Saatlik Asgari Ücrete Yönelik Bireysel Tutumların Makine ve Derin Öğrenme ile Sınıflandırılması

Abstract

Bu çalışma, Türkiye’de saatlik asgari ücret sistemine geçişin çalışanlar nezdindeki kabul düzeyini anlamak amacıyla, birey temelli tutumların makine öğrenmesi ve derin öğrenme algoritmalarıyla sınıflandırılmasını hedeflemektedir. Veri seti, Elazığ ilinde aktif olarak çalışan 343 bireyden yapılandırılmış anket formu yoluyla elde edilmiştir. Anket kapsamında sosyodemografik, psikolojik ve davranışsal özelliklere dair çok sayıda değişken toplanmış; katılımcıların saatlik asgari ücret sistemine ilişkin “evet” ya da “hayır” yönündeki tutumları hedef değişken olarak belirlenmiştir. Bu tutumlar, Lojistik Regresyon, Rastgele Orman, XGBoost, CatBoost, LogisticGAM ve TabNet modelleriyle sınıflandırılmış; hiperparametre optimizasyonları GridSearchCV ve Optuna yöntemleriyle gerçekleştirilmiştir. Test verisinde en yüksek doğruluk (%94) ve AUC (%99.7) performansı TabNet modeli tarafından elde edilmiştir. Ayrıca, LogisticGAM modeli doğrusal olmayan yapılar üzerindeki başarısıyla dikkat çekmiştir (%84 doğruluk). En iyi sınıflandırma sonucunu veren TabNet modelinin sınıf bazlı öznitelik katkı analizleri ile yorumlanarak, çalışanların ücret algısı, iş tatmini, performansa dayalı ücretlendirme görüşü ve esnek çalışmaya yönelik eğilimlerinin tutumları üzerindeki etkileri açıklanmıştır. Çalışma, Türkiye’de saatlik asgari ücret bağlamında yapay zekâ temelli ilk birey düzeyli sınıflandırma uygulaması olma niteliği taşımakta; sosyal politika analizine açıklanabilir yapay zekâ modelleri aracılığıyla özgün bir metodolojik katkı sunmaktadır.

Keywords

• Saatlik Asgari Ücret
• Bireysel Tutumlar
• Makine Öğrenmesi
• Derin Öğrenme
• Sosyal Politika

The Classification of Individual Attitudes Toward the Hourly Minimum Wage Using Machine and Deep Learning

Abstract

This study aims to understand the level of acceptance of the transition to an hourly minimum wage system in Turkey among workers by classifying individual-based attitudes using machine learning and deep learning algorithms. The data set was obtained through structured survey forms from 343 individuals actively working in Elazığ province. The survey collected numerous variables related to sociodemographic, psychological, and behavioral characteristics; participants' attitudes toward the hourly minimum wage system, categorized as “yes” or “no,” were designated as the target variable. These attitudes were classified using Logistic Regression, Random Forest, XGBoost, CatBoost, LogisticGAM, and TabNet models; hyperparameter optimizations were performed using GridSearchCV and Optuna methods. The highest accuracy (94%) and AUC (99.7%) performance in the test data was achieved by the TabNet model. Additionally, the LogisticGAM model stood out for its success on non-linear structures (84% accuracy). The class-based feature contribution analysis of the TabNet model, which yielded the best classification result, was interpreted to explain the effects of employees' perceptions of wages, job satisfaction, views on performance-based pay, and tendencies toward flexible work on their attitudes. The study represents the first individual-level classification application based on artificial intelligence in the context of hourly minimum wage in Turkey, offering a unique methodological contribution to social policy analysis through explainable artificial intelligence models.

Keywords

• Hourly Minimum Wage
• Individual Attitudes
• Machine Learning
• Deep Learning
• Social Policy

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