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All Journal Sodality: Jurnal Sosiologi Pedesaan Media Kedokteran Hewan Jurnal Kesehatan Ibu dan Anak International Journal of Advances in Data and Information Systems Majalah Ilmiah Matematika dan Statistika (MIMS) Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Jurnal Veteriner dan Biomedis Scientific Journal of Informatics Indonesian Journal of Statistics and Its Applications International Journal of Oil Palm
Sachnaz Desta Oktarina
Departemen Statistika, Fakultas Matematika Dan Ilmu Pengetahuan Alam, IPB, Bogor, Indonesia
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Environmental Science Health Professions Immunology & microbiology Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary Other

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SPADE-LSTM: An Integrated Sequential Pattern Mining and Deep Learning for Badminton Next-Stroke Prediction Sari, Jefita Resti; Oktarina, Sachnaz Desta; Erfiani, Erfiani
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v7i1.1510

Abstract

Badminton rallies consist of complex and rapid stroke transitions that reflect players’ tactical decision-making. While prior studies have examined stroke patterns descriptively or applied standalone predictive models, limited research integrates interpretable sequential pattern mining with deep learning for next-stroke prediction. This study proposes an integrated SPADE–LSTM framework to analyze and predict badminton stroke sequences using a 10-class scheme (drive, dropshot, lob, netting, and smash for two athletes). Match data were transformed into structured stroke sequences and contextual features, then divided into training, validation, and test sets using a match–set–rally grouping strategy to prevent information leakage. Sequential patterns were first extracted using the Sequential Pattern Discovery using Equivalent Classes (SPADE) algorithm to capture frequent tactical transitions. These pattern-based features were subsequently used to train a Long Short-Term Memory (LSTM) model for multi-class classification. The proposed model achieved an accuracy of 88.68%, with weighted precision, recall, and F1-score of 0.9075, 0.8868, and 0.8851, respectively. Misclassifications were mainly observed in tactically similar stroke transitions and minority classes. The results indicate that integrating interpretable sequential pattern mining with deep learning provides both strong predictive performance and meaningful tactical insights for badminton performance analysis.
Co-Authors Alpharofi, Deswita Nur Anisa, Rahma Arbaynah, Siti Ardhani, Naura Tirza Bagus Sartono Erfiani Erfiani Faizal Rafiq Fredian Tonny Nasdian Hapsari, Linda Dwi Hari Wijayanto Hasanah, Delita Nur Indriyani, Cindy Irdayanti Lusi Oktaviani Maziah, Lily Megawati - Mualifah, Laily Nissa Mualifah, Laily Nissa Atul Muzakki, Faiz Aji Najdmuddin, Muhammad Tsaqif Notodipuro, Khairil Anwar Othman, Norashida Pandansari, Niken Rahma Anisa Ratnawati Nurkhoiry Rizky Kurniawan Sari, Jefita Resti Sipayung, Tungkot Sri Wahyuni Ulfa Aulia, Risnayanti Wijaya, Ananda Putra Yarah, Helena Ramadhini Yumna, Fadhilah