Application of the Naïve Bayes Method in Optimizing Marketing Performance at PT. Semen Indonesia

Authors

  • Mahesa Reglisalo departmen of informatics malikussaleh university
  • Dahlan Abdullah
  • Yesy Afrillia

Keywords:

Naïve Bayes, marketing performance, PT. Semen Indonesia, data analysis, classification system, profit, market share

Abstract

This study examines the application of the Naïve Bayes method to improve marketing performance at PT. Semen Indonesia. In an increasingly competitive business environment, effective data management is crucial for strategic decision-making. Currently, PT. Semen Indonesia utilizes the SAP system to manage sales and financial data, but it lacks an automated system to analyze marketing performance. This research aims to develop a Naïve Bayes-based classification system to monitor marketing performance, considering attributes such as profit, market share, sales volume, and customer satisfaction. The Naïve Bayes method was chosen for its accuracy in handling large-scale data and its ability to provide fast and efficient predictions. Marketing performance data is processed using this method to categorize marketing performance as “good” or “poor.” The analysis results show that the developed system achieves a classification accuracy of 43.75% for the “good” category and 56.25% for the “poor” category. This system assists management in designing more effective marketing strategies by leveraging historical data to predict trends and market needs.

Keywords: Naïve Bayes, marketing performance, PT. Semen Indonesia, data analysis, classification system, profit, market share

References

[1] D. Dahri, F. Agus, and D. M. Khairina, “Metode Naive Bayes Untuk Penentuan Penerima Beasiswa Bidikmisi Universitas Mulawarman,” Inform. Mulawarman J. Ilm. Ilmu Komput., vol. 11, no. 2, p. 29, 2016, doi: 10.30872/jim.v11i2.211.

[2] A. Utaminingsih, “Pengaruh orientasi pasar, inovasi, dan kreativitas strategi pemasaran terhadap kinerja pemasaran pada UKM kerajinan rotan di Desa Teluk Wetan, Welahan, Jepara,” Media Ekon. Dan Manaj., vol. 31, no. 2, pp. 77–87, 2016.

[3] et al., “Implementasi Algoritma Naive Bayes Untuk Klasifikasi Penerima Beasiswa (Studi Kasus Universitas Hamzanwadi),” Infotek J. Inform. dan Teknol., vol. 6, no. 1, pp. 177–188, 2023, doi: 10.29408/jit.v6i1.7529.

[4] C. Fadlan, S. Ningsih, and A. P. Windarto, “Penerapan Metode Naïve Bayes Dalam Klasifikasi Kelayakan Keluarga Penerima Beras Rastra,” J. Tek. Inform. Musirawas, vol. 3, no. 1, p. 1, 2018, doi: 10.32767/jutim.v3i1.286.

[5] R. Nurul Arifin, “Implementasi Algoritma Frequent Pattern Growth (FP-GROWTH) Menentukan Asosiasi Antar Produk (Study KAsus Nadia Mart),” Dok. Karya Ilm., pp. 0–1, 2015.

[6] B. N. , T. R. , Syamsul Bahri, “Implementasi Data Mining Dengan Algoritma C4.5 Untuk Penjurusan Siswa (Studi Kasus: Sma Negeri 1 Pontianak),” Coding J. Komput. dan Apl., vol. 4, no. 3, 2016, doi: 10.26418/coding.v4i3.17034.

[7] Styawati, Nurkholis Andi, and Anjumi Krisma Nur, “Analisis Pola Transaksi Pelanggan Menggunakan Algoritme Apriori,” J. Sains Komput. Inform., vol. 5, no. 2, pp. 619–626, 2021.

[8] C. Darujati et al., “Pemanfaatan Teknik Supervised Untuk,” Text, vol. 16, no. 1, pp. 1–8, 2012.

[9] M. Furqan, M. K. Sastia H Wibowo, S. Kom, and E. Dedy Abdullah, ST. M, “Sistem Persediaan Obat Pada Puskesmas Menggunakan Metode Naive Bayes (Studi Kasus Puskesmas Talang Tinggi Seluma) Skripsi. Universits Muhammadiyah Bengkulu,” Univ. Muhamadiyah Bengkulu, vol. 18, no. 60, pp. 1–8, 2016.

[10] E. Darnila, M. Maryana, and M. Azmi, “Aplikasi Klasifikasi Status Gizi Balita Menggunakan Metode Naïve Bayes Berbasis Android,” METHOMIKA J. Manaj. Inform. dan Komputerisasi Akunt., vol. 5, no. 2, pp. 135–141, 2021, doi: 10.46880/jmika.vol5no2.pp135-141.

[11] A. Lutfi and Firmansyah, “Peningkatan Kinerja Pemasaran Umkm Berdasarkan Orientasi Kewirausahaan, Pengetahuan Pemasaran Dan Keadaan Lingkungan,” J. Bisnis, Manajemen, Dan Inform., vol. 18, no. II, pp. 86–102, 2021, doi: 10.26487/jbmi.v18i2.13977.

[12] M. Ridho Handoko, “Sistem Pakar Diagnosa Penyakit Selama Kehamilan Menggunakan Metode Naive Bayes Berbasis Web,” J. Teknol. dan Sist. Inf., vol. 2, no. 1, pp. 50–58, 2021, [Online]. Available: http://jim.teknokrat.ac.id/index.php/JTSI

[13] H. D. Wijaya and S. Dwiasnati, “Implementasi Data Mining dengan Algoritma Naïve Bayes pada Penjualan Obat,” J. Inform., vol. 7, no. 1, pp. 1–7, 2020, doi: 10.31311/ji.v7i1.6203.

Downloads

Published

2024-12-27

Issue

Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)

Section

Articles

License

Copyright (c) 2024 mahesa reglisalo, Dahlan Abdullah, Yesy Afrillia

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Copyright Notice

Authors published in this journal agree to the following terms:
1. The copyright of each article is retained by the author (s).
2. The author grants the journal the first publication rights with the work simultaneously licensed under the Creative Commons Attribution License, allowing others to share the work with an acknowledgment of authorship and the initial publication in this journal.
3. Authors may enter into separate additional contractual agreements for the non-exclusive distribution of published journal versions of the work (for example, posting them to institutional repositories or publishing them in a book), with acknowledgment of their initial publication in this journal.
4. Authors are permitted and encouraged to post their work online (For example in the Institutional Repository or on their website) before and during the submission process, as this can lead to productive exchanges, as well as earlier and larger citations of published work.
5. Articles and all related material published are distributed under a Creative Commons Attribution-ShareAlike 4.0 International License.