Penerapan Information Gain pada K-Nearest Neighbor untuk Klasifikasi Tingkat Kognitif Soal pada Taksonomi Bloom

Penerapan Information Gain pada K-Nearest Neighbor untuk Klasifikasi Tingkat Kognitif Soal pada Taksonomi Bloom

• By Tyas Setiyorini;
• Jurnal Informatika
• 221
• 57-62

Taksonomi Bloom merupakan sistem klasifikasi yang digunakan untuk mendefinisikan dan membedakan tingkat kognisi (berpikir, belajar, dan memahami) manusia yang berbeda-beda. Tujuan awal dalam pembuatan taksonomi adalah memfokuskan pada tiga domain utama dari pembelajaran, yaitu kognitif, afektif, dan psikomotorik. Saat ini, kalangan akademisi mengidentifikasi tingkat kognitif Bloom sebuah pertanyaan secara manual. Namun, hanya sedikit akademisi yang dapat mengidentifikasi tingkat kognitif dengan benar, sehingga sebagian besar melakukan kesalahan mengkategorisasikan pertanyaan. K-Nearest Neighbor (KNN) merupakan metode sederhana namun efektif untuk kategorisasi tingkat kognitif soal pada taksonomi Bloom, namun KNN memiliki dimensi vektor yang besar. Untuk menyelesaikan masalah tersebut diperlukan metode Information Gain (IG) untuk mengurangi dimensi vektor teks. Beberapa eksperimen dilakukan untuk mendapatkan arsitektur yang optimal dan menghasilkan klasifikasi yang akurat. Hasil dari 10 eksperimen pada dataset Question Bank dengan KNN didapatkan akurasi terbesar adalah 59,97% dan kappa terbesar adalah 0,496. Kemudian pada KNN+IG didapatkan akurasi terbesar adalah 66,18% dan kappa terbesar adalah 0,574. Maka dapat disimpulkan klasifikasi tingkat kognitif soal pada taksonomi Bloom dengan menggunakan metode KNN+IG lebih akurat dibanding dengan metode KNN saja.   

 

• Katakunci Klasifikasi, Taksonomi Bloom, K-Nearest Neighboor, Information Gain
• URL https://ejournal.stmikantarbangsa.ac.id/v1/index.php/jsi/article/view/224/pdf
• Penerbit ,

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