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All Journal Teknika Vokasi UNESA Bulletin of Engineering, Technology and Applied Science
Anik Vega Vitianingsih
Informatics Department, Universitas Dr. Soetomo, Surabaya, Indonesia
Computer Science & IT Electrical & Electronics Engineering Engineering Mechanical Engineering Transportation

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Forecasting Model of Export and Import Value of Oil and Gas Using Gated Recurrent Unit Method Ilham Adji Saputra; Anik Vega Vitianingsih; Yudi Kristyawan; Anastasia Lidya Maukar; Jack Febrian Rusdi
Teknika Vol 13 No 2 (2024): Juli 2024
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v13i2.861

Abstract

Indonesia’s natural resources are abundant, including oil and gas. It is one of the countries active in international trade, including exports and imports. Oil and gas exports are a significant source of income for the country, encouraging economic growth. Oil and gas imports are very important to meet domestic energy needs, which continue to increase in demand. Increasing oil and gas imports can increase the trade balance, which can affect the country’s economic stability if the value of imports exceeds the value of exports. Forecasting is a solution to overcome these problems by forecasting the value of oil and gas exports and imports. The gated recurrent unit (GRU) method is used for forecasting in this study because it has a simple computation and fairly high accuracy. The dataset used is monthly time series data from 1993 to 2023 from the website of the Badan Pusat Statistik (BPS). The MAPE results on the GRU model forecast the value of oil and gas exports and imports at 12.19% and 14.30%, respectively. The best average forecasting of export and import values obtained a MAPE of 13.38%.
Adaptive Resonance Theory-Based Approach for Robust and Efficient Face Recognition Hewa Zangana; Ayaz Khalid Mohammed; Marwan Omar; Firas Mahmood Mustafa; Anik Vega Vitianingsih
Vokasi UNESA Bulletin of Engineering, Technology and Applied Science Vol. 2 No. 3 (2025)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v2i3.38709

Abstract

Face recognition systems play a crucial role in security, surveillance, and authentication applications. However, traditional deep learning-based models, particularly Convolutional Neural Networks (CNNs), often struggle with issues such as varying lighting conditions, occlusions, and high computational costs. This paper proposes an Adaptive Resonance Theory (ART)-based face recognition framework that enhances recognition robustness and computational efficiency. Unlike CNNs, ART enables incremental learning without requiring retraining, making it suitable for realtime applications. The study evaluated the proposed system on threebenchmark datasets: LFW, Yale, and ORL. Experimental results indicate that the ART-based model achieved an average accuracy of 96.2%, outperforming CNN-based models (93.5%) while reducing recognition time by 25%. Additionally, ART demonstrated superior adaptability, maintaining recognition accuracy above 94% even under occlusion and low-light conditions. These findings confirm the effectiveness of ART-based face recognition for security, access control, and innovative surveillance applications. Future research will focus on integrating ART with deep learning techniques for enhanced performance in large-scale datasets.
Co-Authors Anastasia Lidya Maukar Ayaz Khalid Mohammed Firas Mahmood Mustafa Hewa Zangana Ilham Adji Saputra Jack Febrian Rusdi Marwan Omar Yudi Kristyawan