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      "title": "ANALISIS PERFORMA MOBILENETV2 PADA DETEKSI\r\nKERUSAKAN JALAN",
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      "keywords": "MobileNetV2, Deteksi kerusakan jalan, Deep learning, Klasifikasi\r\ncitra, Computer vision, Road damage detection, Image classification.",
      "institution": "Universitas AMIKOM Yogyakarta",
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      "abstract": "Kondisi kerusakan jalan merupakan salah satu permasalahan infrastruktur\r\nyang berdampak langsung terhadap keselamatan pengguna jalan, kenyamanan\r\nberkendara, serta efisiensi distribusi logistik. Kerusakan seperti retakan, lubang,\r\ndan deformasi permukaan jalan sering kali sulit terdeteksi secara cepat apabila\r\nmasih mengandalkan inspeksi manual, sehingga berpotensi menyebabkan\r\nketerlambatan penanganan dan peningkatan risiko kecelakaan. Oleh karena itu,\r\ndiperlukan suatu pendekatan otomatis berbasis teknologi yang mampu mendeteksi\r\ndan mengklasifikasikan tingkat kerusakan jalan secara akurat dan efisien.\r\nPenelitian ini menggunakan pendekatan deep learning dengan arsitektur\r\nConvolutional Neural Network (CNN) berbasis MobileNetV2 untuk mendeteksi dan\r\nmengklasifikasikan tingkat kerusakan jalan. Dataset yang digunakan adalah Road\r\nDamage Classification and Assessment yang terdiri dari empat kelas kondisi jalan,\r\nyaitu Good, Satisfactory, Poor, dan Very Poor. Tahapan penelitian meliputi akuisisi\r\ndata, pembagian dataset menjadi data latih, validasi, dan uji, pra-pemrosesan citra\r\n(resizing dan normalisasi), augmentasi data untuk meningkatkan variasi citra,\r\npelatihan model menggunakan teknik transfer learning, serta evaluasi kinerja\r\nmodel menggunakan metrik akurasi, presisi, recall, dan F1-score.\r\nHasil penelitian menunjukkan bahwa model MobileNetV2 mampu\r\nmemberikan performa klasifikasi yang baik dengan tingkat akurasi dan F1-score\r\nyang tinggi pada data uji. Penerapan pra-pemrosesan dan augmentasi data terbukti\r\nmeningkatkan kemampuan generalisasi model dalam mengenali berbagai tingkat\r\nkerusakan jalan. Kontribusi penelitian ini diharapkan dapat menjadi referensi bagi\r\npengembangan sistem pemantauan kondisi jalan otomatis berbasis citra, yang dapat\r\ndimanfaatkan oleh instansi pemerintah, peneliti, serta pengembang sistem cerdas\r\ndalam mendukung pemeliharaan infrastruktur jalan yang lebih efektif dan efisien.",
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