RISIKO MAGANG MAHASISWA DENGAN PENDEKATAN ALGORITMA CONTENT-BASED FILTERING DAN SUPPORT VECTOR MACHINE
DOI:
https://doi.org/10.36618/competitive.v20i1.4195Keywords:
Risiko, Content-Based Filtering, Support Vector Machine, MagangAbstract
Penelitian ini menggunakan pendekatan kuantitatif untuk mengkaji penggunaan algoritma Content-Based Filtering (CBF) dan Support Vector Machine (SVM) dalam manajemen risiko magang mahasiswa. Metode penelitian yang digunakan adalah Crisp-DM (Cross-Industry Standard Process for Data Mining) yang membantu peneliti mengorganisasikan dan menganalisis data dengan langkah-langkah yang terstruktur. Dalam penelitian ini, peneliti mengumpulkan data dengan menggunakan kuesioner yang dirancang khusus yang bertujuan untuk memperoleh informasi mengenai risiko-risiko yang mungkin terjadi selama mahasiswa magang di perusahaan, faktor-faktor yang mempengaruhi keberhasilannya, dan bagaimana algoritma CBF dan SVM dapat digunakan untuk menganalisis risiko. Dengan menggunakan pendekatan kuantitatif dan Crisp-DM, penelitian ini bertujuan untuk memberikan informasi yang objektif dan terukur tentang penggunaan algoritma CBF dan SVM dalam manajemen risiko magang mahasiswa. Diharapkan penelitian ini dapat memberikan pemahaman yang lebih baik tentang bagaimana algoritma CBF dan SVM dapat membantu mengelola risiko dalam magang mahasiswa. Hasil penelitian ini juga diharapkan dapat memberikan saran praktis kepada organisasi yang lebih baik.
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