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 become customer-oriented. Looking for information about products 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. In this research, we try to analyze customer data who buy Starbucks to find out which customers are very loyal to buy Strabucks products using the Naive Bayes algorithm. This algorithm groups loyal and non-loyal customer data, then separates it into test data and training data. From this data Testing will be carried out using the Naive Bayes algorithm to determine loyal customers. The results this research have an accuracy value of 87%, a precision value of 90% and a recall of 95%, which means this classification has good performance
References
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[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.
[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. M. M. Siregar, “Sistem Pendukung Keputusan Penentuan Insentif Bulanan Pegawai Dengan Menggunakan Metode Naïve Bayes,” SISTEMASI, vol. 7, no. 2, pp. 87–94, 2018.
[10] V. M. M. Siregar, “Perancangan Sistem Pendukung Keputusan Penentuan Jurusan Siswa/i SMA Swasta Binaguna Tanah Jawa Dengan Metode Naive Bayes,” in Prosiding SenNasMUDI 2017 ISBN 978-602-50396-1-4, 2017, pp. 66–75.
[11] 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.
[12] 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.
[13] 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.
[14] 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.
[15] 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.
[16] 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.
[17] 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.
[18] W. Purba, S. Tamba, and J. Saragih, “The effect of mining data k-means clustering toward students profile model drop out potential,” J. Phys. Conf. Ser., vol. 1007, no. 1, p. 12049, 2018, [Online]. Available: http://stacks.iop.org/1742-6596/1007/i=1/a=012049
[19] S. P. Tamba, A. W. Tan, Y. Gunawan, and ..., “Penerapan Data Mining Untuk Pembuatan Paket Promosi Penjualan Menggunakan Kombinasi Fp-Tree Dan Tid-List,” … (Teknik Inf. dan …, vol. 4, 2021.
[20] S. Sonang, A. T. Purba, and S. Sirait, “PREDIKSI PRESTASI MAHASISWA DENGAN MENGGUNAKAN ALGORITMA BACKPROPAGATION,” J. Tek. Inf. dan Komput., vol. 5, no. 1, p. 67, Jun. 2022, doi: 10.37600/tekinkom.v5i1.512.
[21] S. Sonang, S. Defit, and M. Ramadhan, “Analisis Optimasi Fungsi Pelatihan Machine Learning Neural Network dalam Peramalan Kemiskinan,” J. Edukasi dan Penelit. Inform., vol. 7, no. 3, pp. 359–369, 2021, [Online]. Available: https://www.bps.go.id/.
[22] S. S. Sitanggang, Y. Yuhandri, and Adil Setiawan, “Image Transformation With Lung Image Thresholding and Segmentation Method,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 7, no. 2, pp. 278–285, 2023, doi: 10.29207/resti.v7i2.4321.
[23] 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.
[24] 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.
Published
2024-12-31
How to Cite
HAYUNINGTYAS, Ratih Yulia.
ANALISA KLASIFIKASI LOYALITAS PELANGGAN MENGGUNAKAN ALGORITMA NAÏVE BAYES.
Jurnal Teknik Informasi dan Komputer (Tekinkom), [S.l.], v. 7, n. 2, p. 891-898, dec. 2024.
ISSN 2621-3079.
Available at: <https://jurnal.murnisadar.ac.id/index.php/Tekinkom/article/view/1725>. Date accessed: 15 may 2025.
doi: https://doi.org/10.37600/tekinkom.v7i2.1725.
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