Evaluasi Efektivitas Model Deep Learning Dalam Meningkatkan Hasil Belajar Kognitif: Sebuah Studi Eksperimen Semu
Keywords:
Deep Learning, Cognitive Learning Outcomes, Quasi-Experimental, CNN, LSTM, Bloom's Taxonomy., Deep Learning, Hasil Belajar Kognitif, Quasi-Experimental, CNN, LSTM, Taksonomi Bloom.Abstract
This study aims to evaluate the effectiveness of applying deep learning models based on Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) as adaptive media in improving cognitive learning outcomes of tenth-grade high school students in Science subjects. A quasi-experimental method with a nonequivalent control group pretest-posttest design was employed. A total of 64 students (32 experimental, 32 control) were selected via purposive sampling. Research instruments consisted of Bloom's Taxonomy-based cognitive tests (C1–C6) with Cronbach Alpha reliability (α = 0.89). Data were analyzed using independent t-test and ANCOVA with pretest scores as covariates. Results showed that the experimental group achieved significantly higher post-test mean scores (M = 82.19; SD = 7.45) compared to the control group (M = 67.56; SD = 8.93) (t(62) = 7.84, p < .001). ANCOVA revealed a large main effect (η² = .501, F(1,61) = 61.37, p < .001). Cohen's d = 1.87 indicated a very large effect size. These findings support the integration of deep learning models as effective adaptive learning media in formal educational contexts.
Keywords: Deep Learning, Cognitive Learning Outcomes, Quasi-Experimental, CNN, LSTM, Bloom's Taxonomy.
Penelitian ini bertujuan untuk mengevaluasi efektivitas penerapan model deep learning berbasis Convolutional Neural Network (CNN) dan Long Short-Term Memory (LSTM) sebagai media adaptif dalam meningkatkan hasil belajar kognitif siswa pada mata pelajaran Sains kelas X SMA. Metode yang digunakan adalah quasi-experimental dengan desain nonequivalent control group pretest-posttest. Sebanyak 64 siswa (32 eksperimen, 32 kontrol) dipilih melalui purposive sampling. Instrumen penelitian berupa tes kognitif berbasis Taksonomi Bloom (C1–C6) dengan reliabilitas Cronbach Alpha (α = 0,89). Data dianalisis menggunakan uji-t independen dan ANCOVA dengan kovariat skor pretest. Hasil penelitian menunjukkan bahwa kelompok eksperimen memperoleh rata-rata post-test lebih tinggi (M = 82,19; SD = 7,45) dibandingkan kelompok kontrol (M = 67,56; SD = 8,93), dengan perbedaan yang signifikan secara statistik (t(62) = 7,84, p < .001). Analisis ANCOVA mengungkap efek utama yang besar (η² = .501, F(1,61) = 61.37, p < .001). Ukuran efek Cohen's d = 1,87 mengindikasikan efek yang sangat besar (very large effect). Temuan ini mendukung integrasi model deep learning sebagai media pembelajaran adaptif yang efektif dalam konteks pendidikan formal.
Keywords: Deep Learning, Hasil Belajar Kognitif, Quasi-Experimental, CNN, LSTM, Taksonomi Bloom.
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