KLASIFIKASI DATA PELANGGAN POTENSIAL PUT IN SERVICE INDIHOME MENGGUNAKAN ALGORITMA NAÏVE BAYES
Abstract
In the digital era like now, the invitation to get a job is very tight, companies are also increasingly demanding prospective employees to have work experience. This experience allows them to know and understand the atmosphere and apply the knowledge gained during college to the world of work. Knowing and learning how to classify customer data, an opportunity for students to gain experience. Data is an observation result in the form of characteristics of the representation that represents an object. While the understanding of information is the result of structured input processing [1]. Classification is an information mining functionality that wants to create a model to predict part of the objects in the information line [2]. Naïve Bayes algorithm is a grouping of data that is needed when predicting the possibility of a class. The customer classification of test data and training data required for grouping is 25 test data and 75 training data, where training information is then processed using the nave Bayes procedure [6]. After the grouping process using the nave Bayes procedure ended, after that the test information experiment amounted to 25 customer information [7]. Suggestions for further research, the method used needs to be redeveloped using other data mining methods, and it is better if the data used needs to be integrated with the database in order to avoid duplication or damage to data and data security is better maintained.
Keywords
References
Regulation of Transcription Elongation in Response to
Osmostress, https://www.yeastgenome.org/reference/S000207095
Kusnawi., (2007) Pengantar Solusi Data Mining
Ninki Hermaduanti., (2008) Bidang Kesehatan Sistem
Pendukung Keputusan Berbasis SMS untuk Menentukan Status Gizi dengan Metode K-Nearest Neighbor
Muhamad et al., (2013) Implementasi Algoritma Naïve
Bayes Berbasis Particle Swarm Optimization Untuk Memprediksi Penyakit Hepatitis
Harisnawan, E., (2016) Laporan Kegiatan Magang PT
Telekomunikasi Indonesia Witel Yogyakarta (Doctoral
dissertation, STIE YKPN)
Putro, H. F., Vulandari, R. T., & Saptomo, W. L. Y.
(2020). Penerapan Metode Naive Bayes Untuk Klasifikasi
Pelanggan. Jurnal TIKOMSIN (Teknologi Informasi dan
Komunikasi Sinar Nusantara), 8(2).
Manalu, E., Sianturi, F. A., & Manalu, M. R. (2017).
Penerapan Algoritma Naïve Bayes Untuk Memprediksi
Jumlah Produksi Barang Berdasarkan Data Persediaan Dan
Jumlah Pemesanan Pada Cv. Papadan Mama Pastries. Jurnal Mantik Penusa, 1(2).
Webb, G. I., Keogh, E., & Miikkulainen, R. (2010). Naïve
Bayes. Encyclopedia of machine learning, 15, 713-714.
DOI: https://doi.org/10.30591/polektro.v12i2.4127
Refbacks
- There are currently no refbacks.

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
----------------------------------------------------------------------------------------------------------------------
Indexed By :
![]() | ![]() | ![]() | ![]() |
![]() | | ![]() | ![]() |
----------------------------------------------------------------------------------------------------------------------
Tim Redaksi POWER ELEKTRONIK : JURNAL ORANG ELEKTRO
Program Studi D3 Teknik Elektro
Politeknik Harapan Bersama Tegal
Jl. Mataram No.09 Pesurungan Lor Kota Tegal
Telp. (0283) 350567
Email :
powerelektronik.ejournal@poltektegal.ac.id
elektropower41@gmail.com












