Klasifikasi Penerima Beasiswa Kartu Indonesia Pintar (KIP-Kuliah) Menggunakan Metode K-Nearest Neighbor Studi kasus: Institut Teknologi Kalimantan
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Institusion
Institut Teknologi Kalimantan
Author
Karangan, Firesta
Subject
QA75 Electronic computers. Computer science
Datestamp
2023-07-18 07:09:43
Abstract :
The Indonesia Smart Card (KIP-Kuliah) Scholarship program is one of the goverment's efforts to help students who have economic limitations. The K-Nearest Neighbor (KNN) method is used to classify prospective scolarship recipients baesd on relevant variabels. The research variabels used were the father's uccupation, mother's uccupation, parent's combined income, number of dependents, and avarage report card scores. Applying the KNN method will obtain the results of the classification of KIP-Collage scholarship recipients. The data used in this study is divided into training data and testing data.Training data using data from study is divided into training data and testing data. Training data using data from KIP-Collage shcolarship applicants at the ITK in 2020-2021 and this testing using data from KIP-Collage shcolarship applicants at ITK in 2022 in this research, a teqchnique called random oversampling was needed to increase the number of samples i the minority class and produce better accuracy values. The proportion of dataset division in this study uses 70:30, 75:25, and 80:20 with the method of random oversampling to obtain the best model on the testing data with an accuracy of 91% in the proportion of 70:30 using the best k, namely k=1
The Indonesia Smart Card (KIP-Kuliah) Scholarship program is one of the goverment's efforts to help students who have economic limitations. The K-Nearest Neighbor (KNN) method is used to classify prospective scolarship recipients baesd on relevant variabels. The research variabels used were the father's uccupation, mother's uccupation, parent's combined income, number of dependents, and avarage report card scores. Applying the KNN method will obtain the results of the classification of KIP-Collage scholarship recipients. The data used in this study is divided into training data and testing data.Training data using data from study is divided into training data and testing data. Training data using data from KIP-Collage shcolarship applicants at the ITK in 2020-2021 and this testing using data from KIP-Collage shcolarship applicants at ITK in 2022 in this research, a teqchnique called random oversampling was needed to increase the number of samples i the minority class and produce better accuracy values. The proportion of dataset division in this study uses 70:30, 75:25, and 80:20 with the method of random oversampling to obtain the best model on the testing data with an accuracy of 91% in the proportion of 70:30 using the best k, namely k=1
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02191013_cover.pdf
02191013_statement_of_authenticity.pdf
02191013_notations.pdf
02191013_chapter_1.pdf
02191013_chapter_2.pdf
02191013_chapter_3.pdf
02191013_chapter_4.pdf
02191013_conclusions.pdf
02191013_bibliography.pdf
02191013_enclosure.pdf
02191013_presentation.pdf
02191013_form020.pdf
02191013_publishing_agreement.pdf
02191013_approval_sheet.pdf
02191013_preface.pdf
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02191013_tables.pdf
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