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      "abstract": "Pertumbuhan industri pakan hewan di Yogyakarta yang pesat menimbulkan \r\ntantangan bagi pelaku usaha petshop, seperti Seana Petshop & Petcare, dalam\r\nmengelola stok produk akibat fluktuasi permintaan yang dinamis. Pengelolaan yang\r\nselama ini masih mengandalkan intuisi pemilik berisiko menyebabkan\r\nketidakseimbangan stok yang berdampak pada inefisiensi operasional dan potensi\r\nkerugian finansial. Penelitian ini bertujuan untuk membangun model prediksi\r\npenjualan yang akurat dengan membandingkan algoritma Random Forest dan\r\nLinear Regression. Tahapan penelitian diawali dengan pengumpulan 11.883 data\r\ntransaksi, dilanjutkan dengan preprocessing data yang meliputi seleksi fitur,\r\npenanganan missing values, serta Standard Scaling. Untuk meningkatkan performa\r\nmodel, dilakukan optimasi menggunakan metode Hyperparameter Tuning dengan\r\nGrid Search pada algoritma Random Forest. Hasil penelitian menunjukkan bahwa\r\nalgoritma Random Forest memiliki performa yang jauh lebih unggul dibandingkan\r\nLinear Regression. Hal ini dibuktikan dengan perolehan nilai koefisien determinasi\r\n(R\r\n2\r\n) sebesar 0,9917 dan Mean Absolute Error (MAE) sebesar Rp164,19, sementara \r\nLinear Regression hanya mencapai R\r\n2\r\n sebesar 0,8385 dengan MAE Rp3.635,34. \r\nTingginya akurasi Random Forest menunjukkan kemampuannya dalam\r\nmenangkap pola hubungan non-linear antar variabel penjualan secara stabil. Hasil\r\npenelitian ini dapat dimanfaatkan oleh pengelola petshop sebagai dasar\r\npengambilan keputusan strategis dalam perencanaan stok dan manajemen inventori\r\nyang lebih efisien. Penelitian lebih lanjut direkomendasikan untuk menambahkan\r\nvariabel eksternal seperti data promosi atau tren musiman guna meningkatkan\r\ncakupan prediksi model.",
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      "dir": "disk0\/00\/03\/21\/27",
      "title": "ANALISIS PENJUALAN PRODUK PAKAN HEWAN DI\r\nPETSHOP MENGGUNAKAN RANDOM FOREST DAN \r\nLINEAR REGRESSION DENGAN HYPERPARAMETER\r\nTUNING",
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