MILIARTI, SASI (2025) PENGUKURAN PENGARUH PENGGUNAAN TEKNOLOGI AUGMENTED REALITY (AR) TERHADAP EFEKTIVITAS PEMBELAJARAN DI SEKOLAH MENENGAH ATAS DENGAN ALGORITMA REGRESI LINIER DAN DECISION TREE. S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
The advancement of information technology has significantly transformed the field of education, including the integration of Augmented Reality (AR) as an interactive learning medium. This study aims to measure the impact of AR technology on the effectiveness of English vocabulary learning at the senior high school level. The dataset used in this research is sourced from Kaggle, containing simulated data of AR-based vocabulary learning. The research applies a data mining approach using two main algorithms: Linear Regression and Decision Tree Regressor. Linear Regression is utilized to analyze the linear relationship between variables such as age, learning duration, pre-test scores, student engagement, activity type, and AR features with the feedback score. Meanwhile, the Decision Tree algorithm is employed to capture more complex non-linear patterns that can better explain variations in the feedback score. Model evaluation is conducted using MAE, MSE, and R² Score metrics. The results indicate that the Decision Tree model performs better than Linear Regression, achieving an R² Score of 0.130 on the training data. Variables such as Pre_Test_Score, Duration, and Activity_Type are identified as the most influential factors in determining learning effectiveness. The decision tree visualization helps map decision-making patterns that can serve as a reference for designing more effective AR-based learning strategies. This study contributes to the implementation of AR technology and machine learning algorithms to support quantitative evaluation of learning outcomes. Keywords: Augmented Reality, Interactive Learning, Linear Regression, Decision Tree, Feedback Score, Data Mining. Perkembangan teknologi informasi telah mendorong transformasi dalam dunia pendidikan, salah satunya melalui pemanfaatan Augmented Reality (AR) sebagai media pembelajaran interaktif. Penelitian ini bertujuan untuk mengukur pengaruh penggunaan teknologi AR terhadap efektivitas pembelajaran kosakata bahasa Inggris di tingkat Sekolah Menengah Atas. Dataset yang digunakan berasal dari platform Kaggle, yang berisi data simulasi pembelajaran berbasis AR. Penelitian ini menggunakan pendekatan data mining dengan dua algoritma utama, yaitu Regresi Linier dan Decision Tree Regressor. Regresi Linier digunakan untuk menganalisis hubungan linier antara variabelvariabel seperti usia, durasi, skor pre-test, keterlibatan siswa, jenis aktivitas, dan fitur AR terhadap hasil belajar. Sementara itu, algoritma Decision Tree digunakan untuk menangkap pola non-linier yang dapat menjelaskan variasi skor umpan balik secara lebih interpretatif. Evaluasi model dilakukan menggunakan metrik MAE, MSE, dan R² Score. Hasil penelitian menunjukkan bahwa model Decision Tree memiliki kinerja yang lebih baik dibandingkan Regresi Linier, dengan R² Score sebesar 0.130 pada data training. Variabel skor pre-test, durasi, jenis aktivitas ditemukan sebagai faktor paling berpengaruh terhadap efektivitas pembelajaran. Visualisasi pohon keputusan membantu memetakan pola-pola keputusan yang dapat dijadikan acuan dalam merancang strategi pembelajaran berbasis AR yang lebih optimal. Penelitian ini memberikan kontribusi dalam pemanfaatan teknologi AR serta penerapan algoritma machine learning untuk mendukung evaluasi pembelajaran secara kuantitatif. Kata Kunci: Augmented Reality, Pembelajaran Interaktif, Regresi Linier, Decision Tree, Feedback Score, Data Mining.
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