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      "title": "ANALISIS PERBANDINGAN OPTIMASI HYPERPARAMETER\r\nGRID SEARCH DAN RANDOM SEARCH PADA ALGORITMA XGBOOST\r\nUNTUK KLASIFIKASI PRAKIRAAN CUACA",
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      "abstract": "Penelitian ini bertujuan untuk menganalisis dan membandingkan pengaruh optimasi\r\nhyperparameter menggunakan metode Grid Search dan Random Search pada algoritma\r\nExtreme Gradient Boosting (XGBoost) untuk klasifikasi prakiraan cuaca. Dataset yang\r\ndigunakan berasal dari Global Weather Repository yang diperoleh dari Kaggle, kemudian\r\nmelalui tahapan prapemrosesan data yang meliputi pemilihan fitur, pembersihan data,\r\ntransformasi data, pemisahan data latih dan data uji, serta penyeimbangan data menggunakan\r\nSynthetic Minority Oversampling Technique (SMOTE). Evaluasi model dilakukan\r\nmenggunakan metrik accuracy, precision, recall, dan F1-score, sedangkan proses optimasi\r\nhyperparameter menggunakan validasi silang (cross-validation) sebanyak 3 lipatan. Hasil\r\npenelitian menunjukkan bahwa model XGBoost dasar memperoleh accuracy sebesar 0.8210,\r\nsedangkan model hasil Grid Search memperoleh accuracy sebesar 0.8309 dan model hasil\r\nRandom Search memperoleh accuracy sebesar 0.8358. Pada proses validasi silang, Grid Search\r\nmenghasilkan CV Score tertinggi sebesar 0.9116, sementara Random Search memperoleh CV\r\nScore sebesar 0.9091. Namun, hasil pengujian pada data uji menunjukkan bahwa Random\r\nSearch menghasilkan performa terbaik berdasarkan accuracy, precision, recall, dan F1-score.\r\nHasil penelitian menunjukkan bahwa optimasi hyperparameter mampu meningkatkan\r\nperforma XGBoost dalam klasifikasi kondisi cuaca, serta menunjukkan bahwa nilai validasi\r\nsilang yang lebih tinggi tidak selalu menghasilkan performa terbaik pada data pengujian.\r\nDengan demikian, Random Search menjadi metode optimasi yang memberikan performa\r\nterbaik pada dataset dan ruang parameter yang digunakan dalam penelitian ini.",
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