Pengembangan Perangkat Deep Learning dengan Strategi Kr-Heuristic Berbasis Ethno-Stem untuk Meningkatkan Kemampuan Berpikir Lateral Matematis Siswa
Keywords:
Deep Learning, KR-Heuristic, Ethno-STEM, Berpikir Lateral, Pengembangan Perangkat PembelajaranAbstract
Penelitian ini bertujuan untuk mengembangkan perangkat pembelajaran matematika berbasis Deep Learning dengan strategi KR-Heuristic dan pendekatan Ethno-STEM guna meningkatkan kemampuan berpikir lateral matematis siswa SMP. Metode yang digunakan adalah penelitian dan pengembangan (R&D) dengan model 4D yang terdiri atas tahap Define, Design, Develop, dan Disseminate. Tahap define mencakup analisis kebutuhan, karakteristik siswa, kurikulum, serta identifikasi kearifan lokal. Tahap design menghasilkan rancangan LKPD, modul ajar, instrumen penilaian, serta integrasi strategi KR-Heuristic. Pada tahap develop dilakukan validasi ahli, revisi, dan uji coba terbatas kepada siswa kelas VIII di sekolah mitra. Hasil validasi menunjukkan bahwa perangkat yang dikembangkan berada pada kategori sangat valid. LKPD memperoleh tingkat kevalidan 85,33%, modul pembelajaran 93,85%, dan rata-rata validitas ahli mencapai 91,69%. Uji coba terbatas menunjukkan bahwa perangkat praktis digunakan dalam pembelajaran, ditandai dengan keterlaksanaan pembelajaran yang baik serta respons positif guru dan siswa. Efektivitas perangkat ditunjukkan melalui nilai rata-rata N-gain sebesar 0,7372 (kategori tinggi), yang menandakan adanya peningkatan signifikan dalam kemampuan berpikir lateral matematis siswa setelah menggunakan perangkat. Dengan demikian, perangkat pembelajaran berbasis Deep Learning dengan strategi KR-Heuristic berbasis Ethno-STEM dinyatakan layak dan efektif digunakan untuk meningkatkan kemampuan berpikir lateral dalam pembelajaran matematika. Perangkat ini juga berpotensi menjadi model pembelajaran inovatif yang relevan diterapkan dalam konteks Kurikulum Merdeka.
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Copyright (c) 2025 Ahmad Muzaki, Agus Fianuddin, Sabrun Sabrun, Lalu Sirajul Hadi, Khosiah Khosiah, Kamarudin Kamarudin
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