Deep Learning Based Model for Predicting the Contribution of SMEs to the Economy

Year 2023, Volume: 35 Issue: 2, 865 - 874, 01.09.2023

Anıl Utku , Ali Sevinç , M. Ali Akcayol

https://doi.org/10.35234/fumbd.1340992

Abstract

Small and Medium-sized Enterprises (SMEs) are private sector enterprises whose capital, workforce and assets are below the thresholds determined according to national regulations. SMEs play an important role in the economy of most countries in the world, especially in developing countries. SMEs, which make up approximately 90% of enterprises worldwide, provide more than 50% of employment. Estimating the contribution of SMEs to the economy at the country level is very important in terms of planning and investment. In this study, a deep learning-based model was developed to predict the contribution of SMEs to the economy. The developed LSTM-based deep learning model was compared with RF, SVM, CNN, GRU, MLP and RNN. Experimental results showed that the developed model had a better prediction performance than other models compared with 2.169 MSE, 1.473 RMSE, 1.175 MAE, and 0.959 R2 values.

Keywords

SME, MCDM, OCRA, economy, deep learning, machine learning

KOBİ’lerin Ekonomiye Sağladıkları Katkının Tahmini İçin Derin Öğrenme Tabanlı Model

Abstract

Küçük ve Orta Büyüklükteki İşletme (KOBİ)'ler, sermayesi, işgücü ve varlıkları, ulusal yönetmeliklere göre belirlenen eşik değerlerin altında olan özel sektör işletmeleridir. KOBİ'ler, özellikle gelişmekte olan ülkelerde olmak üzere dünyadaki çoğu ülkenin ekonomisinde önemli rol oynamaktadır. Dünya genelinde işletmelerin yaklaşık %90'ını oluşturan KOBİ'ler, istihdamın %50'sinden fazlasını sağlamaktadır. Ülke ölçeğinde KOBİ’lerin ekonomiye katkılarının tahin edilmesi planlama ve yatırım açısından oldukça önemlidir. Bu çalışmada, KOBİ’lerin ekonomiye sağladıkları katkının tahminine yönelik derin öğrenme tabanlı bir model geliştirilmiştir. Geliştirilen LSTM tabanlı derin öğrenme modelinin sonuçları, RF, SVM, CNN, MLP, RNN ve GRU ile karşılaştırılmıştır. Deneysel sonuçlar, geliştirilen derin öğrenme modelinin 2,169 MSE, 1,473 RMSE, 1,175 MAE ve 0,959 R2 değeri ile karşılaştırılan diğer modellerden daha başarılı tahmin performansına sahip olduğunu göstermiştir.

Keywords

KOBİ, ÇKKV, OCRA, ekonomi, derin öğrenme, makine öğrenmesi

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