Comparison of K-Means & K-Means++ Clustering Models using Singular Value Decomposition (SVD) in Menu Engineering

Nina Setiyawati - UKSW, Salatiga, 50711, Indonesia
Dwi Bangkalang - UKSW, Salatiga, 50711, Indonesia
Hindriyanto Purnomo - UKSW, Salatiga, 50711, Indonesia

Citation Format:



The menu is one of the most fundamental aspects of business continuity in the culinary industry. One of the tools that can be used for menu analysis is menu engineering. Menu engineering is an analytical tool that assists restaurants, companies, and small and medium-sized enterprises (SMEs) in assessing and making decisions on marketing strategies, menu design, and sales so that it can produce maximum profit. In this study, several menu engineering models were proposed, and the performance of these models was analyzed. This study used a dataset from the Point of Sales (POS) application in an SME engaged in the culinary field. This research consists of three stages. First, pre-processing the data, comparing the models, and evaluating the models using the Davies Bouldin index. At the model comparison stage, four models are being compared: K-Means, K-Means++, K-Means using Singular Value Decomposition (SVD), and K-Means++ using SVD. SVD is used in the dataset transformation process. K-Means and K-Means++ algorithms are used for grouping menu items. The experiments show that the K-Means++ model with SVD produced the most optimal cluster in this research. The model produced an average cluster distance value of 0.002; the smallest Davies-Bouldin Index (DBI) value is 0.141. Therefore, using the K-Means++ model with SVD in menu engineering analysis produces clusters containing menu items with high similarity and significant distance between groups. The results obtained from the proposed model can be used as a basis for strategic decision-making of managing price, marketing strategy, etc., for SMEs, especially in the culinary business.


Menu engineering; K-Means; K-Means++; Singular Value Decomposition Davies-Bouldin Index; SME; Cluster

Full Text:



N. Setiyawati, “A Proposed Classification Method in Menu Engineering Using the K-Nearest Neighbors Algorithm,” vol. 11, no. 4, 2021.

H. B. J. Lai, M. R. Z. Abidin, M. Z. Hasni, M. S. A. Karim, and F. A. C. Ishak, “Key Activities of Menu Management and Analysis Performed by SME Restaurants in Malaysia,” Int. J. Acad. Res. Bus. Soc. Sci., vol. 11, no. 4, p. 3, 2021, doi: 10.6007/IJARBSS/v11-i4/9184.

J. M. Antun and C. M. Gustafson, “Menu analysis: Design, merchandising, and pricing strategies used by successful restaurants and private clubs,” J. Nutr. Recipe Menu Dev., vol. 3, no. 3–4, pp. 81–102, 2005, doi: 10.1300/J071v03n03_07.

R. Linassi, A. Alberton, and S. V. Marinho, “Menu engineering and activity-based costing: An improved method of menu planning,” Int. J. Contemp. Hosp. Manag., vol. 28, no. 7, pp. 1417–1440, 2016, doi: 10.1108/IJCHM-09-2014-0438.

B. M. Noone and T. A. Maier, “A decision framework for restaurant revenue management,” J. Revenue Pricing Manag., vol. 14, no. 4, pp. 231–244, 2015, doi: 10.1057/rpm.2015.15.

S. Oktaviani, A. Sudono, and O. Sukirman, “Analisis Menu Engineering Pada Menu A ’ la Carte di Grand Pasundan Convention Hotel Dalam Upaya Meningkatkan Keputusan Pembelian Melalui Suggestive Selling,” vol. 4, 2008.

N. A. S. Kadek, N. K. Bagiastuti, I. A. Elistyawati, and M. Sudiarta, “Menu Engineering on Main Course to Increase Sales,” Int. J. Glocal Tour., vol. 1, no. 1, pp. 51–60, 2020.

H. Atkinson and P. Jones, “Menu Engineering: Managing the Foodsemice Micro-Marketing Mix,” J. Restaur. Foodserv. Mark., vol. 1, no. 1, pp. 37–55, 1993, doi: 10.1300/J061v01n01.

