Analysis of Chess Opening Patterns Using Data Mining Methods to Determine Players’ Strategic Tendencies

Femasta Sembiring, Rahmadani Rahmadani, Muhammad Fauzi Muhammad Fauzi

Sari


Online chess games generate large-scale match data that can be used to objectively analyze opening patterns. This study aims to cluster chess openings based on game statistical characteristics using the K-Means Clustering algorithm. The dataset was obtained from Lichess in PGN format, focusing on Rapid games played by players with ratings above 1200. The data were selected, preprocessed, transformed based on ECO codes, and reduced to the 300 most frequent ECO codes. The variables used were Win Rate, Draw Rate, and Average Moves, which were standardized using Z-Score. Testing was conducted using 3-, 4-, and 5-cluster scenarios and evaluated using the Davies-Bouldin Index. The DBI values for each scenario were 0.9538, 0.8715, and 0.7312. The best result was obtained by the 5-cluster scenario because it produced the smallest DBI value. The clustering results show that chess openings can be grouped into aggressive, solid, balanced, and special-characteristic tendencies.

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R. McIlroy-Young, R. Wang, S. Sen, J. Kleinberg, and A. Anderson, “Learning Models of Individual Behavior in Chess,” Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., no. August 2022, pp. 1253–1263, 2022, doi: 10.1145/3534678.3539367.

S. Chowdhary, I. Iacopini, and F. Battiston, “Quantifying human performance in chess,” Sci. Rep., vol. 13, no. 1, pp. 1–8, 2023, doi: 10.1038/s41598-023-27735-9.

R. Sanjaya, J. Wang, and Y. Yang, “Measuring the Non-Transitivity in Chess,” pp. 1–22, 2022.

G. De Marzo and V. D. P. Servedio, “Quantifying the complexity and similarity of chess openings using online chess community data,” Sci. Rep., vol. 13, no. 1, 2023, doi: 10.1038/s41598-023-31658-w.

F. Wijayanto, “Clustering Analysis of Chess Portable Game Notation Text,” J. Sains, Nalar, dan Apl. Teknol. Inf., vol. 3, no. 3, pp. 137–142, 2024, doi: 10.20885/snati.v3.i3.42.

F. Wijayanto, “Forsyth-Edwards Notation in Chess Game Clustering: A Depth-Based Evaluation,” J. Sains, Nalar, dan Apl. Teknol. Inf., vol. 4, no. 1, pp. 18–25, 2025, doi: 10.20885/snati.v4.i1.3.

F. Wijayanto, “Jurnal Sains , Nalar , dan Aplikasi Teknologi Informasi Mapping the Safest Routes : A Clustering Study of the French Defense,” vol. 4, no. 2, pp. 54–61, 2025, doi: 10.20885/snati.v4.i2.40910.

M. A. Aldi et al., “IMPLEMENTASI K-MEANS CLUSTER ING DALAM PENGELOMPOKAN DATA KUNJUNGAN,” vol. 2, no. 1, pp. 13–19, 2025.

B. Aji, K. Batam, and K. Riau, “BLITZ PADA DATASET TIDAK SEIMBANG SKALA BESAR MENGGUNAKAN,” vol. 13, no. 1, pp. 46–52, 2026.

H. L. Siregar, R. Hidayanthi, and A. L. D. Sakti, “Implementation of K-Means clustering on student learning achievements based on social economic and social related,” Res. Dev. Educ., vol. 4, no. 2, pp. 1447–1459, 2024, doi: 10.22219/raden.v4i2.36742.

S. R. Agustin, I. Purnamasari, and B. N. Sari, “Implementasi K-Means Untuk Pengelompokan Kategori Penjualan Barang Berbasis Web,” vol. 5, no. 3, pp. 167–176, 2025, doi: 10.47065/jimat.v5i3.610.

M. D. Adrian and F. Wijayanto, “Chesstify : Chess Game Database Management System using Portable Game Notation,” vol. 8, no. 1, 2026, doi: 10.35842/ijicom.

M. Rochmawati et al., “Implementasi Algoritma K-Means dalam Klasterisasi Penjualan pada Sebuah Perusahaan menggunakan Metodologi KDD Implementation of the K-Means Algorithm in Sales Clustering at a Company using the KDD Methodology,” vol. 13, pp. 54–62, 2024.

D. A. Tarigan, “Optimization of the K-Means Clustering Algorithm Using Davies Bouldin Index in Iris Data Classification,” vol. 4, no. 1, pp. 545–552, 2023, doi: 10.30865/klik.v4i1.964.

C. W. Id, “The impact of neglecting feature scaling in k-means clustering,” pp. 1–19, 2024, doi: 10.1371/journal.pone.0310839.

