Skripsi
KLASIFIKASI KEJADIAN HUJAN KOTA PRABUMULIH MENGGUNAKAN METODE DECISION TREE TANPA DAN DENGAN SYNTHETIC MINORITY OVER-SAMPLING TECHNIQUE (SMOTE)
Classification of rainfall events in Prabumulih City is important because it affects the agricultural and plantation sectors, especially oil palm and rice. Ecologically, oil palm and rice require a lot of water in the growth process and will thrive in an environment with sufficient soil moisture. In addition to its benefits to the agricultural and plantation sectors, the classification of rainfall events is also useful for preparing solutions to the impact of extreme weather that has the potential to cause disasters such as landslides and floods that result in disrupted community activities. This research uses secondary data obtained from Visual Grossing which has 17 variables with 2556 data. In this data there is class imbalance, the class balancing technique used is Synthetic Minority Oversampling Technique (SMOTE). The method used for the classification of rainfall events is the Decision Tree C4.5 method. The results of this study obtained a value on Decision Tree C4.5 without SMOTE with accuracy of 75.62%, precision of 74.07% and recall of 87.07%. While using SMOTE in the Decision Tree method results in an accuracy value of 74.38%, precision of 70.84% and recall of 92.44%. Keywords : Rainfall Events, Decision Tree, SMOTE, Prabumulih City
| Inventory Code | Barcode | Call Number | Location | Status |
|---|---|---|---|---|
| 2407005207 | T145959 | T1459592024 | Central Library (References) | Available but not for loan - Not for Loan |
| Title | Edition | Language |
|---|---|---|
| KLASIFIKASI PENYAKIT DIABETES MENGGUNAKAN METODE DECISION TREE DAN RANDOM FOREST | id | |
| KLASIFIKASI BAHAGIA BERDASARKAN FASILITAS UMUM MENGGUNAKAN METODE DECISION TREE | id |