PENERAPAN GINI INDEX DAN K-NEAREST NEIGHBOR UNTUK KLASIFIKASI TINGKAT KOGNITIF SOAL PADA TAKSONOMI BLOOM

Authors

  • Tyas Setiyorini Teknik Informatika STMIK Nusa Mandiri Jakarta
  • Rizky Tri Asmono Teknik Informatika STMIK Swadharma

Keywords:

K-Nearest Neighbor, Taksonomi Bloom, Gini Index, Clasification

Abstract

Sebagai pedoman dalam merancang ujian yang layak, yang terdiri dari soal-soal yang memiliki berbagai tingkatan secara kognitif, Taksonomi Bloom telah diterapkan secara luas. Saat ini, kalangan pendidik mengidentifikasi tingkat kognitif soal pada Taksonomi Bloom masih menggunakan cara manual. Hanya sedikit pendidik yang dapat mengidentifikasi tingkat kognitif dengan benar, sebagian besar melakukan kesalahan dalam mengklasifikasikan soal-soal. K-Nearest Neighbor (KNN) adalah metode yang efektif untuk klasifikasi tingkat kognitif soal pada Taksonomi Bloom, tetapi KNN memiliki kelemahan yaitu kompleksitas komputasi kemiripan datanya besar apabila dimensi fitur datanya tinggi. Untuk menyelesaikan kelemahan tersebut diperlukan metode Gini Index untuk mengurangi dimensi fitur yang tinggi. Beberapa percobaan dilakukan untuk memperoleh arsitektur yang terbaik dan menghasilkan klasifikasi yang akurat. Hasil dari 10 percobaan pada dataset Question Bank dengan KNN diperoleh akurasi tertinggi yaitu 59,97% dan kappa tertinggi yaitu 0,496. Kemudian pada KNN+Gini Index diperoleh akurasi tertinggi yaitu 66,18% dan kappa tertinggi yaitu 0,574. Berdasarkan hasil tersebut maka dapat disimpulkan bahwa Gini Index mampu mengurangi dimensi fitur yang tinggi, sehingga meningkatkan kinerja KNN dan meningkatkan tingkat akurasi klasifikasi tingkat kognitif soal pada Taksonomi Bloom.

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Published

2017-09-15

How to Cite

Setiyorini, T., & Asmono, R. T. (2017). PENERAPAN GINI INDEX DAN K-NEAREST NEIGHBOR UNTUK KLASIFIKASI TINGKAT KOGNITIF SOAL PADA TAKSONOMI BLOOM. Jurnal Pilar Nusa Mandiri, 13(2), 209–216. Retrieved from https://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/239

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Vol. 13 No. 2 (2017): Pilar Nusa Mandiri : Journal of Computing and Information System Periode September 2017

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