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International Transactions on Artificial Intelligence (ITALIC)
Published by Pandawan Sejahtera Indonesia
ISSN : 29636086     EISSN : 29631939     DOI : https://doi.org/10.33050/italic
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Library & Information Science
International Transactions on Artificial Intelligence (ITALIC) is an international, open-access journal established to publish groundbreaking research in the field of Artificial Intelligence (AI). ITALIC focuses on both theoretical and experimental AI research and explores its applications across various interdisciplinary fields. The journal places a strong emphasis on emerging technologies that contribute to sustainable development, in line with the United Nations Sustainable Development Goals (SDGs). ITALIC welcomes contributions that cover a wide range of AI applications, including machine learning, neural networks, natural language processing, AI in energy management, sustainability, and urban infrastructure. In addition to original research, the journal publishes reviews, mini-reviews, case studies, and commentaries, fostering dynamic discussions on the evolving role of AI in addressing global challenges. All submissions are rigorously reviewed through a double-blind peer-review process, ensuring high academic standards. As an open-access journal, ITALIC makes its content freely available to a global audience, enhancing the dissemination of critical insights. Each article is assigned a Digital Object Identifier (DOI), ensuring permanent access and easy referencing.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 84 Documents
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Deep Learning-Based Solar Energy Forecasting Under Climate Variability Untung Rahardja; Nolan Liam; Sri Watini
International Transactions on Artificial Intelligence Vol. 5 No. 1 (2026): November
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v5i1.1094

Abstract

Rapid growth in renewable energy adoption and increasing climate variability have created significant challenges for accurately forecasting solar energy generation, as fluctuations in environmental conditions directly influence photovoltaic system performance and energy stability. This study aims to develop a deep learning based forecasting framework capable of improving solar energy prediction accuracy under changing climatic conditions by effectively capturing complex nonlinear relationships among meteorological variables and energy production patterns. The proposed approach utilizes historical solar irradiance, temperature, humidity, wind speed, cloud cover, and atmospheric pressure data as predictive inputs and employs a Long Short-Term Memory (LSTM) deep learning architecture to model temporal dependencies and environmental variability affecting solar energy generation. Experimental evaluation was conducted using publicly available renewable energy datasets, with model performance assessed through Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). The results demonstrate that the proposed deep learning framework achieved an RMSE of 4.12, an MAE of 3.08, a MAPE of 5.43%, and an R² value of 0.952, indicating strong predictive capability and high agreement between predicted and actual solar energy outputs. These findings suggest that deep learning models can effectively support intelligent renewable energy management by improving forecasting reliability under climate uncertainty, thereby contributing to energy efficiency, grid stability, and sustainable green technology development. 
A Hybrid Fuzzy Neural Network Model for Intelligent Traffic Congestion Prediction in Smart Cities Ryan Saputra Alam; Salahuddin Alam; Maulana Abbas; Asher Nuche
International Transactions on Artificial Intelligence Vol. 5 No. 1 (2026): November
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v5i1.1097

Abstract

Rapid urbanization and the increasing number of vehicles in metropolitan areas have intensified traffic congestion problems, creating significant challenges for transportation efficiency, environmental sustainability, and urban mobility management in smart cities. This study aims to develop a hybrid fuzzy neural network model capable of improving traffic congestion prediction accuracy by effectively handling the uncertainty and nonlinear relationships inherent in urban traffic systems. The proposed model integrates fuzzy logic with neural network learning mechanisms to capture ambiguous traffic patterns and adaptively learn complex interactions among traffic-related variables, including traffic volume, vehicle speed, traffic density, weather conditions, time intervals, and road occupancy rates. The experimental evaluation was conducted using historical traffic datasets collected from smart city transportation environments, while model performance was assessed using Root Mean Square Error, Mean Absolute Error, Mean Absolute Percentage Error, accuracy, and the coefficient of determination. The results demonstrate that the proposed hybrid fuzzy neural network model achieved superior predictive performance and greater robustness compared with conventional machine learning approaches, particularly in scenarios characterized by dynamic traffic fluctuations and uncertain environmental conditions. Furthermore, the integration of fuzzy reasoning significantly improved the model's capability to represent gradual transitions between congestion levels and reduce sensitivity to noisy traffic observations. The study concludes that hybrid fuzzy neural network approaches provide an effective and scalable solution for intelligent traffic congestion prediction and can support data-driven traffic management strategies in future smart city ecosystems. 
A Hybrid Artificial Intelligence Framework for Predicting Crop Yield Under Environmental Uncertainty Dariari Robert; Victoriani Victoriani
International Transactions on Artificial Intelligence Vol. 5 No. 1 (2026): November
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v5i1.1100

Abstract

Agricultural productivity is increasingly challenged by environmental uncertainty caused by fluctuations in temperature, rainfall, humidity, and soil conditions, making accurate crop yield prediction difficult when relying solely on conventional statistical approaches. This study aims to develop a hybrid artificial intelligence framework capable of improving crop yield prediction performance under uncertain environmental conditions by integrating machine learning techniques with fuzzy modelling to better capture nonlinear relationships and ambiguous environmental patterns. The proposed framework employs a hybrid architecture combining a neural learning mechanism with fuzzy inference to process multiple environmental variables, including temperature, precipitation, soil moisture, humidity, and fertilizer usage, as predictive inputs for crop yield estimation. Experimental evaluation was conducted using agricultural datasets containing historical environmental and production records, while model performance was assessed using Root Mean Square Error, Mean Absolute Error, Mean Absolute Percentage Error, and the coefficient of determination. The results demonstrate that the hybrid framework achieved superior predictive accuracy and robustness compared with conventional machine learning models, particularly when handling noisy and uncertain environmental data. Furthermore, the incorporation of fuzzy reasoning enhanced the model's capability to represent uncertainty and improve decision-making reliability in agricultural management. The study concludes that hybrid artificial intelligence approaches provide an effective and scalable solution for supporting sustainable agriculture through more accurate and adaptive crop yield prediction under environmental uncertainty.
Machine Learning-Based Prediction of Electric Vehicle Charging Demand for Sustainable Transportation Nyree Ani; Nuke Puji Lestari Santoso; Rajendra Kumar
International Transactions on Artificial Intelligence Vol. 5 No. 1 (2026): November
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v5i1.1245

Abstract

The increasing adoption of Electric Vehicles (EV) presents challenges in managing uncertain charging demand, optimizing infrastructure utilization, and maintaining energy system stability. This study aims to develop and evaluate a machine learning-based prediction framework for estimating EV charging energy demand and identifying the most effective predictive algorithm to support sustainable transportation planning. The proposed framework employs historical EV charging observations and contextual variables, including charging duration, charging time, day of the week, ambient temperature, charging frequency, and historical energy consumption. Three supervised machine learning algorithms, namely Random Forest (RF), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost), are comparatively evaluated. Predictive performance is assessed using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). The reported experimental findings indicate that XGBoost achieves the highest predictive accuracy, with an RMSE of 2.84 kWh, MAE of 1.97 kWh, MAPE of 6.21%, and R² of 0.941. However, the comparative performance metrics for RF and SVR must be reported in the main Results section to substantiate the claimed superiority of XGBoost. The proposed framework demonstrates the potential of machine learning to identify charging demand patterns using historical charging, temporal, and environmental information. Subject to further experimental verification, the findings may support data-driven charging infrastructure management, electricity resource allocation, and sustainable transportation planning.

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