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All Journal Jurnal Info Sains : Informatika dan Sains Journal of Artificial Intelligence and Digital Business Journal of Computer Science and Research Jurnal Ilmiah Multidisiplin Indonesia Journal of Data Mining and Information Systems Multidisiplin Pengabdian Kepada Masyarakat (M-PKM) Journal of Data Science, Technology, and Artificial Intelligence Jurnal Sistem Informasi dan Teknologi Jaringan
Harefa, Ade May Luky
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Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Library & Information Science Nursing Physics

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Crop Yield Prediction Using Artificial Neural Network with Principal Component Analysis Dimensionality Reduction Bosker Sinaga; Harefa, Ade May Luky; Adrianta Pandia; Agil Alfarezi
Jurnal Sistem Informasi dan Teknologi Jaringan Vol 7 No 1 (2026): Maret
Publisher : CV. ADMITECH SOLUTIONS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63703/sisfotekjar.v7i1.144

Abstract

Accurate crop yield prediction is essential to support agricultural planning, food supply stability, and decision-making in modern precision agriculture. Agricultural production is influenced by many complex and nonlinear factors such as rainfall, temperature, humidity, soil conditions, and fertilizer usage. Traditional statistical methods often face limitations in handling high-dimensional and nonlinear agricultural datasets. Therefore, this study proposes a crop yield prediction model using Artificial Neural Network (ANN) combined with Principal Component Analysis (PCA) for dimensionality reduction. PCA is applied in the preprocessing stage to reduce redundant and correlated input variables while preserving the most important data variance. The reduced dataset is then used to train the ANN model to predict crop yield values. The model is implemented using Python with libraries including NumPy, Pandas, Scikit-learn, and TensorFlow/Keras. The dataset used in this research consists of 1000 agricultural records covering three crop commodities, namely maize, barley, and rice. Model performance is evaluated using visualization techniques including histogram error, histogram predicted, PCA explained variance, predicted vs actual plot, residual plot, and training history graph. Experimental results show that the PCA-ANN model produces accurate and stable prediction results with low prediction error and strong agreement between predicted and actual values. The integration of PCA and ANN improves prediction performance, reduces computational complexity, and minimizes overfitting risk. This research demonstrates that the PCA-ANN approach is effective for crop yield prediction and can support data-driven agricultural decision-making.
Co-Authors Ade Maulana Adhitya Pratama, Yudhistira Adrianta Pandia Afif Yasri Agil Alfarezi Antoni, Robin Bosker Sinaga Dulianto, Des Fitri Amalini Hasugian , Paska Marto Insandi, Arief Muhazir Jijon Raphita Sagala Limbong, Yohannes France Medhian Ahmadi Putra MIFTAHUL JANNAH Muhammad Syahputra Novelan Nababan, Adli Abdillah Nazara, Melfance Priskila Parimanam Ramlan Marbun Sijabat, Petti Indrayati Sitepu, Andri Ismail Yudhistira Adhitya Pratama