M. L. Kasavana and D. Smith, Menu Engineering: A Practical Guide to Menu Analysis.Lansing. 1982.

G. Rigas, “Menu Profitability Analysis Models: Linking Theory and Practice in the Greek Hospitality Context,” no. December, pp. 1–62, 2018.

D. V. Pavesic, “Cost/margin analysis: a third approach to menu pricing and design,” Int. J. Hosp. Manag., vol. 2, no. 3, pp. 127–134, 1983, doi: 10.1016/0278-4319(83)90033-6.

M. Tom and K. Annaraud, “A fuzzy multi-criteria decision making model for menu engineering,” IEEE Int. Conf. Fuzzy Syst., 2017, doi: 10.1109/FUZZ-IEEE.2017.8015612.

N. Setiyawati et al., “Implementation of Two-Step Clustering Method in Menu,” vol. 7, no. 2, 2020, doi: 10.25126/jtiik.202072012.

S. S. Nagari and L. Inayati, “Implementation of Clustering Using K-Means Method To Determine Nutritional Status,” J. Biometrika dan Kependud., vol. 9, no. 1, p. 62, 2020, doi: 10.20473/jbk.v9i1.2020.62-68.

M. Z. Rodriguez et al., Clustering algorithms: A comparative approach, vol. 14, no. 1. 2019.

Y. Singh and A. Mohan, “A Survey on Unsupervised Clustering Algorithm based on K-Means Clustering,” Int. J. Comput. Appl., vol. 156, no. 8, pp. 6–9, 2016, doi: 10.5120/ijca2016912481.

K. Abhishekkumar, “Survey Report on K-Means Clustering Algorithm,” Int. J. Mod. Trends Eng. Res., vol. 4, no. 4, pp. 218–221, 2017, doi: 10.21884/ijmter.2017.4143.lgjzd.

S. Shukla, “A Review ON K-means DATA Clustering APPROACH,” Int. J. Inf. Comput. Technol., vol. 4, no. 17, pp. 1847–1860, 2014.

A. . JAIN, M. . MURTY, and P. . FLYNN, “Data Clustering: A Review,” ACM Comput. Surv., vol. 31, no. 3, pp. 265–323, 1999.

D. Arthur and S. Vassilvitskii, “K-means++: The advantages of careful seeding,” Proc. Annu. ACM-SIAM Symp. Discret. Algorithms, vol. 07-09-Janu, pp. 1027–1035, 2007.

H. Zarzour, Z. Al-Sharif, M. Al-Ayyoub, and Y. Jararweh, “A new collaborative filtering recommendation algorithm based on dimensionality reduction and clustering techniques,” 2018 9th Int. Conf. Inf. Commun. Syst. ICICS 2018, vol. 2018-Janua, pp. 102–106, 2018, doi: 10.1109/IACS.2018.8355449.

U. Dauda and B. M. Ismail, “A study of normalization approach on K-means clustering algorithm,” Int. J. Appl. Math. Stat., vol. 45, no. 15, pp. 439–446, 2013.

V. R. Patel and R. G. Mehta, “Performance analysis of MK-means clustering algorithm with normalization approach,” Proc. 2011 World Congr. Inf. Commun. Technol. WICT 2011, pp. 974–979, 2011, doi: 10.1109/WICT.2011.6141380.

D. Virmani, S. Taneja, and G. Malhotra, “Normalization based K means Clustering Algorithm,” pp. 1–5, 2015.

G. Aksu, C. O. Güzeller, and M. T. Eser, “The Effect of the Normalization Method Used in Different Sample Sizes on the Success of Artificial Neural Network Model,” Int. J. Assess. Tools Educ., vol. 6, no. 2, pp. 170–192, 2019, doi: 10.21449/ijate.479404.

A. Martinez-Millana et al., “Optimisation of children z-score calculation based on new statistical techniques,” PLoS One, vol. 13, no. 12, pp. 1–13, 2018, doi: 10.1371/journal.pone.0208362.

S. Raschka, “Model Evaluation , Model Selection , and Algorithm Selection in Machine Learning,” 2018.

N. Hidayati, M. Ihsan, and M. Danny, “Pengaruh Singular Value Decomposition Terhadap Metode – Metode Clustering,” pp. 95–104, 2017.

