Optimisasi model K-nearest neighbors, Ssupport vector regression, decision tree & random forest: kasus pemilihan konsentrasi program studi = Model optimization of K-nearest neighbors, support vector regression, decision tree & random forest: case study of academic concentration selection

Bachtiar, Nadya Felim (2019) Optimisasi model K-nearest neighbors, Ssupport vector regression, decision tree & random forest: kasus pemilihan konsentrasi program studi = Model optimization of K-nearest neighbors, support vector regression, decision tree & random forest: case study of academic concentration selection. Bachelor thesis, Universitas Pelita Harapan.

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Abstract

Choosing an academic concentration is not an easy task for students due to the lack of information. Therefore, research about predicting the success rate of a student in an academic concentration based on their grades is necessary. This research uses the data of graduated Informatics students’ grades from the class of 2013-2015. With four different algorithms, K-Nearest Neighbors, Support Vector Regression, Decision Tree, and Random Forest, the data is processed to create several prediction models based on each algorithm. To overcome the limited amount of data, the research use hyperparameter tuning for all the algorithms. This research shows that the prediction models of Support Vector Regression and Random Forest algorithms are proven to predict GPA for each academic concentration using student’s grades from the 1st until the 4th semester with a low RMSE score of 0.106 and 0.052 respectively. = Pemilihan konsentrasi merupakan keputusan yang sulit bagi mahasiswa karena kurangnya informasi yang ada. Agar mahasiswa mengetahui kemungkinan keberhasilan mereka dalam suatu konsentrasi, maka diperlukan penelitian untuk membuat prediksi mengenai nilai mahasiswa dalam tiap konsentrasi. Data yang dipakai berupa nilai akademik mahasiswa Universitas Pelita Harapan program studi Informatika angkatan 2013-2015 yang sudah lulus. Data ini kemudian diolah untuk membuat beberapa model prediksi berdasarkan empat algoritma yang berbeda, yaitu K-Nearest Neighbors, Support Vector Regression, Decision Tree dan Random Forest. Untuk mengatasi terbatasnya data, dilakukan hyperparameter tuning dari setiap algoritma yang digunakan. Dari hasil penelitian, dapat dibuktikan bahwa algoritma Support Vector Regression dan Random Forest mampu menghasilkan prediksi nilai IPK mata kuliah konsentrasi berdasarkan nilai semester satu hingga semester empat dengan nilai RMSE yang rendah, yaitu 0.106 dan 0.052.

Item Type: Thesis (Bachelor)
Creators:
CreatorsNIMEmail
Bachtiar, Nadya FelimNIM00000019602nadyaf.bachtiar@gmail.com
Contributors:
ContributionContributorsNIDN/NIDKEmail
Thesis advisorYugopuspito, PujiantoNIDN0324086701UNSPECIFIED
Thesis advisorLukas, SamuelNIDN0331076001UNSPECIFIED
Additional Information: SK 82-16 BAC o
Uncontrolled Keywords: academic concentration; Decision Tree; K-Nearest Neighbors; optimization; prediction model; Random Forest; regression; Support Vector Regression;
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: University Subject > Current > Faculty/School - UPH Karawaci > School of Information Science and Technology > Informatics
Current > Faculty/School - UPH Karawaci > School of Information Science and Technology > Informatics
Depositing User: Users 2770 not found.
Date Deposited: 18 Nov 2019 03:10
Last Modified: 16 Aug 2021 06:06
URI: http://repository.uph.edu/id/eprint/5603

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