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All Journal Journal of Soft Computing Exploration Journal of Information System Exploration and Research
Zaenur Rozikin
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.
A Multi-Level Clustering Framework for Provincial EducationalFacility Equalization in Indonesia Antika Zahrotul Kamalia; Zaenur Rozikin; Hemdani Rahendra Herlianto; Hendra Arya Syaputra; Asep Arwan Sulaeman; Choiriyatun Nisa Latansa
Journal of Information System Exploration and Research Vol. 4 No. 2: April 2026
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v4i2.13

Abstract

Equitable distribution of educational facilities is crucial for development planning, as regional disparities in facility availability can constrain access to education. This study identifies priority areas for school-facility equalization in Indonesia based on 2024 data covering 38 provinces and village/urban-ward-based facility availability by education level. Unlike single-stage clustering studies, this study combines macro-prioritization and micro-level need profiling to identify which provinces should be prioritized but also which education levels require attention. The analysis includes log1p transformation, standardization, optimal cluster selection using Elbow and Silhouette criteria, and the application of Level-1 and Level-2 K-Means clustering. The Level-1 results produce three priority groups: High Priority, Medium Priority, and Low Priority, with the optimal structure at K=3. The Level-2 analysis within the high-priority group is most stable at K2=2, distinguishing provinces dominated by primary and lower-secondary facility shares from those with a more balanced composition and relatively higher tertiary share. The Silhouette values indicate that the selected clusters provide reasonably separated groupings. The proposed framework provides a data-driven priority map and level-specific need profiles. The results can support staged infrastructure planning and differentiated interventions across provincial priority groups to strengthen educational facility equalization in Indonesia.
Co-Authors Antika Zahrotul Kamalia Asep Arwan Sulaeman Choiriyatun Nisa Latansa Hemdani Rahendra Herlianto Hendra Arya Syaputra Shiza Hassan