AbstrakUpah Minimum Kota (UMK) adalah sebuah standardisasi upah atau gaji karyawan atau pegawai untuk diterapkan diperusahaan baik itu BUMN, BUMS, maupun perusahaan lain yang berskala besar. Faktor yang mempengaruhi UMK sangat banyak dan beragam salah satunya adalah rata-rata inflasi pengeluaran dimana terdapat 8 kategori yang dipakai. Tulisan ini memaparkan penggunaan Backpropagation Neural Network (BPNN) untuk memprediksi besarnya UMK. Pada tahap uji coba data dibagi menjadi dua bagian yaitu data latih dan data uji, dimana data latih digunakan untuk mencari jumlah iterasi, jumlah hidden layer, dan nilai learning rate yang optimal. Pengujian data latih memberikan hasil yakni jumlah iterasi optimal diperoleh pada saat iterasi 80, sedangkan untuk jumlah hidden layer yang optimal adalah sebanyak satu hidden layer dan untuk nilai learning rate optimal yakni pada saat bernilai 0.8. Semua variabel yang diperoleh dikatakan optimal karena memiliki rata-rata MSE paling kecil dibandingkan dengan data lainnya. Hasil yang diperoleh saat data uji dengan menggunakan iterasi, jumlah hidden layer, dan nilai learning rate yang optimal didapatkan hasil MSE sebesar 0.07280534710552478.
Kata kunci: UMK, Inflasi, Backpropagation Neural Network (BPNN), MSE
Abstract
State Minimum Wage (SMW) is a standardization of wages or salary of the employee applied in the company
Signboards are important location landmarks that provide services to a local community. Nondisabled people can easily understand the meaning of a signboard based on its special shape; however, visually impaired people who need an assistive system to guide them to destinations or to help them understand their surroundings cannot. Currently, designing accurate assistive systems remain a challenge. Computer vision struggles to recognize signboards due to the diverse designs that combine text and images. Moreover, there is a lack of datasets to train the best model and reach good results. In this paper, we propose a novel framework that can automatically detect and recognize signboard logos. In addition, we utilize Google Street View to collect signboard images from Taiwan's streets. The proposed framework consists of a domain adaptation that not only reduces the loss function between source-target datasets, but also represents important source features adopted by the target dataset. In our model, we add nonlocal blocks and attention mechanisms called deep attention networks to achieve the best final result. We perform extensive experiments on both our dataset and public datasets to demonstrate the superior performance and effectiveness of our proposed method. The experimental results show that our proposed method outperforms state-of-the-art methods across all evaluation metrics.
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