Pengolahan data kualitatif dan data kuantitatif dengan menggunakan decision tree = Processing qualitative data and quantitative data using decision tree

Riandy, Daniel (2020) Pengolahan data kualitatif dan data kuantitatif dengan menggunakan decision tree = Processing qualitative data and quantitative data using decision tree. Bachelor thesis, Universitas Pelita Harapan.

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Abstract

Skripsi ini bertujuan untuk melakukan pengolahan data kualitatif dan kuantitatif menggunakan decision tree. Data yang digunakan pada data kualitatif adalah data dari wine vinho verde, data ini memiliki 11 variabel, yang kemudian dicari pengaruh dari variabel-variabel tersebut terhadap kualitas dari wine itu sendiri. Sedangkan, untuk data kuantitatif, digunakan data klaim asuransi yang memiliki 6 variabel, dari variabel-variabel yang ada dilihat pengaruhnya terhadap besar klaim yang dilakukan. Pada masing-masing data dilakukan pembentukan decision tree untuk melihat variabel-variabel yang mempengaruhi dan hasil akhir dari tiap variabel tersebut. Kemudian dilihat galat dari masing-masing data untuk melihat akurasi dari modelnya. Pada data kaulitatif uji galat yang digunakan adalah confuion matrix dan pada data Kuantitatif uji galat yang digunakan adalah r-squared. Confusion matrix dilakukan sebanyak sepuluh kali, karena data training dan data testing dibagi secara acak dengan bantuan r-studio, sehingga uji galat dilakukan sebanyak sepuluh kali untuk kemudian dilihat rata-rata dari hasil uji galat tersebut. / This thesis aims to do qualitative and quantitative data processing using decision tree. The data used in qualitative data is data from wine vinho verde, this data has 11 variables, which then look for the influence of these variables on the quality of wine itself. Meanwhile, for quantitative data, insurance claim data is used which has 6 variables, from the variables that are seen the effect on the amount of claims made. In each of the data, decision tree is formed to see the variables that influence and the final result of each variable. Then the error is seen from each data to see the accuracy of the model. For the error test data caulitative used is confuion matrix and in the quantitative data the error test used is r-squared. Confusion matrix is performed ten times, because the training and testing data are randomly shared with the help of r-studio, so that the error test is carried out ten times to then be seen on average average of the error test results.

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