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All Journal Jurnal Teknik Informatika (JUTIF) Journal of Soft Computing Exploration
Antika Zahrotul Kamalia
Department of Informatics Engineering, Universitas Pelita Bangsa, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Comparative evaluation of deep learning models for dried corn price prediction in east java Antika Zahrotul Kamalia; Choiriyatun Nisa Latansa; Zaenur Rozikin; Hemdani Rahendra Herlianto; Shiza Hassan
Journal of Soft Computing Exploration Vol. 7 No. 2 (2026): June 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i2.48

Abstract

Forecasting dry shelled corn prices was important for supporting decision-making by farmers, traders, feed industries, and local governments. This study comparatively evaluated several deep learning models, namely Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network 1D (CNN1D), Temporal Convolutional Network (TCN), and Transformer, for predicting dry shelled corn prices in East Java. Classical benchmark models, namely naïve, drift, and simple exponential smoothing (SES), were also incorporated into the experimental design. Using daily price data from 2020 to 2024, a 30-day lookback window, and multivariate features derived from price movements, calendar variables, and rolling statistics, model performance was assessed using MAE, RMSE, MAPE, sMAPE, and . The results showed that the naïve baseline achieved the best overall performance on the 2024 test set, while TCN was the strongest among the evaluated deep learning models. TCN obtained RMSE of 176.95 and of 0.6895, whereas the naïve baseline achieved RMSE of 20.06 and of 0.9960. Overall, all deep learning models were outperformed by the naïve persistence benchmark, indicating that greater model complexity did not automatically improve forecasting accuracy on this highly persistent price series.
Artificial Intelligence in Green and Sustainable Investment: a Bibliometric and Systematic Literature Review Antika Zahrotul Kamalia; Arief Wibowo; Deni Mahdiana
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5287

Abstract

Green and sustainable investment has gained increasing global attention due to the urgency of the climate crisis, social demands, and the adoption of Environmental, Social, and Governance (ESG) principles. However, research on the application of artificial intelligence (AI) in this domain remains fragmented and lacks a comprehensive mapping. This study aims to map the trends, research directions, and key findings related to AI in green and sustainable investment using a bibliometric and systematic literature review (SLR) approach. Data were retrieved from the Scopus database and screened with the PRISMA framework, resulting in 24 articles analyzed through VOSviewer and thematic synthesis. The results indicate significant developments in energy efficiency, green buildings, machine learning, and sustainability, alongside an expanding pattern of international collaboration. Nonetheless, limitations remain, including insufficient cross-sectoral integration, limited empirical studies in developing countries, and the lack of AI models that holistically incorporate risk, ESG, and SDGs indicators. The main contribution of this study lies in providing a structured literature mapping that can serve as a foundation for developing more integrative AI frameworks and expanding research contexts to optimize sustainable green investment. These findings are expected to be valuable for researchers and practitioners in advancing innovation and strengthening the AI-driven sustainable finance ecosystem.
Co-Authors Arief Wibowo Choiriyatun Nisa Latansa Deni Mahdiana Hemdani Rahendra Herlianto Shiza Hassan Zaenur Rozikin