Optimization of Prediction of Labor Percentage of Agricultural Information in NTB using Salp Swarm Algorithm

Lailatul Hoyali, Syaharuddin Syaharuddin, Vera Mandailina

Abstract


Abstract: This research aims to develop an appropriate prediction model regarding the percentage of informal labor in the agricultural sector of West Nusa Tenggara (NTB) Province by utilizing the Salp Swarm Algorithm (SSA) within the framework of the third-order Autoregressive (AR) model. This quantitative approach with computational experiments uses secondary data on the percentage of informal labor in the agricultural sector in NTB from 2015 to 2024. The SSA algorithm is used to optimize the time series model parameters and evaluated using the Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) indicators. The results show that the resulting prediction model has high accuracy, with an MSE of 0.9353 and MAPE of 0.93%, as well as stable projections for the period 2025-2029 which are estimated to be between 97.19% to 98.41%. This study contributes to the development of agricultural employment policies and suggests further research by considering external variables to improve the accuracy of the model.

 


Keywords


Salp Swarm Algorithm (SSA), Informal Labor Prediction, NTB Agricultural Sector.

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References


Al-Shabi, M., Ghenai, C., Bettayeb, M., Ahmad, F. F., & Assad, M. E. H. (2021). Estimating pv models using multi-group salp swarm algorithm. IAES International Journal of Artificial Intelligence, 10(2), 398–406. https://doi.org/10.11591/IJAI.V10.I2.PP398-406

Anisah, A., & Damayanti, R. (2024). Perlindungan Hukum Bagi Pekerja Freelance : Analisis Regulasi , Tantangan , dan Akses Jaminan Sosial di Indonesia. 2(4), 566–571.

Houssein, E. H., Mohamed, I. E., & Wazery, Y. M. (2020). Salp Swarm Algorithm: A Comprehensive Review. Studies in Computational Intelligence, 890, 285–308. https://doi.org/10.1007/978-3-030-40977-7_13

Huang, S., Nianguang, C. A. I., Penzuti Pacheco, P., Narandes, S., Wang, Y., & Wayne, X. U. (2018). Applications of support vector machine (SVM) learning in cancer genomics. Cancer Genomics and Proteomics, 15(1), 41–51. https://doi.org/10.21873/cgp.20063

Munthe, A., Yarham, M., & Siregar, R. (2023). Peranan UMKM terhadap perekonomian Indonesia. Jurnal Ekonomi Bisnis, Manajemen Dan Akuntansi, 2(3), 593–614.

Nasir, M., Mahmudinata, A. A., Ulya, M., & Firdaus, F. A. (2023). Strategi pemberdayaan sekolah sebagai upaya peningkatan manajemen pendidikan. Journal Of International Multidisciplinary Research, 1(2), 799–816. https://doi.org/10.62504/mbznza39

Pertanian, F., & Muhammadiyah, U. (2024). Seminar Nasional Pertanian Artificial Intelligence-Based Innovation in Improving Agricultural Production and Welfare of Village Farmers in Indonesia. 1–14.

Sabillah, S., Safitri, A., Basuki, B., Nur, F., Habibi, A., Airlangga, U., & Korespondensi, P. (2025). Equity Penggunaan Artificial Intelligence Dalam Proses Audit : Sudut Pandang Etika Islam. 28(1), 1–14. https://doi.org/10.34209/equ.v28i1.7256

Sibagariang, F. A., Mauboy, L. M., Erviana, R., & Kartiasih, F. (2023). Gambaran Pekerja Informal dan Faktor-Faktor yang Memengaruhinya di Indonesia Tahun 2022. Seminar Nasional Official Statistics, 2023(1), 151–160. https://doi.org/10.34123/semnasoffstat.v2023i1.1892

Srivinay, Manujakshi, B. C., Kabadi, M. G., & Naik, N. (2022). A Hybrid Stock Price Prediction Model Based on PRE and Deep Neural Network. Data, 7(5), 1–12. https://doi.org/10.3390/data7050051

Wang, Z., Ala, A., Liu, Z., Cui, W., Ding, H., Jin, G., & Lu, X. (2024). A Hybrid Equilibrium Optimizer Based on Moth Flame Optimization Algorithm to Solve Global Optimization Problems. Journal of Artificial Intelligence and Soft Computing Research, 14(3), 207–235. https://doi.org/10.2478/jaiscr-2024-0012


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