• Sekar Setyaningtyas STMIK Tegal
  • Bangkit Indarmawan Nugroho Sekolah Tinggi Manajemen Informatika dan Komputer Tegal
  • Zaenul Arif Sekolah Tinggi Manajemen Informatika dan Komputer Tegal


data mining, clustering, K-Means, systematic literature review


Data Mining is a method for analyzing future patterns and characteristics as well as gathering unexpected,

never-before-seen information from large databases. In data mining, clustering is one of the useful

techniques for analyzing data. One of the data mining algorithms is the K-Means algorithm, which is a

clustering technique based on distance division. The goal to be achieved in this paper is to analyze the

clustering technique using the K-Means algorithm in data mining by conducting an in-depth review and

searching through the literature selected based on the criteria and studies that will be selected to answer

research questions. Systematic Literature Review (SLR) is a method that aims to identify and find research

results with techniques based on specific procedures from comparison results. Based on the literature on

the selection of journal publications, Pattern Recognition, Knowledge-Based Systems, Applied Soft

Computing and IEEE Access can be the main references related to the K-Means algorithm. The results of

the comparison show that Euclidean Distance has the advantage of better distance calculation, so that this

method can be used as the main choice related to the calculation theory of the K-Means algorithm.


S. Jambekar and Z. Saquib, “Prediction of Crop Production in India Using Data Mining Techniques,” 2018 Fourth Int. Conf. Comput. Commun. Control Autom., pp. 1–5, 2018.

Y. Yin, L. Long, and X. Deng, “Dynamic Data Mining of Sensor Data,” IEEE Access, vol. 8, pp. 41637–41648, 2020, doi: 10.1109/ACCESS.2020.2976699.

G. Gustientiedina, M. H. Adiya, and Y. Desnelita, “Penerapan Algoritma K-Means Untuk Clustering Data Obat-Obatan,” J. Nas. Teknol. dan Sist. Inf., vol. 5, no. 1, pp. 17–24, 2019, doi: 10.25077/teknosi.v5i1.2019.17-24.

P. O. Olukanmi and B. Twala, “K-Means-sharp: Modified centroid update for outlier-robust K-Means clustering,” 2017 Pattern Recognit. Assoc. South Africa Robot. Mechatronics Int. Conf. PRASA-RobMech 2017, vol. 2018-Janua, pp. 14–19, 2017, doi: 10.1109/RoboMech.2017.8261116.

M. S. Yang and K. P. Sinaga, “A feature-reduction multi-view K-Means clustering algorithm,” IEEE Access, vol. 7, pp. 114472–114486, 2019, doi: 10.1109/ACCESS.2019.2934179.

M. A. Mercioni and S. Holban, “Evaluating hierarchical and non-hierarchical grouping for develop a smart system,” 2018 13th Int. Symp. Electron. Telecommun. ISETC 2018 - Conf. Proc., pp. 1–4, 2018, doi: 10.1109/ISETC.2018.8583997.

pallavi pallu, R. Suryawnashi, A. Dubey, and A. Abha Choubey, “A Systematic Review on K-Means Clustering Techniques Related papers A Novel Approach for Dat a Clust ering using Improved K-Means Algorit hm A Systematic Review on K-Means Clustering Techniques,” Int. J. Sci. Res. Eng. Technol., vol. 6, no. 6, 2017.

W. DIng, Y. Zhang, Y. Sun, and T. Qin, “An Improved SFLA-Kmeans Algorithm Based on Approximate Backbone and Its Application in Retinal Fundus Image,” IEEE Access, vol. 9, pp. 72259–72268, 2021, doi: 10.1109/ACCESS.2021.3079119.

S. S. Yu, S. W. Chu, C. M. Wang, Y. K. Chan, and T. C. Chang, “Two improved K-Means algorithms,” Appl. Soft Comput. J., vol. 68, pp. 747–755, 2018, doi: 10.1016/j.asoc.2017.08.032.

C. Kamila, M. Adiyatma, G. R. Namang, R. Ramadhan, F. Syah, and D. Redaksi, “Pendidikan Teknik Informatika dan Komputer, Fakultas Teknik,” J. Intech, vol. 2, no. 1, p. 1, 2021.

A. Ashabi, S. Bin Bin Sahibuddin, and M. Salkhordeh Salkhordeh Haghighi, “The systematic review of K-Means clustering algorithm,” ACM Int. Conf. Proceeding Ser., pp. 13–18, 2020, doi: 10.1145/3447654.3447657.

D. W. L. Pamungkas and S. Rochimah, “Pengujian Aplikasi Web - Tinjauan Pustaka Sistematis,” J. IPTEK, vol. 23, no. 1, pp. 17–24, 2019, doi: 10.31284/j.iptek.2019.v23i1.459.

R. S. Wahono, “A Systematic Literature Review of Software Defect Prediction: Research Trends, Datasets, Methods and Frameworks,” J. Softw. Eng., vol. 1, no. 1, pp. 1–16, 2015.

