Analisis Trend Topik Penelitian pada Web Of Science dan SINTA untuk Penentuan Tema Tugas Akhir Mahasiswa AMIK Indonesia Banda Aceh

Authors

  • Bahruni Bahruni AMIK Indonesia
  • Fathurrahmad Fathurrahmad AMIK Indonesia

DOI:

https://doi.org/10.33020/saintekom.v10i1.91

Keywords:

analysis, trend, topik penelitian, web of science, sinta

Abstract

Penelitian ini mencoba melakukan penambangan dengan menggunakan teknologi web untuk mengumpulkan data informasi yang berasal dari Web of Science dan SINTA yang dikumpulkan. Metodologi Cross Industry Standard Process for Data Mining (CRISP–DM) digunakan sebagai standard proses data mining sekaligus sebagai metode penelitian. Peneliti mengumpulkan data melalui daftar jurnal Web of Science dan SINTA. Untuk melacak trend topik penelitian, peneliti memilih rentang waktu dari tahun 2018 sampai dengan 2019 dan mengekspor data dari Web of Science Core Collection pada April 2019. Ada 38.162 publikasi yang berhasil diambil di Web-Science-defined kategori Ilmu Komputer dan Sistem Informasi dan 230 diambil dari website SINTA. Tetapi, penulis hanya mengambil 20 Jurnal dengan H-Index Tertinggi di Web of Science Core Collection. Sedangkan pada SINTA, penulis juga mengambil 20 Jurnal dengan rangking SINTA 1 dan 2. penelitian ini menyimpulkan topik penelitian dalam jurnal Web of Science dan dikaitkan dengan dengan tren topik penelitian dan yang muncul terbanyak adalah learning, network, analysis, system, control, data, image, optimization, systems, dan neural. Adapun untuk klasifikasi menggunakan model Naive Bayes, Generalized Linear Model, Logistic Regression, Fast Large Margin, Deep Learning, Decision Tree, Random Forest, Gradient Boosted Trees, dan Support Vector Machine. Berdasarkan hasil akurasi, model Generalized Linear Model dan Decision Tree memiliki akurasi sebesar 94.3%, sedangkan Gradient Boosted Trees memiliki  persentase akurasi sebesar 93.8%. Naive Bayes menunjukkan tingkat akurasi sebesar 91.4%, diikuti dengan model Fast Large Margin, Deep Learning, Random Forest, dan Support Vector Machine memiliki akurasi sebesar 91.4%. Nilai dengan akurasi terendah menggunakan model Logistic Regression sebesar 65.2%. Hal ini menunjukan bahwa tingkat akurasi tertinggi yaitu dengan menggunakan model Generalized Linear Model dan Decision Tree sehingga hasilnya dapat memprediksi cukup akurat.

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Published

09-03-2020

How to Cite

Bahruni, Bahruni, and Fathurrahmad Fathurrahmad. 2020. “Analisis Trend Topik Penelitian Pada Web Of Science Dan SINTA Untuk Penentuan Tema Tugas Akhir Mahasiswa AMIK Indonesia Banda Aceh”. Jurnal Saintekom : Sains, Teknologi, Komputer Dan Manajemen 10 (1):13-25. https://doi.org/10.33020/saintekom.v10i1.91.

Issue

Vol. 10 No. 1 (2020): March 2020

Section

Articles

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