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      Penggerombolan Sekolah pada Penerimaan Mahasiswa Baru Jalur SNMPTN di IPB Menggunakan Metode Two-Step Cluster

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      Date
      2021-07-19
      Author
      Dewantari, Ni Kadek Manik
      Syafitri, Utami Dyah
      Alamudi, Aam
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      Abstract
      Penerimaan mahasiswa baru dibuka dalam tiga jalur diantaranya SNMPTN, SBMPTN, dan Seleksi Mandiri. Dalam rangka peningkatan sistem seleksi SNMPTN di IPB, maka dilakukan kajian mengenai kualitas SMA/MA yang mendaftar ke IPB melalui penggerombolan sekolah. Pada umumnya, analisis gerombol tidak dapat menangani data yang berukuran besar dan bertipe campuran, sehingga penggerombolan sekolah ini menggunakan metode Two-Step Cluster dengan dua alternatif, yaitu tanpa penanganan pencilan dan dengan penanganan pencilan 5 persen. Kedua alternatif ini menghasilkan rata – rata nilai koefisien Silhouette masing – masing 0,2 dan 0,3, yang masih dibawah kategori baik (good), namun penggerombolan tanpa penanganan pencilan menghasilkan kriteria gerombol yang lebih rinci dengan terbentuk 4 gerombol optimal. Kriteria dari keempat gerombol ini diantaranya, Gerombol 1 merupakan kategori sekolah Low Commitment, Low Quality, dan Low Consistency, Gerombol 2 dan 3 merupakan kategori sekolah yang memiliki kriteria khusus pada kategori tertentu, serta Gerombol 4 merupakan kategori sekolah High Commitment, High Quality, dan High Consistency.
       
      New student admissions are opened in three pathways including SNMPTN, SBMPTN, and Seleksi Mandiri. In order to improve the SNMPTN selection system at IPB, a study was conducted on the quality of SMA/MA which registered to IPB through school clustering. In general, cluster analysis cannot handle large and mixed-type data, so this school clustering used the Two-Step Cluster method with two alternatives, namely without handling outliers and handling 5 percent outliers. Both of these alternatives produced an average Silhouette coefficient value of 0.2 and 0.3 respectively, which was still under the good category. However, clustering without handling outliers resulted in more detailed cluster criteria with 4 optimal clusters. The criteria for these four clusters include, Cluster 1 is a category of Low Commitment, Low Quality, and Low Consistency schools, Cluster 2 and 3 are categories of schools that have special criteria in certain categories, and Cluster 4 is a category of High Commitment, High Quality, and High Consistency.
       
      URI
      http://repository.ipb.ac.id/handle/123456789/107569
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      • UT - Statistics and Data Sciences [2260]

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      Copyright © 2020 Library of IPB University
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      Contact Us | Send Feedback
      Indonesia DSpace Group 
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