CLUSTERING DATA PENJUALAN PRODUK KOPI MENGGUNAKAN K-MEANS UNTUK PERUMUSAN STRATEGI HARGA DAN PROMOSI

Deswita Hendiyani; Agus Bahtiar

Authors

Deswita Hendiyani STMIK IKMI Cirebon, Indonesia
Agus Bahtiar STMIK IKMI Cirebon, Indonesia

Keywords:

K-Means, Segmentasi Produk, Strategi Harga, Promo

Abstract

This study aims to cluster coffee product sales data based on profit margin and sales volume to formulate more effective pricing and promotion strategies. The method employed is the Knowledge Discovery in Databases (KDD) framework combined with the K-Means Clustering algorithm. The research stages include data selection and cleaning, transformation and normalization, determination of the optimal number of clusters, algorithm implementation, and result interpretation leading to strategic recommendations. The findings indicate that K-Means models with K=2, K=3, and K=6 produce well-separated and compact clusters, as reflected by low Davies-Bouldin Index values. The analysis identifies product segments with distinct characteristics, providing a basis for accurately targeted pricing and promotion strategies. These findings contribute practical insights for coffee business operators to enhance data-driven marketing effectiveness and serve as a reference for future research in sales analysis and data mining in the coffee sector.

References

M. Yunianto, F. Anwar, D. Nur Septianingsih, T. Dwi Ardyanto, and R. Farits Pradana, “KLASIFIKASI KANKER PARU PARU MENGGUNAKAN NAÏVE BAYES DENGAN VARIASI FILTER DAN EKSTRAKSI CIRI GRAY LEVEL CO-OCCURANCE MATRIX (GLCM),” Indonesian Journal of Applied Physics, vol. 11, no. 2, 2021.

L. Listyalina, “PENINGKATAN KUALITAS CITRA FOTO RONTGEN SEBAGAI MEDIA DETEKSI KANKER PARU,” Jurnal Teknologi Informasi, 2017.

N. Elsayed, Z. Saad Zaghloul, and M. Bayoumi, “Brain Computer Interface: EEG Signal Preprocessing Issues and Solutions,” 2017.

D. Candra and R. Novitasari, “Klasifikasi Sinyal EEG Menggunakan Metode Fuzzy C-Means Clustering (FCM) Dan Adaptive Neighborhood Modified Backpropagation (ANMBP),” Jurnal Online Fakultas Sains dan Teknologi, 2022, [Online]. Available: http://sccn.ucsd.edu/~arno/fam2data/publicly_

R. Widadi, B. Arif Widodo, and D. Zulherman, “Klasifikasi Sinyal EEG pada Sistem BCI Pergerakan Jari Manusia Menggunakan Convolutional Neural Network,” Jurnal Techno.com, vol. 19, no. 4, pp. 459–467, 2020.

T. M. M. Keumala, M. Melinda, and S. Syahrial, “Decision tree method to classify the electroencephalography-based emotion data,” JURNAL INFOTEL, vol. 14, no. 1, pp. 37–49, Feb. 2022, doi: 10.20895/infotel.v14i1.750.

H. B. Kim, Y. S. Park, J. E. Lee, K. Do Han, and Y. H. Park, “Study on relationship between self-recognition of voice disorder and mental health status: Korea National Health and Nutrition Examination Survey,” J Affect Disord, vol. 338, pp. 482–486, Oct. 2023, doi: 10.1016/j.jad.2023.05.082.

U. R. Acharya, S. L. Oh, Y. Hagiwara, J. H. Tan, and H. Adeli, “Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals,” Comput Biol Med, vol. 100, pp. 270–278, Sep. 2018, doi: 10.1016/j.compbiomed.2017.09.017.

A. Novianto and M. D. Anasanti, “Autism Spectrum Disorder (ASD) Identification Using Feature-Based Machine Learning Classification Model,” IJCCS (Indonesian Journal of Computing and Cybernetics Systems), vol. 17, no. 3, p. 259, Jul. 2023, doi: 10.22146/ijccs.83585.

M. Subramanian, J. Cho, V. E. Sathishkumar, and O. S. Naren, “Multiple Types of Cancer Classification Using CT/MRI Images Based on Learning Without Forgetting Powered Deep Learning Models,” vol. 11, pp. 10336–10354, 2023, doi: 10.1109/ACCESS.2023.3240443.

J. Yi, X. Liu, S. Cheng, L. Chen, and S. Zeng, “Multi-scale window transformer for cervical cytopathology image recognition,” vol. 24, pp. 314–321, 2024, doi: 10.1016/j.csbj.2024.04.028.

S. M. Abd-Alhalem, H. S. Marie, W. El-Shafai, T. Altameem, R. S. Rathore, and T. M. Hassan, “Cervical cancer classification based on a bilinear convolutional neural network approach and random projection,” vol. 127, 2024, doi: 10.1016/j.engappai.2023.107261.

P. Malathi and G. Charlyn Pushpa Latha, “Classification of Multi-view Digital Mammogram Images Using SMO-WkNN,” vol. 46, no. 2, pp. 1741–1758, 2023, doi: 10.32604/csse.2023.035185.

M. Salehi, A. Vafaei Sadr, S. R. Mahdavi, H. Arabi, I. Shiri, and R. Reiazi, “Deep Learning-based Non-rigid Image Registration for High-dose Rate Brachytherapy in Inter-fraction Cervical Cancer,” vol. 36, no. 2, pp. 574–587, 2023, doi: 10.1007/s10278-022-00732-6.