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INDONESIA
Journal of Informatics and Vocational Education
Published by Universitas Sebelas Maret
ISSN : -     EISSN : 27467813     DOI : 10.20961/joive
Core Subject : Science, Education, Social,
Computer Science & IT Education Social Sciences
The Journal of Informatics and Vocational Education (JOIVE) is committed to advancing the understanding of applied computer science education, with a particular focus on the integration of informatics in vocational training and the development of innovative teaching and learning methodologies. Pertinent but not limited to the teaching and learning of informatics, including curriculum design, instructional methods, and the use of technology to enhance the educational experience.Vocational Education,Innovative Educational Practices, Educational Technology Development, Impact of Informatics on Society , and Case Studies and Best Practices. JOIVE scope cover all aspect of Informatics Theory, Application, and Vocational Education, including(but not limited): Informatics Theory, Information System, Mobile and Wireless Communication Computer Networks. Distributed System, Cloud Computing, IoT Data Mining, Artificial intelligence, Machine Learning, and Education Learning Technology E-Learning Educational Technology Vocational Education Emerging technologies in education
Arjuna Subject : Ilmu Sosial - Pendidikan
Articles 51 Documents
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Predicting Solar Radiation Using Temporal Convolutional Networks and Long Short-Term Memory: A Comparative Study Based on Automatic Weather Station Data Famiyana Dewi; Tukiyat; Yan Mitha Djaksana
Journal of Informatics and Vocational Education Vol. 9 No. 3 (2026): November 2026
Publisher : Informatics Education Department, Faculty of Teacher Training and Education, Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/joive.v9i3.3758

Abstract

Solar radiation is a key meteorological parameter for climatology, hydrology, agriculture, renewable energy, and environmental analysis. Direct measurement remains limited because it requires specialised instruments, regular calibration, and high maintenance costs. This study compared Temporal Convolutional Network (TCN) and Long Short-Term Memory (LSTM) models for predicting solar radiation using Automatic Weather Station data from the Aceh Climatological Station. The dataset comprised 4,152 observations from January to December 2023, with rainfall, air temperature, relative humidity, air pressure, wind speed, wind direction, and time features as predictors, while solar radiation served as the target variable. The research process included data quality control, interpolation of invalid values, Min-Max Scaling, time feature engineering, sliding-window sequence generation with 24 time steps, and time-based data splitting using 70:15:15, 80:10:10, and 70:10:20 scenarios. Model performance was evaluated using MAE, RMSE, MAPE, and R². The 80:10:10 split produced the best results for both models. The LSTM model achieved slightly better overall performance than TCN based on RMSE and R², although both models captured the temporal pattern of solar radiation. The study provides an AWS-based temporal deep learning framework for radiation prediction in tropical regions of Indonesia.

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