Komparasi Algoritma Pengklasifikasian Terhadap Dokumen Jurnal Ilmiah
DOI:
https://doi.org/10.26418/jari.v2i1.53133Keywords:
Klasifikasi, Jurnal, Subjek, Pembelajaran MesinAbstract
Dengan kemajuan teknologi yang sangat pesat, membuat banyak sekali jurnal yang dihasilkan dengan berbagai macam subjek mulai dari subjek komputasi, sistem informasi, dan jaringan. Penerapan teknologi informasi dalam dunia pendidikan juga dapat menghasilkan banyak dokumen jurnal yang terdiri dari beberapa subjek yang berpengaruh pada proses pembelajaran. Secara otomatis memerlukan program atau aplikasi yang dapat membantu membedakan setiap jurnal yang kita inginkan. Pada penelitian ini, dua algoritma klasifikasi yaitu Support Vector Machine, dan Naive Bayes di bandingkan kemampuannnya dalam pengklasifikasiaan terhadap jurnal ilmiah untuk menentukan subjek jurnal tersebut. Pengujian yang dilakukan adalah pengujian, precession, recall, f1-score dan accuracy. Dari data yang diperoleh, dapat dilihat bahwa dari rata-rata nilai precision, algoritma Support Vector Machine mendapat nilai tertinggi yaitu 0,6640, sedangkan Naive Bayes terendah pada 0,5360. Untuk rata-rata nilai recall, Support Vector Machine mendapat nilai tertinggi dengan skor 0.5717 sedangkan Naive Bayes terendah dengan skor 0,4414. Untuk F1-Score, rata-rata nilai tertinggi terdapat pada algoritma Support Vector Machine, yaitu 0,5768, sementara rata-rata nilai terendah terdapat pada algoritma Naive Bayes dengan nilai 0,4124. Pada rata-rata nilai accuracy, nilai tertinggi berada pada algoritma Support Vector Machine dengan nilai 0,7468 dan nilai terendah berada pada algoritma Naive Bayes dengan nilai 0,6575.
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