Analisis Dinamika Ekonomi Sumatera Barat dan Implikasinya terhadap Perencanaan Karier menggunakan Feed-Forward Neural Networks

Mutia Yollanda, Ghea Weisha, Lidya Pratiwi, Mawanda Almuhayar, Dahlia Misrika, Ade Herdian Putra

Abstract


Penelitian ini menganalisis dinamika ekonomi di Sumatera Barat serta implikasinya terhadap perencanaan karier, dengan memanfaatkan model Feed-Forward Neural Networks (FFNN). Model dikembangkan untuk memprediksi Tingkat Pengangguran Terbuka (TPT) berdasarkan data tahunan 2010–2024, menggunakan lima variabel ekonomi: Produk Domestik Regional Bruto (PDRB), inflasi, Upah Minimum Provinsi (UMP), Tingkat Partisipasi Angkatan Kerja (TPAK), dan lama sekolah. Arsitektur jaringan terdiri dari dua lapisan tersembunyi, dengan hasil prediksi menunjukkan nilai Sum of Squared Errors (SSE) sebesar 0.145 pada data pelatihan dan 0.023 pada data pengujian, yang mencerminkan tingkat akurasi tinggi. Hasil estimasi menunjukkan bahwa PDRB, UMP, dan lama sekolah memiliki pengaruh negatif signifikan terhadap TPT, sedangkan pengaruh inflasi dan TPAK relatif kecil. Temuan ini memberikan dasar empiris untuk perumusan kebijakan ketenagakerjaan yang lebih adaptif dan tepat sasaran. Selain itu, hasil model ini dapat dimanfaatkan dalam layanan konseling karier berbasis data guna membantu masyarakat memahami tren pasar kerja serta merancang jalur karier yang sesuai dengan dinamika ekonomi daerah. Pendekatan FFNN terbukti efektif dalam mendukung perencanaan karier yang lebih responsif, sekaligus memperkuat strategi pembangunan sumber daya manusia di Sumatera Barat.


Keywords


Tingkat Pengangguran Terbuka; Produk Domestik Regional Bruto; Upah Minimum Provinsi; Lama Sekolah; Feed-Forward Neural Networks

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DOI: https://doi.org/10.15548/jostech.v5i2.11387

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