ANALISA KLASIFIKASI LOYALITAS PELANGGAN MENGGUNAKAN ALGORITMA NAÏVE BAYES
Abstract
Customers are one of the important assets in business ventures, every company competes to attract customers by various means of promoting the products they sell. It turns out by focusing on the product it cannot attract customers, therefore the company changed its method to become customer-oriented. Looking for information about products that are in high demand by customers can attract customers to remain loyal to the products sell. To find out whether customers are loyal or not from each visit, you can use a classification algorithm, namely Naïve Bayes. The Naïve Bayes algorithm is one of the best algorithms for classification because it can help find hidden data models during data analysis. This research tries to analyze the dataset of customers who buy Starbucks, to find out which customers are loyal to purchasing their products. The results of this research have an accuracy value of 87%, a precision value of 90% and a recall of 95%, which means that this classification has good performance.
References
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[16] I. M. A. A. Pramana, I. W. Sudiarsa, and P. G. S. C. Nugraha, “Penerapan Algoritma Naive Bayes Untuk Prediksi Penjualan Produk Terlaris Pada CV Akusara Jaya Abadi,” J. Tek. Inform. dan Sist. Inforamsi, vol. 10, no. 4, pp. 518–534, 2023, [Online]. Available: https://jurnal.mdp.ac.id/index.php/jatisi/article/view/6498%0Ahttps://jurnal.mdp.ac.id/index.php/jatisi/article/download/6498/1694
[17] Y. Septiani and P. F. Ariyani, “Penerapan Algoritma Naive Bayes Menentukan Klasifikasi Tingkat Kelulusan Siswa SMK Media Informatika Jakarta,” Semin. Nas. Mhs. Fak. Teknol. Inf., no. September, pp. 607–613, 2022, [Online]. Available: http://senafti.budiluhur.ac.id/index.php/senafti/article/view/361%0Ahttp://senafti.budiluhur.ac.id/index.php/senafti/article/download/361/71
[2] N. Wayan Wardani et al., “Prediksi Pelanggan Loyal Menggunakan Metode Naïve Bayes Berdasarkan Segmentasi Pelanggan dengan Pemodelan RFM,” J. Manaj. Dan Teknol. Inf., vol. 12, no. 2, pp. 2087–5312, 2022, [Online]. Available: https://ojs.mahadewa.ac.id/index.php/jmti
[3] J. Lemantara, “Penerapan Algoritma Naïve Bayes dan ID3 untuk Memprediksi Segmentasi Pelanggan pada Penjualan Mobil,” J. Technol. Informatics, vol. 4, no. 1, pp. 31–40, 2022, doi: 10.37802/joti.v4i1.265.
[4] R. Alfarez, Rianto, and V. Purwayoga, “Penerapan Naïve Bayes untuk Prediksi Customer Churn (Studi Kasus: PT Hutchison 3 Indonesia),” J. Ris. dan Apl. Mhs. Inform., vol. 5, no. 2, pp. 301–307, 2024, doi: 10.30998/jrami.v5i2.8556.
[5] K. Alpan and G. S. Ilgi, “Classification of Diabetes Dataset with Data Mining Techniques by Using WEKA Approach,” 4th Int. Symp. Multidiscip. Stud. Innov. Technol. ISMSIT 2020 - Proc., 2020, doi: 10.1109/ISMSIT50672.2020.9254720.
[6] P. S. Zakaria, R. Julianto, and R. S. Bernada, “Implementasi Naive Bayes Menggunakan Python dalam Klasifikasi Data,” BIIKMA Bul. Ilm. Ilmu Komput. dan Multimed., vol. 1, no. 2, pp. 126–131, 2023.
[7] H. Kamel, D. Abdulah, and J. M. Al-Tuwaijari, “Cancer Classification Using Gaussian Naive Bayes Algorithm,” Proc. 5th Int. Eng. Conf. IEC 2019, pp. 165–170, 2019, doi: 10.1109/IEC47844.2019.8950650.
[8] D. Fahrudy et al., “Intelligent System for Classification of Student Personality With Naive Bayes Algorithm,” SINTECH (Science Inf. Technol. J., vol. 5, no. 1, pp. 1–9, 2022, doi: 10.31598/sintechjournal.v5i1.969.
