Ekstraksi Fitur Produktivitas Dinamis berdasarkan Topik Artikel Ilmiah untuk Klasterisasi Peneliti

Authors

  • Addien Haniefardy Institut Teknologi Sepuluh Nopember
  • Diana Purwitasari Institut Teknologi Sepuluh Nopember
  • Chastine Fatichah Institut Teknologi Sepuluh Nopember

DOI:

https://doi.org/10.33633/tc.v20i2.4512

Keywords:

Klasterisasi peneliti, Produktivitas, Topik Artikel Ilmiah, Publikasi, Sitasi

Abstract

Pengelompokkan peneliti seringkali menggunakan informasi tekstual yang terdapat pada artikel ilmiah peneliti, contohnya judul, abstrak, dan kata kunci sehingga menghasilkan kelompok peneliti dengan kemiripan informasi tekstual pada artikel ilmiah mereka. Pengelompokkan peneliti juga seringkali menggunakan jumlah publikasi dan sitasi sehingga menghasilkan kelompok peneliti yang memiliki jumlah publikasi dan sitasi yang cenderung sama. Berdasarkaan kedua metode di atas, penelitian ini mencoba untuk menganalisis penggunaan topik artikel ilmiah pada proses ekstraksi fitur produktivitas. Fitur ini merupakan fitur yang didapatkan melalui penghitungan kinerja peneliti berdasarkan jumlah publikasi dan sitasi. Hasil ekstraksi fitur akan digunakan untuk klasterisasi peneliti menggunakan metode K-Means++. Sebelum data peneliti diklasterisasi, terlebih dahulu data peneliti dianalisis untuk menghilangkan kemungkinan adanya outlier. Evaluasi hasil klaster dilakukan dengan mempertimbangkan nilai Sum Squared Error dan Silhouette. Hasilnya, klaster optimal didapatkan dengan nilai K sama dengan 8 dan nilai silhouette sama dengan 0.15396. Kemudian, hasil klaster dianalisis untuk dapat memberikan label terhadap masing-masing klaster dengan mempertimbangkan topik artikel ilmiah, jumlah publikasi dan jumlah sitasi.

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Published

2021-05-19

Issue

Vol. 20 No. 2 (2021): Mei 2021

Section

Articles

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