p-ISSN, pp. 2355–7699, 2017.
[11] N. Nurmahaludin, “ANALISIS PERBANDINGAN METODE JARINGAN SYARAF TIRUAN DAN REGRESI LINEAR BERGANDA PADA PRAKIRAAN CUACA,” J. INTEKNA Inf. Tek. dan Niaga, vol. 14, no. 2, 2014.
[12] S. D. Jadhav and H. P. Channe, “Comparative study of K-NN, naive Bayes and decision tree classification techniques,” Int. J. Sci. Res., vol. 5, no. 1, pp. 1842–1845, 2016.
[13] D. Okta?a and D. L. Pardede, “Perbandingan Kinerja Algoritma Decision Tree dan Naïve Bayes dalam Prediksi Kebangkrutan,” J. Ilm. Ilmu Komput. Progr. Stud. Sist. Inf., 2010.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.