Perbandingan Algoritma Klasifikasi untuk Rekomendasi Tanaman Berdasarkan Data Lingkungan

Willy Muhammad Fauzi; Adi Dadan Ramdana; Fajar Firmansyah; Fajar Yudha Nugraha; Akpil Mauhib

doi:10.30591/polektro.v14i1.8682

Perbandingan Algoritma Klasifikasi untuk Rekomendasi Tanaman Berdasarkan Data Lingkungan

Willy Muhammad Fauzi, Adi Dadan Ramdana, Fajar Firmansyah, Fajar Yudha Nugraha, Akpil Mauhib

Abstract

Pemilihan tanaman yang tepat sangat penting dalam meningkatkan produktivitas pertanian. Dengan adanya teknologi machine learning, proses rekomendasi tanaman berdasarkan data lingkungan dapat lebih efisien, terutama dalam menghadapi kondisi iklim yang bervariasi. Penelitian ini bertujuan untuk membandingkan tiga algoritma klasifikasi, yaitu Random Forest, XGBoost, dan SVM, dalam memberikan rekomendasi tanaman yang sesuai berdasarkan data lingkungan yang mencakup suhu, kelembaban, pH tanah, dan curah hujan. Penelitian ini menggunakan dataset yang mencakup fitur lingkungan dari BPS Kota Tasikmalaya, yang kemudian diuji dengan tiga algoritma klasifikasi machine learning Random Forest, XGBoost, dan SVM. Setiap model dievaluasi berdasarkan akurasi, precision, recall, dan F1-score. Random Forest menunjukkan hasil terbaik dengan akurasi 99.32%, diikuti oleh XGBoost dengan akurasi 98.64%, dan SVM dengan akurasi 96.82%. Model-model ini memberikan rekomendasi tanaman seperti jeruk dan melon, sementara SVM lebih sering merekomendasikan mothbeans. Random Forest memberikan hasil yang paling optimal dalam sistem rekomendasi tanaman berbasis data lingkungan, meskipun SVM lebih cepat dalam hal pelatihan model. Penelitian ini menunjukkan pentingnya penerapan algoritma machine learning untuk mendukung keputusan pertanian berbasis kondisi lingkungan lokal.

Keywords

Rekomendasi Tanaman; Machine Learning; Random Forest; XGBoost; SVM

Full Text:

References

A. Changela, Y. Kumar, and A. Koul, “Machine Learning-based Approaches for Crop Recommendations and Prediction,” in 2023 International Conference on Communication, Security and Artificial Intelligence (ICCSAI), IEEE, Nov. 2023, pp. 370–376. doi: 10.1109/ICCSAI59793.2023.10421406.

S. Gambhir, M. Sharma, K. Agarwal, K. Kumar, L. Kumar, and M. Chaudhary, “Crop Recommendation System Using Machine Learning,” 2023 5th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), pp. 523–530, 2023, doi: 10.1109/ICAC3N60023.2023.10541784.

Dolli, P. Rawat, M. Bajaj, S. Vats, and V. Sharma, “An Analysis of Crop Recommendation Systems Employing Diverse Machine Learning Methodologies,” 2023 International Conference on Device Intelligence, Computing and Communication Technologies, (DICCT), pp. 619–624, 2023, doi: 10.1109/DICCT56244.2023.10110085.

S. Bhatnagar, N. Lakshmi, A. A, and V. S. M. Y, “Development of a Crop Recommendation System Through the Use of Various Machine Learning Algorithms,” 2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE), pp. 1–6, 2024, doi: 10.1109/ic-ETITE58242.2024.10493758.

E. Elbasi et al., “Crop Prediction Model Using Machine Learning Algorithms,” Applied Sciences, p., 2023, doi: 10.3390/app13169288.

U. P. Jakarbet, S. P, S. M, and A. P, “An Empirical Analysis of Machine Learning Algorithms for Agricultural Crop Prediction,” 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), pp. 1782–1790, 2024, doi: 10.1109/I-SMAC61858.2024.10714677.

S. R. Sani, S. V. S. Ummadi, S. Thota, N. Muthineni, V. S. S. Swargam, and T. S. Ravella, “Crop Recommendation System using Random Forest Algorithm in Machine Learning,” 2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC), pp. 501–505, 2023, doi: 10.1109/ICAAIC56838.2023.10141384.

T. Van Klompenburg, A. Kassahun, and C. Catal, “Crop yield prediction using machine learning: A systematic literature review,” Comput. Electron. Agric., vol. 177, p. 105709, 2020, doi: 10.1016/j.compag.2020.105709.

H. Lahza et al., “Optimization of Crop Recommendations Using Novel Machine Learning Techniques,” Sustainability, p., 2023, doi: 10.3390/su15118836.

S. J. Jang, H. H. Park, and Y. I. Kuk, “Application of Various Extracts Enhances the Growth and Yield of Cucumber (Cucumis sativus L.) without Compromising the Biochemical Content,” Agronomy, vol. 11, no. 3, p. 505, Mar. 2021, doi: 10.3390/agronomy11030505.

S. S. , T. R. K. , R. A. S. Soni, “A machine learning approach to crop recommendations,” Int J Environ Res, vol. 19, pp. 148–158, 2019.

B. N. , K. V. Mohapatra, “Crop recommendation system using machine learning algorithms,” International Journal of Computer Applications, vol. 9, pp. 12–23, 2021.

“Incorporating soil information with machine learning for crop recommendation,” Soni, S. R., Tiwari, R. K., Rawat, A. S., vol. 25, 2025.

S. Behera, “Smart crop prediction using random forest and machine learning models,” SSRN Electronic Journal, vol. 13, pp. 244–257, 2024.

A. , G. R. Kumar, “Predicting optimal crop yields using machine learning algorithms,” Agricultural Environmental Sciences, vol. 6, p. 172, 2022.

S. , J. S. S. , P. S. R. Mahesh, “Integration of remote sensing data and machine learning for crop classification,” Journal Remote Sensing, vol. 11, 2019.

DOI: https://doi.org/10.30591/polektro.v14i1.8682