Optimasi Model Prediksi Kesuksesan Startup Menggunakan StandartScaler Tranform
DOI:
https://doi.org/10.29407/stains.v3i1.4340Keywords:
Kesuksesan Startup, Deep Neural Networks, StandartScaler Transform, Optimasi, PrediksiAbstract
Pertumbuhan pesat startup di era teknologi modern menimbulkan tantangan kompleks dalam memprediksi kesuksesan suatu startup. Berdasarkan data yang diperoleh, 90 dari 100 startup gagal mencapai tingkat pendanaan yang diinginkan. Keberhasilan sebuah startup tidak hanya mencerminkan prestasi bisnis individu tetapi juga memiliki dampak yang luas pada dinamika ekonomi global. Oleh karena itu, perlu dikembangkan model prediksi yang handal sebagai dasar pengambilan keputusan yang efektif dalam pengembangan startup. Untuk mengatasi masalah ini, penelitian ini menggunakan integrasi Deep Neural Networks dengan StandartScaler Transform untuk mengoptimalkan model prediksi kesuksesan startup. StandartScaler Transform digunakan untuk menormalkan distribusi data sebelum diterapkan pada Deep Neural Networks. Hasil eksperimen pada model prediksi kesuksesan startup menunjukkan peningkatan akurasi sebesar 35,25% pada tahap training, dan peningkatan tambahan sebesar 36,95% pada tahap testing. Model prediksi kesuksesan startup yang dibangun berhasil mengatasi masalah overfitting/underfitting, sehingga menjadikannya sebagai dasar untuk pengembangan aplikasi prediksi kesuksesan startup.
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