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All Journal Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer
Agustian Prakarsya
Universitas Serelo Lahat
Computer Science & IT

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Optimasi Hyperparameter WOA-SVM pada Citra Daun Kopi Terpupuk NPK Agustian Prakarsya; Nina Dwi Putriani; Yusi Nurmala Sari; Firza Septian
BETRIK Vol. 16 No. 02 (2025): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/zrj1e094

Abstract

This study aims to analyze the impact of Whale Optimization Algorithm (WOA) optimization on the performance of Support Vector Machine (SVM) in classifying images of coffee leaves treated with NPK fertilizer. WOA is employed to find the optimal combination of SVM parameters to improve classification accuracy. The dataset consists of coffee leaf images that have undergone feature extraction based on color and texture. Performance evaluation was conducted using a confusion matrix, classification report, and heatmap visualization. The results show that the SVM model optimized with WOA performs better than the non-optimized SVM. Specifically, the non-optimized SVM achieved a precision of 0.82, recall of 0.81, and F1-score of 0.81. After optimization with WOA, the model’s precision increased to 0.90, recall to 0.88, and F1-score to 0.87. This study demonstrates that metaheuristic approaches like WOA can significantly enhance the performance of classification algorithms in the context of digital image processing. The findings have practical implications for early detection of plant quality through image-based analysis in technology-driven agriculture
Prediktor NPK Berbasis AI untuk Budidaya Kopi dengan Whale Optimization Algorithm Firza Septian; Agustian Prakarsya
BETRIK Vol. 16 No. 03 (2025): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/6t030w66

Abstract

Coffee is one of Indonesia’s major agricultural commodities, yet its productivity is often limited by inefficient fertilizer management, particularly in determining nitrogen (N), phosphorus (P), and potassium (K) requirements. Although conventional soil and leaf analyses are reliable, they are time-consuming and less practical for smallholder farmers. This underscores the need for an accurate, scalable, and cost-effective solution to optimize fertilizer usage. To address this issue, the study introduces an AI-based predictor for assessing NPK sufficiency in coffee plants. The research integrates computer vision and metaheuristic optimization to form a practical decision-support system. A dataset containing 12,000 images of coffee leaves was classified into three categories: Deficient, Sufficient, and Excessive. Image preprocessing involved resizing, grayscale conversion, HSV transformation, and normalization. Feature extraction utilized Histogram of Oriented Gradients (HOG) and HSV Color Histograms, followed by classification using a Support Vector Machine (SVM) optimized with the Whale Optimization Algorithm (WOA). The model achieved an accuracy exceeding 97%, effectively recognizing Deficient and Sufficient categories, with most misclassifications occurring in the Excessive class due to visual similarities. Model performance was validated using a confusion matrix, learning curve, and PCA visualization, confirming efficient convergence. The study highlights the promise of AI-driven solutions in enhancing precision agriculture and promoting sustainable coffee farming practices.
Co-Authors Firza Septian Nina Dwi Putriani Yusi Nurmala Sari