B. Kitchenham, O. Pearl Brereton, D. Budgen, M. Turner, J. Bailey, and S. Linkman, “Systematic literature reviews in software engineering - A systematic literature review,” Inf. Softw. Technol., vol. 51, no. 1, pp. 7–15, 2009, doi: 10.1016/j.infsof.2008.09.009.

M. Capó, A. Pérez, and J. A. Lozano, “An efficient approximation to the K-Means clustering for massive data,” Knowledge-Based Syst., vol. 117, pp. 56–69, 2017, doi: 10.1016/j.knosys.2016.06.031.

L. Bai, X. Cheng, J. Liang, H. Shen, and Y. Guo, “Fast density clustering strategies based on the K-Means algorithm,” Pattern Recognit., vol. 71, pp. 375–386, 2017, doi: 10.1016/j.patcog.2017.06.023.

A. P. Windarto, “Implementation of Data Mining on Rice Imports by Major Country of Origin Using Algorithm Using K-Means Clustering Method,” Int. J. Artif. Intell. Res., vol. 1, no. 2, p. 26, 2017, doi: 10.29099/ijair.v1i2.17.

S. Wang et al., “K-Means Clustering With Incomplete Data,” IEEE Access, vol. 7, pp. 69162–69171, 2019, doi: 10.1109/ACCESS.2019.2910287.

H. Xie et al., “Improving K-Means clustering with enhanced Firefly Algorithms,” Appl. Soft Comput. J., vol. 84, p. 105763, 2019, doi: 10.1016/j.asoc.2019.105763.

Yuda Irawan, “Implementation Of Data Mining For Determining Majors Using K-Means Algorithm In Students Of SMA Negeri 1 Pangkalan Kerinci,” J. Appl. Eng. Technol. Sci., vol. 1, no. 1, pp. 17–29, 2019, doi: 10.37385/jaets.v1i1.18.

T. Li, Y. Ma, and T. Endoh, “Normalization-based validity index of adaptive K-Means clustering for multi-solution application,” IEEE Access, vol. 8, pp. 9403–9419, 2020, doi: 10.1109/ACCESS.2020.2964763.

C. Xia, J. Hua, W. Tong, and S. Zhong, “Distributed K-Means clustering guaranteeing local differential privacy,” Comput. Secur., vol. 90, 2020, doi: 10.1016/j.cose.2019.101699.

J. Chen, X. Qi, L. Chen, F. Chen, and G. Cheng, “Quantum-inspired ant lion optimized hybrid K-Means for cluster analysis and intrusion detection,” Knowledge-Based Syst., vol. 203, p. 106167, 2020, doi: 10.1016/j.knosys.2020.106167.

A. Kaur, S. K. Pal, and A. P. Singh, “Hybridization of Chaos and Flower Pollination Algorithm over K-Means for data clustering,” Appl. Soft Comput., vol. 97, no. xxxx, p. 105523, 2020, doi: 10.1016/j.asoc.2019.105523.

H. H. Zhao, X. C. Luo, R. Ma, and X. Lu, “An Extended Regularized K-Means Clustering Approach for High-Dimensional Customer Segmentation with Correlated Variables,” IEEE Access, vol. 9, pp. 48405–48412, 2021, doi: 10.1109/ACCESS.2021.3067499.

X. Wang, Z. Wang, M. Sheng, Q. Li, and W. Sheng, “An adaptive and opposite K-Means operation based memetic algorithm for data clustering,” Neurocomputing, vol. 437, pp. 131–142, 2021, doi: 10.1016/j.neucom.2021.01.056.

Y. Fan et al., “PPMCK: Privacy-preserving multi-party computing for K-Means clustering,” J. Parallel Distrib. Comput., vol. 154, pp. 54–63, 2021, doi: 10.1016/j.jpdc.2021.03.009.

A. Rizwan, N. Iqbal, A. N. Khan, R. Ahmad, and D. H. Kim, “Toward Effective Pattern Recognition Based on Enhanced Weighted K-Mean Clustering Algorithm for Groundwater Resource Planning in Point Cloud,” IEEE Access, vol. 9, pp. 130154–130169, 2021, doi: 10.1109/ACCESS.2021.3111112.

S. Huang, Z. Kang, Z. Xu, and Q. Liu, “Robust deep K-Means: An effective and simple method for data clustering,” Pattern Recognit., vol. 117, p. 107996, 2021, doi: 10.1016/j.patcog.2021.107996.



How to Cite

Sekar Setyaningtyas, Indarmawan Nugroho, B. ., & Arif, Z. . (2022). TINJAUAN PUSTAKA SISTEMATIS PADA DATA MINING: STUDI KASUS ALGORITMA K-MEANS CLUSTERING. Jurnal Teknoif Teknik Informatika Institut Teknologi Padang, 10(2), 52–61. https://doi.org/10.21063/jtif.2022.V10.2.52-61