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All Journal Notary Law Journal PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science
Anugrah, Muhammad Ridho
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice

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Dinamika Hukum dalam Perlindungan Pekerja Anak Malik, Muhamad Maulana; Sativa, Charlet Oriza; Handayani, Mutia; Anugrah, Muhammad Ridho
Notary Law Journal Vol. 3 No. 1 (2024): January-Maret
Publisher : Program Studi Kenotariatan Fakultas Hukum Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32801/nolaj.v3i1.54

Abstract

Eksploitasi pekerja anak masih terjadi di Indonesia. Dinamika peraturan mengenai pekerja anak dinilai belum secara penuh melindungi hak-hak anak. Oleh karena itu tulisan ini bertujuan untuk mengkaji sejarah dan perkembangan dasar hukum serta relevansinya pada perlindungan pekerja anak di Indonesia. Hasil penelitian ini menunjukkan bahwa terjadinya pekerja anak dipengaruhi oleh berbagai faktor sosial seperti kemiskinan dan ekonomi. Perlindungan terhadap pekerja anak telah diatur dalam rumusan undang-undang dan Konvensi Internasional yang diratifikasi oleh Indonesia. Berbagai upaya telah dilakukan untuk mengatasi masalah pekerja anak, namun upaya pemerintah belum maksimal seperti yang diharapkan
Application of Recurrent Neural Network Bi-Long Short-Term Memory, Gated Recurrent Unit and Bi-Gated Recurrent Unit for Forecasting Rupiah Against Dollar (USD) Exchange Rate Fayyad, Muhammad Fauzi; Kurniawan, Viki; Anugrah, Muhammad Ridho; Estanto, Baihaqi Hilmi; Bilal, Tasnim
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 2 No. 1: PREDATECS July 2024
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v2i1.1094

Abstract

Foreign exchange rates have a crucial role in a country's economic development, influencing long-term investment decisions. This research aims to forecast the exchange rate of Rupiah to the United States Dollar (USD) by using deep learning models of Recurrent Neural Network (RNN) architecture, especially Bi-Long Short-Term Memory (Bi-LSTM), Gated Recurrent Unit (GRU), and Bi-Gated Recurrent Unit (Bi-GRU). Historical daily exchange rate data from January 1, 2013 to November 3, 2023, obtained from Yahoo Finance, was used as the dataset. The model training and evaluation process was performed based on various parameters such as optimizer, batch size, and time step. The best model was identified by minimizing the Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Among the models tested, the GRU model with Nadam optimizer, batch size 16, and timestep 30 showed the best performance, with MSE 3741.6999, RMSE 61.1694, MAE 45.6246, and MAPE 0.3054%. The forecast results indicate a strengthening trend of the Rupiah exchange rate against the USD in the next 30 days, which has the potential to be taken into consideration in making investment decisions and shows promising economic growth prospects for Indonesia.
Co-Authors Bilal, Tasnim Estanto, Baihaqi Hilmi Fayyad, Muhammad Fauzi Handayani, Mutia Kurniawan, Viki Malik, Muhamad Maulana Sativa, Charlet Oriza