A. A. Mohamed, “An effective dimension reduction algorithm for clustering Arabic text,” Egypt. Informatics J., vol. 21, no. 1, pp. 1–5, 2020, doi: 10.1016/j.eij.2019.05.002.

E. Muningsih, H. M. Nur, F. F. Dwi Imaniawan, Saifudin, V. R. Handayani, and F. Endiarto, “Comparative Analysis on Dimension Reduction Algorithm of Principal Component Analysis and Singular Value Decomposition for Clustering,” J. Phys. Conf. Ser., vol. 1641, no. 1, 2020, doi: 10.1088/1742-6596/1641/1/012101.

J. MACQUEEN, “Some Methods For Classification And Analysis Of Multivariate Observations,” 1967.

D. S. Maylawati, T. Priatna, H. Sugilar, and M. A. Ramdhani, “Data science for digital culture improvement in higher education using K-means clustering and text analytics,” Int. J. Electr. Comput. Eng., vol. 10, no. 5, pp. 4569–4580, 2020, doi: 10.11591/IJECE.V10I5.PP4569-4580.

C. M. Fikri, F. E. M. Agustin, and F. Mintarsih, “Pengelompokan Kualitas Kerja Pegawai Menggunakan Algoritma K-Means++ Dan Cop-Kmeans Untuk Merencanakan Program Pemeliharaan Kesehatan Pegawai Di Pt. Pln P2B Jb Depok,” Pseudocode, vol. 4, no. 1, pp. 9–17, 2017, doi: 10.33369/pseudocode.4.1.9-17.

S. Sukamto, I. D. Id, and T. R. Angraini, “Penentuan Daerah Rawan Titik Api di Provinsi Riau Menggunakan Clustering Algoritma K-Means,” JUITA J. Inform., vol. 6, no. 2, p. 137, 2018, doi: 10.30595/juita.v6i2.3172.

B. Aubaidan, M. Mohd, M. Albared, and F. Author, “Comparative study of k-means and k-means++ clustering algorithms on crime domain,” J. Comput. Sci., vol. 10, no. 7, pp. 1197–1206, 2014, doi: 10.3844/jcssp.2014.1197.1206.

Y. S.Thakare and S. B. Bagal, “Performance Evaluation of K-means Clustering Algorithm with Various Distance Metrics,” Int. J. Comput. Appl., vol. 110, no. 11, pp. 12–16, 2015, doi: 10.5120/19360-0929.

H. L. Sari, D. Suranti, and L. N. Zulita, “Implementation of k-means clustering method for electronic learning model,” J. Phys. Conf. Ser., vol. 930, no. 1, 2017, doi: 10.1088/1742-6596/930/1/012021.

I. A. Stmik and I. Gorontalo, “Penerapan Algoritma Singular Value Decomposition (Svd) Untuk Pengurangan Dimensi Pada High-Dimentional Biomedical Data Set,” pp. 1–7.

B. Jumadi Dehotman Sitompul, O. Salim Sitompul, and P. Sihombing, “Enhancement Clustering Evaluation Result of Davies-Bouldin Index with Determining Initial Centroid of K-Means Algorithm,” J. Phys. Conf. Ser., vol. 1235, no. 1, pp. 6–12, 2019, doi: 10.1088/1742-6596/1235/1/012015.

Y. A. Wijaya, D. A. Kurniady, E. Setyanto, W. S. Tarihoran, D. Rusmana, and R. Rahim, “Davies Bouldin Index Algorithm for Optimizing Clustering Case Studies Mapping School Facilities,” TEM J., vol. 10, no. 3, pp. 1099–1103, 2021, doi: 10.18421/TEM103-13.


  • There are currently no refbacks.

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

JOIV : International Journal on Informatics Visualization
ISSN 2549-9610  (print) | 2549-9904 (online)
Organized by Department of Information Technology - Politeknik Negeri Padang, and Institute of Visual Informatics - UKM and Soft Computing and Data Mining Centre - UTHM
W :
E :,,

View JOIV Stats

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