PENERAPAN ALGORITMA NAIVE BAYES BERBASIS FORWARD SELECTION UNTUK MEMPREDIKSI PENJUALAN MOBIL BEKAS
Main Article Content
Abstract
Cars are one of the vehicles that are the daily needs of the people, not only the use of new cars is in demand, but now used cars are also in great demand because the quality of used cars is still good and the many types of used cars are sold in the market. The aim of the researchers is to increase public interest in switching to buying used cars. This study uses data mining methods, one of which is prediction using the Naive Bayes algorithm as an algorithm that uses probabilistic and statistical methods to predict the future, besides that the data is also processed using forward selection feature selection which aims to reduce the level of complexity of a classification algorithm while increasing accuracy. The research data used were 2318 records, in this study an experiment was carried out with the accuracy results obtained using split validation on the naive Bayes algorithm of 96.98% and then another experiment was carried out to obtain accurate results using split validation on the naive bayes algorithm based on forward selection of 97.82 %. Thus the naive Bayes algorithm based on forward selection is suitable for predicting, as well as being used for handling in the future that there are still many used cars that are of interest to the public..
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work’s authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal’s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
[2] . N., N. Septianti, N. Retnowati, and A. Wibowo, “Prediksi Tingkat Kelulusan Tepat Waktu Mahasiswa Menggunakan Algoritma Naïve Bayes pada Universitas XYZ,” Ultim. J. Tek. Inform., vol. 12, no. 2, pp. 104–107, 2020.
[3] A. D. R. Prabowo and M. Muljono, “Prediksi Nasabah Yang Berpotensi Membuka Simpanan Deposito Menggunakan Naive Bayes Berbasis Particle Swarm Optimization,” Techno.Com, vol. 17, no. 2, pp. 208–219, 2018.
[4] H. Harafani and H. A. Al-Kautsar, “Meningkatkan Kinerja K-Nn Untuk Klasifikasi Kanker Payudara Dengan Forward Selection,” J. Pendidik. Teknol. dan Kejuru., vol. 18, no. 1, p. 99, 2021.
[5] E. Purwaningsih, “Improving the Performance of Support Vector Machine With Forward Selection for Prediction of Chronic Kidney Disease,” JITK (Jurnal Ilmu Pengetah. dan Teknol. Komputer), vol. 8, no. 1, pp. 18–24, 2022.
[6] M. F. Nugroho and S. Wibowo, “Fitur Seleksi Forward Selection Untuk Menetukan Atribut Yang Berpengaruh Pada Klasifikasi Kelulusan Mahasiswa Fakultas Ilmu Komputer UNAKI Semarang Menggunakan Algoritma Naive Bayes,” J. Inform. Upgris, vol. 3, no. 1, pp. 63–70, 2017.
[7] R. Reynaldi, W. Syafrizal, and M. F. Al Hakim, “Analisis Perbandingan Akurasi Metode Fuzzy Tsukamoto dan Fuzzy Sugeno Dalam Prediksi Penentuan Harga Mobil Bekas,” Indones. J. Math. Nat. Sci., vol. 44, no. 2, pp. 73–80, 2021.
[8] G. Gushelmi and D. Guswandi, “Sistem Pendukung Keputusan Pemilihan Mobil Bekas Menggunakan Metode Analythical Hierarchy Process,” J. Teknol. Dan Sist. Inf. Bisnis, vol. 3, no. 2, pp. 380–386, 2021.
[9] W. Supriyanti and N. Puspitasari, “Implementasi Teknik Seleksi Fitur Forward Selection Pada Algoritma Klasifikasi Data Mining untuk Prediksi Masa Studi Mahasiswa Politeknik Indonusa Surakarta,” J. Inf. Politek. Indones. Surakarta, vol. 4, no. 2, pp. 49–54, 2018.
[10] F. D. A. Ika Nur Fajri1, “Pengaruh SMOTE Dan Forward Selection Dalam Menangani Ketidakseimbangan Kelas Pada Algoritma Klasifikasi,” J. Inf. Interaktif, vol. 7, no. 1, pp. 45–49, 2022.
[11] H. Annur, “Penerapan Algoritma Naïve Bayes Berbasis Backward Elimination Untuk Prediksi Pemesanan Kamar Hotel,” J. Ilm. Ilmu Komput. Banthayo Lo Komput., vol. 1, no. 1, pp. 1–5, 2022.
[12] N. Nurajijah and D. Riana, “Algoritma Naïve Bayes, Decision Tree, dan SVM untuk Klasifikasi Persetujuan Pembiayaan Nasabah Koperasi Syariah,” J. Teknol. dan Sist. Komput., vol. 7, no. 2, pp. 77–82, 2019.
[13] F. Andraini and E. Mahdiyah, “Analisis Sentimen Twitter Terhadap Peperangan Rusia Dan Ukraina Menggunakan Algoritma Support Vector Machine,” J. Apl. Komput., vol. 2, no. 1, pp. 46–58, 2022.
[14] R. Tuntun, “Analisis Perbandingan Kinerja Algoritma Klasifikasi dengan Menggunakan Metode K-Fold Cross Validation,” vol. 6, pp. 2111–2119, 2022.