Firefly Algorithm for SVM Multi-class Optimization on Soybean Land Suitability Analysis

Andi Nurkholis - Universitas Teknokrat Indonesia, Indonesia
Styawati Styawati - Universitas Teknokrat Indonesia, Indonesia
Alvi Suhartanto - Universitas Teknokrat Indonesia, Indonesia

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Soybean is the primary source of vegetable protein nutrition, containing fat and vitamins that Indonesian people widely consume. The decline in soybean production in Indonesia every year is due to the reduced area of soybean cultivation, thereby increasing dependence on imports from other countries. Land suitability maps can provide directions for priority locations for soybean cultivation based on land characteristics and weather to produce optimal production. The SVM multi-class algorithm has been applied to classify land suitability data to create a land suitability map but has yet to obtain optimal accuracy, especially for sigmoid kernels. The objective of this study is to enhance the performance of the sigmoid kernel SVM by utilizing the firefly algorithm. The study focuses on evaluating the suitability of soybean cultivation in Bogor and Grobogan Regencies. The results of the tests indicate that the firefly algorithm-optimized SVM (FA-SVM) significantly improves accuracy compared to the SVM without optimization. The accuracy achieved by FA-SVM is 89.95%, while the SVM without optimization only achieves an accuracy of 65.99%. The best parameters produced by the firefly algorithm are C=2.33 and σ=0.45 obtained from firefly customization, and the number of generations is 10. Based on this, the optimization algorithm can be used to produce an optimal model. The best optimal model obtained can be used as a guide for priority locations/areas for soybean cultivation by farming communities, so as to produce maximum soybean productivity.


Firefly; land suitability; sigmoid; soybean; SVM.

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R. Krisdiana, N. Prasetiaswati, I. Sutrisno, F. Rozi, A. Harsono, and M. J. Mejaya, “Financial Feasibility and Competitiveness Levels of Soybean Varieties in Rice-Based Cropping System of Indonesia,” Sustainability, vol. 13, no. 15, p. 8334, 2021.

A. Romulo and R. Surya, “Tempe: A traditional fermented food of Indonesia and its health benefits,” Int J Gastron Food Sci, vol. 26, p. 100413, 2021.

L. Liu et al., “Traditional fermented soybean products: processing, flavor formation, nutritional and biological activities,” Crit Rev Food Sci Nutr, vol. 62, no. 7, pp. 1971–1989, 2022.

P. Qin, T. Wang, and Y. Luo, “A review on plant-based proteins from soybean: Health benefits and soy product development,” J Agric Food Res, vol. 7, p. 100265, 2022.

A. C. Akmalovna, “Characteristics and Advantages of Soybean Benefits in Every way,” Journal of Ethics and Diversity in International Communication, vol. 1, no. 8, pp. 67–69, 2022.

Pusat Data dan Sistem Informasi Pertanian, Outlook Komoditas Pertanian Tanaman Pangan Kedelai. Kementerian Pertanian, 2020.

A. Nurkholis and I. S. Sitanggang, “A spatial analysis of soybean land suitability using spatial decision tree algorithm,” in Proceedings of SPIE - The International Society for Optical Engineering, 2019. doi: 10.1117/12.2541555.

A. Nurkholis and S. Styawati, “Prediction Model for Soybean Land Suitability Using C5. 0 Algorithm,” Jurnal Online Informatika, vol. 6, no. 2, pp. 163–171, 2021.

P. Munene, L. M. Chabala, and A. M. Mweetwa, “Land Suitability Assessment for Soybean (Glycine max (L.) Merr.) Production in Kabwe District, Central Zambia,” Journal of Agricultural Science, vol. 9, no. 3, p. 74, 2017, doi: 10.5539/jas.v9n3p74.

A. Nurkholis, I. S. Sitanggang, Annisa, and Sobir, “Spatial decision tree model for garlic land suitability evaluation,” International Journal of Artificial intelligence (IJ-AI), vol. 10, no. 3, pp. 666–675, 2021, doi: 10.11591/ijai.v10.i3.pp666-675.

I. S. Sitanggang, A. Nurkholis, Annisa, and M. A. Agmalaro, “Garlic Land Suitability System based on Spatial Decision Tree,” in Proceedings ofthe International Conferences on Information System and Technology, 2020, pp. 206–210. doi: 10.5220/0009908002060210.

A. K. Nisyak, F. Ramdani, and Suprapto, “Web-GIS development and analysis of land suitability for rice plant using GIS-MCDA method in Batu city,” in International Symposium on Geoinformatics, 2017, pp. 24–33. doi: 10.1109/ISYG.2017.8280667.

I. W. Nuarsa, I. N. Dibia, K. Wikantika, D. Suwardhi, and I. N. Rai, “Gis based analysis of agroclimate land suitability for Banana plants in Bali Province, Indonesia,” Hayati, vol. 25, no. 1, pp. 11–17, 2018.

