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All Journal Syntax Literate: Jurnal Ilmiah Indonesia Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi
Wijayanto, Feri
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Humanities Computer Science & IT Education Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Forsyth-Edwards Notation in Chess Game Clustering: A Depth-Based Evaluation Wijayanto, Feri
Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi Vol. 4 No. 1 (2025)
Publisher : Department of Informatics Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/snati.v4.i1.3

Abstract

Chess games clustering poses the challenge of accurately grouping games with similar strategies and positions, especially when the openings are similar. Previous research has used Portable Game Notation (PGN) as a feature for clustering, but its emphasis on move order can limit position transposition. This research addresses this limitation by evaluating Forsyth-Edwards Notation (FEN), which focuses on board position, as an alternative. Hierarchical clustering with complete linkage and K-means clustering were used to analyze 100 chess games at move depths of 20, 30, 40, and 60. Both methods effectively cluster games involving the English Opening and the Queen's Gambit Declined, with FEN providing slightly better differentiation than PGN. However, challenges remain in grouping French Defence variations, especially the Poulsen Attack and variations with 6.a3, due to positional similarities. This study underlines the robustness of FEN for clustering tasks and its compatibility with hierarchical clustering, highlighting the important role of move depth. The results provide a basis for refining clustering methods and using larger data sets to deepen insights into chess strategies.
Mapping the Safest Routes: A Clustering Study of the French Defense Wijayanto, Feri
Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi Vol. 4 No. 2 (2025)
Publisher : Department of Informatics Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/snati.v4.i2.40910

Abstract

This study explores the safest variations in the French Defense using 5,156 artificially generated chess games with Stockfish 17. Unlike prior work reliant on historical data, this method reduces theory bias by randomly selecting from the engine's top five moves at each position. We applied k-means clustering with cosine similarity to group move sequences based on evaluation scores. Both two-cluster and three-cluster models were tested. Stability was assessed via 50 resamples using 50% of the data. The three-cluster model, which includes a neutral group, had excellent stability (ARI = 0.99) but moderate cohesion (silhouette = 0.53). The two-cluster model showed better cohesion (silhouette = 0.65) but lower stability (ARI = 0.68). Among the variations, e5 (Advance) and exd5 (Exchange) stood out, with about 54% of games in each line falling into clusters favoring White. This suggests they are the safest and most reliable options. In contrast, Bb5+ performed well in simulations but poorly in real-world data, indicating theoretical risks. In summary, clustering on simulated games reveals hidden strategic insights, confirming e5 and exd5 as strong, low-risk choices for White in the French Defense.
Sistem Informasi Terpadu Sepakbola di Sleman Mustofa, Muhammad Rizqy; Wijayanto, Feri; Paputungan, Irving Vitra
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : Syntax Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36418/syntax-literate.v10i9.61787

Abstract

Kurangnya pusat informasi yang terintegrasi mengenai kegiatan sepakbola di Kabupaten Sleman menjadi kendala bagi masyarakat dan para pegiat sepakbola. Informasi seringkali tersebar secara parsial dari mulut ke mulut atau terbatas pada sumber berita tertentu yang hanya meliputi tim profesional. Penelitian ini bertujuan untuk merancang dan membangun sebuah Sistem Informasi Sepakbola Terpadu berbasis website bernama Football of Sleman (FOSE) untuk mengatasi masalah tersebut. Pengembangan sistem ini menggunakan metode waterfall yang meliputi tahapan analisis kebutuhan, desian, implementasi, dan pengujian. Hasil dari penelitian ini Adalah sebah platform digital yang menyajikan beragam informasi penting seperti daftar berita terkini, profil klub sepakbola (profesional, amatir, hingga sekolah sepakbola), direktori lapangan yang ada di wilayah Sleman. Dengan adanya sistem ini, diharapkan masyarakat dapat dengan mudah mengakses informasi sepakbola secara terpusat, sehingga meningkatkan visibilitas klub-klub lokal dan memudahkan pengelolaan informasi bagi para pemangku kepentingan.
Clustering Analysis of Chess Portable Game Notation Text Wijayanto, Feri
Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi Vol. 3 No. 3 (2024)
Publisher : Department of Informatics Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/snati.v3.i3.42

Abstract

Chess is a game that requires a high level of intelligence and strategy. Generally, in order to understand complex move patterns and strategies, the expertise of chess masters is required. With the rapid development in the field of machine learning, the digitization of chess game recordings in Portable Game Notation (PGN) format, and the availability of large and widely accessible data, it is possible to apply machine learning techniques to analyze chess games. This research studies the use of text clustering algorithms, specifically hierarchical clustering and K-means clustering, to categorize chess games based on their moves. We extracted 100 chess games that use certain openings such as French Defence, Queen's Gambit Declined, and English Opening. In the implementation of hierarchical clustering, single, average, and complete linkage methods are used. As a result, our findings show that hierarchical clustering with single linkage is less effective. On the other hand, the average and complete linkage methods, as well as K-means clustering, successfully identify clusters corresponding to the original openings. Notably, K-means clustering showed the highest accuracy in clustering chess games. This research highlights the potential of machine learning techniques in uncovering strategic patterns in chess games, paving the way for deeper insights into game strategies.
Co-Authors Mustofa, Muhammad Rizqy Paputungan, Irving Vitra