Application of Data Mining using the K-Means Method for Visitor Grouping

Authors

  • Rahmayuni Syah Universitas Labuhanbatu, Indonesia
  • Marnis Nasution Universitas Labuhanbatu, Indonesia
  • Irmayanti Universitas Labuhanbatu, Indonesia

DOI:

10.33395/sinkron.v8i2.13624

Keywords:

Keywords: Clustering; Confusion Matrix; Data Mining; K-Means; Scatter Plot

Abstract

Grouping amusement ride visitor data is an important process that aims to identify certain patterns of visitors, enabling management to adjust marketing strategies and improve their services more effectively. This process begins with a data selection stage where relevant visitor data is collected and prepared for analysis. The next stage is data pre-processing, which involves cleaning the data from noise or irrelevant data, as well as ensuring the data is in a format suitable for analysis. After that, the data mining model design is carried out by selecting the most appropriate method for grouping visitor data. The next stage is testing and evaluating the model to verify its accuracy and effectiveness. The results of model testing show that visitor data can be categorized into three groups: C1 with 50 data, C2 with 20 data, and C3 with 48 data. The results of the model evaluation confirm that the designed model succeeded in classifying data with perfect accuracy, namely 100%. This success shows that the model is highly effective in identifying and segmenting visitor patterns, providing valuable insights for strategic decision making in service improvement and marketing. This success also opens up opportunities for the application of similar methods to other datasets in an effort to improve visitor experience and operational efficiency.

GS Cited Analysis

Downloads

Download data is not yet available.

References

Abas, M. I., Ibrahim, I., Syahrial, S., Lamusu, R., Baderan, U. S., & Kango, R. (2023). Analysis of Covid-19 Growth Trends Through Data Mining Approach As Decision Support. Sinkron, 8(1), 101–108. https://doi.org/10.33395/sinkron.v8i1.11861

Aji, G. W., & Devi, P. A. R. (2023). Data Mining Implementation For Product Transaction Patterns Using Apriori Method. Sinkron, 8(1), 421–432. https://doi.org/10.33395/sinkron.v8i1.12071

Aldo, D. (2023). Data Mining Sales of Skin Care Products Using the K-Means Method. Sinkron, 8(1), 295–304. https://doi.org/10.33395/sinkron.v8i1.12007

Andi, A., Juliandy, C., & David, D. (2023). Clustering Analysis of Tweets About COVID-19 Using the K-Means Algorithm. Sinkron, 8(1), 543–533. https://doi.org/10.33395/sinkron.v8i1.12145

Asriningtias, S. R., Wulandari, E. R. N., Persijn, M. B., Rosyida, N., & Sutawijaya, B. (2023). Identification of Public Library Visitor Profiles using K-means Algorithm based on The Cluster Validity Index. Sinkron, 8(4), 2615–2626. https://doi.org/10.33395/sinkron.v8i4.12901

Bustomi, Y., Nugraha, A., Juliane, C., & Rahayu, S. (2023). Data Mining Selection of Prospective Government Employees with Employment Agreements using Naive Bayes Classifier. Sinkron, 8(1), 1–8. https://doi.org/10.33395/sinkron.v8i1.11968

Hasibuan, F. F., Dar, M. H., & Yanris, G. J. (2023). Implementation of the Naïve Bayes Method to determine the Level of Consumer Satisfaction. SinkrOn, 8(2), 1000–1011. https://doi.org/10.33395/sinkron.v8i2.12349

Hasibuan, S. A., Sihombing, V., & Nasution, F. A. (2023). Analysis of Community Satisfaction Levels using the Neural Network Method in Data Mining. Sinkron, 8(3), 1724–1735. https://doi.org/10.33395/sinkron.v8i3.12634

Indah, I. C., Sari, M. N., & Dar, M. H. (2023). Application of the K-Means Clustering Agorithm to Group Train Passengers in Labuhanbatu. SinkrOn, 8(2), 825–837. https://doi.org/10.33395/sinkron.v8i2.12260

Mawaddah, A., Dar, M. H., & Yanris, G. J. (2023). Analysis of the SVM Method to Determine the Level of Online Shopping Satisfaction in the Community. SinkrOn, 8(2), 838–855. https://doi.org/10.33395/sinkron.v8i2.12261

Pratama, H. A., Yanris, G. J., Nirmala, M., & Hasibuan, S. (2023). Implementation of Data Mining for Data Classification of Visitor Satisfaction Levels. 8(3), 1832–1851.

Saputra, A. D. S., Hindarto, D., & Haryono, H. (2023). Supervised Learning from Data Mining on Process Data Loggers on Micro-Controllers. Sinkron, 8(1), 157–165. https://doi.org/10.33395/sinkron.v8i1.11942

Sari, M., Yanris, G. J., & Hasibuan, M. N. S. (2023). Analysis of the Neural Network Method to Determine Interest in Buying Pertamax Fuel. SinkrOn, 8(2), 1031–1039. https://doi.org/10.33395/sinkron.v8i2.12292

Sinaga, B., Marpaung, M., Tarigan, I. R. B., & Tania, K. (2023). Implementation of Stock Goods Data Mining Using the Apriori Algorithm. Sinkron, 8(3), 1280–1292. https://doi.org/10.33395/sinkron.v8i3.12852

Siregar, A. P., Irmayani, D., & Sari, M. N. (2023). Analysis of the Naïve Bayes Method for Determining Social Assistance Eligibility Public. SinkrOn, 8(2), 805–817. https://doi.org/10.33395/sinkron.v8i2.12259

Violita, P., Yanris, G. J., & Hasibuan, M. N. S. (2023). Analysis of Visitor Satisfaction Levels Using the K-Nearest Neighbor Method. SinkrOn, 8(2), 898–914. https://doi.org/10.33395/sinkron.v8i2.12257

Wijaya, E. B., Dharma, A., Heyneker, D., & Vanness, J. (2023). Comparison of the K-Means Algorithm and C4.5 Against Sales Data. SinkrOn, 8(2), 741–751. https://doi.org/10.33395/sinkron.v8i2.12224

Downloads


Crossmark Updates

How to Cite

Syah, R. ., Nasution, M. ., & Irmayanti, I. (2024). Application of Data Mining using the K-Means Method for Visitor Grouping. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(2), 1135 - 1147. https://doi.org/10.33395/sinkron.v8i2.13624

Most read articles by the same author(s)