[9] V. Chang, J. Bailey, Q. A. Xu, and Z. Sun, “Pima Indians diabetes mellitus classification based on machine learning (ML) algorithms,” Neural Comput. Appl., vol. 35, no. 22, pp. 16157–16173, 2023, doi: 10.1007/s00521-022-07049-z.
[10] F. M. Okikiola, O. S. Adewale, and O. O. Obe, “A Diabetes Prediction Classifier Model using Naive Bayes Algorithm,” FUDMA J. Sci., vol. 7, no. 1, pp. 253–260, 2023.
[11] A. Pebdika, R. Herdiana, and D. Solihudin, “Klasifikasi Menggunakan Metode Naive Bayes Untuk Menentukan Calon Penerima PIP,” JATI (Jurnal Mhs. Tek. Inform., vol. 7, no. 1, pp. 452–458, 2023, doi: 10.36040/jati.v7i1.6303.
[12] Heliyanti Susana, N. Suarna, Fathurrohman, and Kaslani, “Penerapan Model Klasifikasi Metode Naive Bayes Terhadap Penggunaan Akses Internet,” J. Ris. Sist. Inf. dan Teknol. Inf., vol. 4, no. 1, pp. 1–8, 2022, doi: 10.52005/jursistekni.v4i1.96.
[13] N. Salmi and Z. Rustam, “Naïve Bayes Classifier Models for Predicting the Colon Cancer,” IOP Conf. Ser. Mater. Sci. Eng., vol. 546, no. 5, pp. 1–8, 2019, doi: 10.1088/1757-899X/546/5/052068.
[14] J. P. Tanjung, F. C. Tampubolon, A. W. Panggabean, and M. A. A. Nandrawan, “Customer Classification Using Naive Bayes Classifier With Genetic Algorithm Feature Selection,” Sinkron, vol. 8, no. 1, pp. 584–589, 2023, doi: 10.33395/sinkron.v8i1.12182.
[15] A. F. Haya, R. Azhar, M. K. Mulyadien, and B. N. Sari, “Klasifikasi Minat Beli Pelanggan Terhadap Udang Vaname Menggunakan Algoritma Naïve Bayes,” J. Ilm. Betrik, vol. 13, no. 1, pp. 59–65, 2022, doi: 10.36050/betrik.v13i1.452.
[16] I. M. A. A. Pramana, I. W. Sudiarsa, and P. G. S. C. Nugraha, “Penerapan Algoritma Naive Bayes Untuk Prediksi Penjualan Produk Terlaris Pada CV Akusara Jaya Abadi,” J. Tek. Inform. dan Sist. Inforamsi, vol. 10, no. 4, pp. 518–534, 2023, [Online]. Available: https://jurnal.mdp.ac.id/index.php/jatisi/article/view/6498%0Ahttps://jurnal.mdp.ac.id/index.php/jatisi/article/download/6498/1694
[17] Y. Septiani and P. F. Ariyani, “Penerapan Algoritma Naive Bayes Menentukan Klasifikasi Tingkat Kelulusan Siswa SMK Media Informatika Jakarta,” Semin. Nas. Mhs. Fak. Teknol. Inf., no. September, pp. 607–613, 2022, [Online]. Available: http://senafti.budiluhur.ac.id/index.php/senafti/article/view/361%0Ahttp://senafti.budiluhur.ac.id/index.php/senafti/article/download/361/71
Published
2024-12-31
How to Cite
HAYUNINGTYAS, Ratih Yulia.
ANALISA KLASIFIKASI LOYALITAS PELANGGAN MENGGUNAKAN ALGORITMA NAÏVE BAYES.
Jurnal Tekinkom (Teknik Informasi dan Komputer), [S.l.], v. 7, n. 2, dec. 2024.
ISSN 2621-3079.
Available at: <https://jurnal.murnisadar.ac.id/index.php/Tekinkom/article/view/1725>. Date accessed: 17 jan. 2025.
doi: https://doi.org/10.37600/tekinkom.v7i2.1725.
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