<![CDATA[Abstract: Oil palm leaf diseases can significantly reduce harvest productivity. Manual identification is considered less effective because it requires a long time and carries the risk of misidentification. This study develops a leaf image detection system using a CNN with the MobileNetV2 architecture, implemented on both web and mobile platforms. A dataset of 1,200 images from Kaggle and field observations covers four classes: leaf spot, anthracnose, yellow stripe, and Other. The research stages include data collection, image preprocessing, dataset splitting, CNN model training using the MobileNetV2 architecture, model testing, performance evaluation, and system implementation on web and mobile platforms. Evaluation was conducted using confusion matrix, accuracy, precision, recall, and F1-score parameters. Based on testing results, the model achieved 93.33% accuracy, 93.71% precision, 92.92% recall, and 93.31% F1-score. The developed system was successfully implemented on web and mobile platforms with features such as image upload, automatic classification, detection history, and disease management recommendations. With this system, users are expected to perform early detection of oil palm leaf diseases more quickly, practically, and accurately. Keywords: CNN, Deep Learning, Image Classification, MobileNetV2, Oil Palm Leaf Disease. Abstrak: Penyakit daun kelapa sawit dapat menurunkan produktivitas panen secara signifikan. Identifikasi manual dinilai kurang efektif karena membutuhkan waktu lama dan berpotensi salah identifikasi. Penelitian ini membangun sistem deteksi citra daun menggunakan CNN arsitektur MobileNetV2 berbasis web dan mobile. Dataset 1.200 citra dari Kaggle dan observasi lapangan mencakup empat kelas: bercak daun, antraknosa, garis kuning, dan Other. Tahapan penelitian meliputi pengumpulan data, preprocessing citra, pembagian dataset, pelatihan model CNN menggunakan arsitektur MobileNetV2, pengujian model, evaluasi performa, serta implementasi sistem berbasis web dan mobile. Proses evaluasi dilakukan menggunakan parameter confusion matrix, accuracy, precision, recall, dan F1-score. Berdasarkan hasil pengujian, model memperoleh akurasi 93,33%, precision 93,71%, recall 92,92%, dan F1-score 93,31%. Sistem yang dibangun berhasil diimplementasikan pada platform web dan mobile dengan fitur unggah citra, proses klasifikasi otomatis, riwayat deteksi, serta rekomendasi penanganan penyakit. Dengan adanya sistem ini, pengguna diharapkan dapat melakukan deteksi dini penyakit daun kelapa sawit secara lebih cepat, praktis, dan akurat. Kata Kunci: CNN, Deep Learning, Klasifikasi Citra, MobileNetV2, Penyakit Daun Sawit.]]>
