[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"ssr-repo-dspace000006360":3,"ssr-repo-divisions":69,"menu-footer-id":255,"menu-main-id":316},{"id":4,"title":5,"authors":6,"files":8,"advisors":35,"authors_detail":36,"advisors_detail":40,"publisher":41,"publish_place":42,"language":43,"type":44,"type_slug":45,"year":46,"department":47,"study_program":48,"keywords":49,"keywords_detail":55,"abstract":66,"abstract_html":67,"url":68},"repo-dspace000006360","Penerapan Support Vector Regression serta Classification and Regression Tree untuk Prediksi dan Klasifikasi Cuaca dalam Mendukung Perawatan Tanaman Jeruk",[7],"Angelina Balqis Khansa",[9,13,16,19,23,26,29,32],{"chapter":10,"requires_login":11,"label":12,"available":11},"abstrak",false,"Abstrak",{"chapter":14,"requires_login":11,"label":15,"available":11},"bab1","Bab I",{"chapter":17,"requires_login":11,"label":18,"available":11},"bab2","Bab II",{"chapter":20,"requires_login":21,"label":22,"available":11},"bab3",true,"Bab III",{"chapter":24,"requires_login":21,"label":25,"available":11},"bab4","Bab IV",{"chapter":27,"requires_login":21,"label":28,"available":11},"bab5","Bab V",{"chapter":30,"requires_login":21,"label":31,"available":11},"full","Dokumen Lengkap",{"chapter":33,"requires_login":11,"label":34,"available":21},"open","Akses Terbuka",[],[37],{"name":7,"nim":38,"slug":39},"2141762069","angelina-balqis-khansa",[],"Politeknik Negeri Malang","Malang","Indonesia","Skripsi (D4)","skripsi-d4",2025,"D-IV SISTEM INFORMASI BISNIS",null,[50,51,52,53,54],"Klasifikasi Cuaca","Prediksi Cuaca","SVR","CART","WSM",[56,58,60,62,64],{"name":50,"slug":57},"klasifikasi-cuaca",{"name":51,"slug":59},"prediksi-cuaca",{"name":52,"slug":61},"svr",{"name":53,"slug":63},"cart",{"name":54,"slug":65},"wsm","Perubahan cuaca yang tidak menentu menjadi tantangan dalam dunia pertanian karena dapat berdampak terhadap produktivitas tanaman jeruk. Informasi cuaca yang tidak akurat dapat menyebabkan pengambilan keputusan yang tidak tepat dalam pengelolaan lahan dan tanaman. Oleh karena itu, diperlukan sistem yang mampu memprediksi dan mengklasifikasikan kondisi cuaca secara tepat. Penelitian ini menggunakan algoritma Support Vector Regression (SVR) untuk melakukan prediksi parameter cuaca dan Classification and Regression Trees (CART) untuk mengklasifikasikan kondisi cuaca sebagai “Baik” atau “Buruk”. Data yang digunakan terdiri dari suhu rata-rata, kelembaban rata-rata, curah hujan, dan lama penyinaran matahari harian. Setiap parameter cuaca diprediksi menggunakan SVR berdasarkan data historis, yang kemudian hasilnya diklasifikasikan menggunakan CART untuk mendapatkan dua kelas kondisi cuaca. Dengan menggunakan hyperparameter optimal, SVR berhasil memprediksi nilai setiap parameter cuaca dengan RMSE kelembaban sebesar 2.06, suhu sebesar 0.27, curah hujan sebesar 4.53, dan lama penyinaran sebesar 0.68. Algoritma CART juga terbukti mampu mengklasifikasikan kondisi cuaca dengan akurasi sebesar 0.9854, presisi sebesar 0.9855, recall sebesar 0.9911, dan f1-score sebesar 0.9882. Hasil penelitian ini menunjukkan bahwa kombinasi SVR dan CART efektif dalam mendukung sistem prediksi dan klasifikasi cuaca yang akurat untuk pertanian.","\u003Cp>Perubahan cuaca yang tidak menentu menjadi tantangan dalam dunia pertanian karena dapat berdampak terhadap produktivitas tanaman jeruk. Informasi cuaca yang tidak akurat dapat menyebabkan pengambilan keputusan yang tidak tepat dalam pengelolaan lahan dan tanaman. Oleh karena itu, diperlukan sistem yang mampu memprediksi dan mengklasifikasikan kondisi cuaca secara tepat. Penelitian ini menggunakan algoritma Support Vector Regression (SVR) untuk melakukan prediksi parameter cuaca dan Classification and Regression Trees (CART) untuk mengklasifikasikan kondisi cuaca sebagai “Baik” atau “Buruk”. Data yang digunakan terdiri dari suhu rata-rata, kelembaban rata-rata, curah hujan, dan lama penyinaran matahari harian. Setiap parameter cuaca diprediksi menggunakan SVR berdasarkan data historis, yang kemudian hasilnya diklasifikasikan menggunakan CART untuk mendapatkan dua kelas kondisi cuaca. Dengan menggunakan hyperparameter optimal, SVR berhasil memprediksi nilai setiap parameter cuaca dengan RMSE kelembaban sebesar 2.06, suhu sebesar 0.27, curah hujan sebesar 4.53, dan lama penyinaran sebesar 0.68. Algoritma CART juga terbukti mampu mengklasifikasikan kondisi cuaca dengan akurasi sebesar 0.9854, presisi sebesar 0.9855, recall sebesar 0.9911, dan f1-score sebesar 0.9882. 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