A. Nurkholis, S. Styawati, I. S. Sitanggang, J. Jupriyadi, A. Matin, and P. Maulana, “SVM Multi-Class Algorithm for Soybean Land Suitability Evaluation,” in 2022 International Conference on Information Technology Research and Innovation (ICITRI), IEEE, 2022, pp. 65–70.

S. Styawati and K. Mustofa, “A Support Vector Machine-Firefly Algorithm for Movie Opinion Data Classification,” IJCCS (Indonesian Journal of Computing and Cybernetics Systems), vol. 13, no. 3, pp. 219–230, 2019.

H. Zhang, Y. Shi, X. Yang, and R. Zhou, “A firefly algorithm modified support vector machine for the credit risk assessment of supply chain finance,” Res Int Bus Finance, vol. 58, p. 101482, 2021.

BBSDLP, Atlas peta kesesuaian lahan dan arahan komoditas pertanian pertanian, Kabupaten Grobogan, Provinsi Jawa Tengah, skala 1:50.000. Bogor (ID): Badan Penelitian dan Pengembangan Pertanian, Kementerian Pertanian, 2016.

BBSDLP, Atlas peta kesesuaian lahan dan arahan komoditas pertanian pertanian, Kabupaten Bogor, Provinsi Jawa Barat, skala 1:50.000, 2nd ed. Bogor (ID): Badan Penelitian dan Pengembangan Pertanian, Kementerian Pertanian, 2016.

Pusdatin, Outlook Kedelai: Komoditas Pertanian Subsektor Tanaman Pangan. Jakarta (ID): Pusat Data dan Sistem Informasi Pertanian Kementerian Pertanian, Kementerian Pertanian RI, 2018.

A. Nurkholis, Styawati, D. Alita, A. Sucipto, M. Chanafy, and Z. Amalia, “Hotspot Classification for Forest Fire Prediction using C5.0 Algorithm,” in International Conference on Intelligent Cybernetics Technology & Applications (ICICyTA), Bandung: IEEE, 2021.

S. K. Adhikary, N. Muttil, and A. G. Yilmaz, “Cokriging for enhanced spatial interpolation of rainfall in two Australian catchments,” Hydrol Process, vol. 31, no. 12, pp. 2143–2161, 2017, doi: 10.1002/hyp.11163.

E. J. Camp et al., “Supervised machine learning to predict reduced depression severity in people with epilepsy through epilepsy self-management intervention,” Epilepsy & Behavior, vol. 127, p. 108548, 2022.

P. Sahoo, A. K. Behera, M. K. Pandia, C. S. K. Dash, and S. Dehuri, “On the study of GRBF and polynomial kernel based support vector machine in web logs,” in 2013 1st International Conference on Emerging Trends and Applications in Computer Science, IEEE, 2013, pp. 1–5.

D. Alita, “Multiclass Svm Algorithm For Sarcasm Text In Twitter,” JATISI (Jurnal Teknik Informatika Dan Sistem Informasi), vol. 8, no. 1, pp. 118–128, 2021.

D. Alita, S. Priyanta, and N. Rokhman, “Analysis of emoticon and sarcasm effect on sentiment analysis of Indonesian language on Twitter,” Journal of Information Systems Engineering and Business Intelligence, vol. 5, no. 2, pp. 100–109, 2019.

A. Nurkholis, D. Alita, and A. Munandar, “Comparison of Kernel Support Vector Machine Multi-Class in PPKM Sentiment Analysis on Twitter,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 6, no. 2, Apr. 2022.

X.-S. Yang, Z. Cui, R. Xiao, A. H. Gandomi, and M. Karamanoglu, Swarm intelligence and bio-inspired computation: theory and applications. Newnes, 2013.

B. Xing and W.-J. Gao, Innovative computational intelligence: a rough guide to 134 clever algorithms, vol. 62. Springer, 2014.

T. H. Kerbaa, A. Mezache, and H. Oudira, “Model Selection of Sea Clutter Using Cross Validation Method,” Procedia Comput Sci, vol. 158, pp. 394–400, 2019, doi: 10.1016/j.procs.2019.09.067.

H. Ling, C. Qian, W. Kang, C. Liang, and H. Chen, “Combination of Support Vector Machine and K-Fold cross validation to predict compressive strength of concrete in marine environment,” Constr Build Mater, vol. 206, pp. 355–363, 2019.

A. Nurkholis, M. Muhaqiqin, and T. Susanto, “Spatial Decision Tree Algorithm for Evaluation of Suitability of Irrigated Rice Fields,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 4, no. 5, pp. 978–987, 2020, doi: 10.29207/resti.v4i5.2476.

A. Nurkholis, M. Muhaqiqin, and T. Susanto, “Land Suitability Analysis For Upland Rice Based on Soil and Weather Characteristics Using Spatial ID3,” JUITA: Jurnal Informatika, vol. 8, no. 2, p. 235, 2020, doi: 10.30595/juita.v8i2.8311.