J. Reiner, B. Stilwell, and A. Wahbeh, “Leveraging K-Means Clustering and Z-Score for Anomaly Detection in Bitcoin Transactions,” 2025.

F. Anggraeny, “Penerapan K-Means dengan Evaluasi Davies-Bouldin Index untuk Pengelompokan Kelas Unggulan SMP Wijaya Sukodono,” vol. 15, no. 2, pp. 372–382, 2025.

T. Ikhsan, E. Haerani, F. Wulandari, and F. Syafria, “Clustering Data Penduduk Menggunakan Algoritma K-Means TIN : Terapan Informatika Nusantara,” vol. 5, no. 12, pp. 679–687, 2025, doi: 10.47065/tin.v5i12.7328.

N. Amalia, “Z-Score Based Initialization for K-Medoids Clustering : Application on QSAR Toxicity Data,” vol. 9, no. 5, pp. 2410–2417, 2025.

S. Syaqila and M. Fakhriza, “K-Means Clustering Untuk Mengukur Pengaruh Kompetensi Terhadap Kinerja Pegawai,” vol. 6, no. 2, pp. 1420–1431, 2025, doi: 10.47065/josh.v6i2.6758.

I. D. Setiawan and A. Triayudi, “Penerapan Algoritma Clustering K-Means Data Mining dalam Pengelompokan Mahasiswa Penerima Beasiswa,” vol. 5, no. 2, pp. 430–441, 2024, doi: 10.47065/josyc.v5i2.4971.

U. W. Latifah, S. Bahri, and M. Satriandhini, “Implementasi Algoritma K-Means Clustering untuk Strategi Promosi Kampus IBISA Implementation of K-Means Clustering Algorithm for IBISA Campus Promotion Strategy,” no. 2, pp. 292–300, 2024, doi: 10.26798/jiko.v8i2.1307.

M. Sholeh and K. Aeni, “Perbandingan Evaluasi Metode Davies Bouldin, Elbow dan Silhouette pada Model Clustering dengan Menggunakan Algoritma K-Means,” STRING (Satuan Tulisan Ris. dan Inov. Teknol., vol. 8, no. 1, p. 56, 2023, doi: 10.30998/string.v8i1.16388.

I. F. Ashari, E. D. Nugroho, R. Baraku, I. N. Yanda, and R. Liwardana, “Analysis of Elbow , Silhouette , Davies-Bouldin , Calinski-Harabasz , and Rand-Index Evaluation on K-Means Algorithm for Classifying Flood- Affected Areas in Jakarta,” vol. 7, no. 1, pp. 95–103, 2023.

A. Saputra and R. Yusuf, “Comparison of the DBSCAN and K-MEANS Algorithms in Segmenting Customers Using Public Transportation of Transjakarta Using the RFM Method Perbandingan Algoritma DBSCAN dan K-MEANS dalam Segmentasi Pelanggan Pengguna Transportasi Publik Transjakarta Menggunakan Metode RFM,” vol. 4, no. October, pp. 1346–1361, 2024.

L. Bai and J. Liang, “A categorical data clustering framework on graph representation,” Pattern Recognit., vol. 128, p. 108694, 2022, doi: 10.1016/j.patcog.2022.108694.

I. F. Ashari, R. Banjarnahor, and D. R. Farida, “Application of Data Mining with the K-Means Clustering Method and Davies Bouldin Index for Grouping IMDB Movies,” vol. 6, no. 1, pp. 7–15, 2022.

B. Ariansah et al., “Penerapan K-Means Clustering untuk Pengelompokan Data Industri Kecil Menengah di Provinsi Jambi,” vol. 6, no. September, pp. 359–371, 2025, doi: 10.35957/jtsi.v6i2.13553.

N. A. Yolandari, L. E. Butarbutar, G. Citra, and H. Rajagukguk, “ANALISIS PERBANDINGAN K-MEANS DAN DBSCAN DALAM PENGELOMPOKAN DATA TRAVEL REVIEW RATINGS MENGGUNAKAN EVALUASI SILHOUETTE INDEX DAN DAVIES-BOULDIN INDEX,” vol. 13, no. 3.

M. D. Salman, N. R. Pratama, and M. N. F. A, “Comparison of K-Means and K-Medoids Clustering Algorithm Performance in Grouping Schools in Riau Province Based on Availability of Facilities and Infrastructure Perbandingan Kinerja Algoritma Clustering K-Means dan K-Medoids dalam Pengelompokan Sekolah di Provinsi Riau Berdasarkan Ketersediaan Sarana dan Prasarana,” vol. 5, no. July, pp. 797–806, 2025.




DOI: http://dx.doi.org/10.30811/jaise.v6i2.9781

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