Pemanfaatan Deep Learning dalam Pengembangan Komunikasi Matematis: Systematic Literature Review
Keywords:
deep learning, komunikasi matematis, Prisma, komunikasi lisan, komunikasi tulisanAbstract
Komunikasi matematis merupakan kompetensi esensial dalam pembelajaran matematika karena merefleksikan kemampuan peserta didik dalam mengungkapkan ide, penalaran, dan pemahaman konsep secara lisan, tertulis, maupun visual. Seiring perkembangan kecerdasan buatan, khususnya deep learning, berbagai penelitian mulai mengeksplorasi potensinya dalam menganalisis respons matematis siswa dan mendukung penilaian serta umpan balik pembelajaran. Penelitian ini bertujuan untuk mengkaji secara sistematis pemanfaatan deep learning dalam pengembangan komunikasi matematis melalui pendekatan Systematic Literature Review (SLR). Proses penelusuran literatur dilakukan pada basis data Scopus dan Google Scholar dengan kriteria inklusi dan eksklusi tertentu, serta mengikuti alur seleksi PRISMA, sehingga diperoleh 10 artikel yang dianalisis. Hasil SLR menunjukkan adanya peningkatan tren penelitian dalam beberapa tahun terakhir, dengan dominasi penggunaan model Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN/LSTM), dan transformer untuk menganalisis respons tertulis, representasi visual, serta penilaian otomatis komunikasi matematis. Namun demikian, kajian ini juga menemukan variasi definisi dan pengukuran komunikasi matematis serta kecenderungan penelitian yang masih berorientasi teknis. Oleh karena itu, diperlukan penelitian lanjutan yang mengintegrasikan pendekatan pedagogis dan deep learning secara lebih seimbang agar pemanfaatannya berdampak langsung pada peningkatan kualitas komunikasi matematis peserta didik.
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