TY  - JOUR
T1  - Türkiye’nin İllere Göre Konut Satış Miktarının Sinir Ağları Yöntemiyle İncelenmesi
TT  - Analysis of Housing Sales Quantity in Turkey by Provinces Using Neural Network Method
AU  - Ünal, Şeyma Nur
PY  - 2025
DA  - October
Y2  - 2025
DO  - 10.17828/yalovasosbil.1747075
JF  - Yalova Sosyal Bilimler Dergisi
JO  - YSBD
PB  - Yalova Üniversitesi
WT  - DergiPark
SN  - 2146-1333
SP  - 229
EP  - 245
VL  - 15
IS  - 2
LA  - tr
AB  - Konut sektörü bir ülkenin sosyal ve ekonomik kalkınmasında çok önemli bir rol oynamaktadır. Sektör, istihdam, tasarruf, yatırım ve işgücü verimliliği gibi başlıca makroekonomik göstergeler üzerindeki etkisiyle ekonomik büyüme ve kalkınmaya katkıda bulunmaktadır. Bilişim teknolojisinin ilerlemesiyle birlikte, makine öğreniminin konut sektöründe, özellikle de konut satış analizi için uygulanması giderek daha önemli hale gelmiştir. Bu araştırma illere göre konut satış miktarını Yapay Sinir Ağları (YSA) yöntemiyle analiz etmeyi amaçlamaktadır. Kullanılan veriler, sanayi üretim endeksi, tüketici güven endeksi, inşaat güven endeksi, ekonomik güven endeksi, döviz kurudur ve illere göre konut satış miktarıdır. Algoritmaların analizi, her iki algoritmanın performans test sonuçlarının Ortalama Karesel Hata (OKH), Kök Ortalama Karesel Hata (KOKH) ve R-Kare (R2) gibi regresyon modelleri için performans metrikleri kullanılarak karşılaştırılmasıyla gerçekleştirilir. Ek olarak, bu araştırma eğitim, doğrulama ve test verileri arasında hangi veri oranının en iyi sonuçları verdiğini analiz eder. Araştırma bulguları, OKH 0.0011 ve KOKH 0.0331 değerleri performans ölçütü ileri beslemeli sinir ağının 10-2 ağa sahip modelin YSA içinde en iyi performansı ürettiğini göstermektedir.
KW  - Makro İktisat
KW  - Konut Satışı
KW  - Yapay Zekâ
KW  - Makine Öğrenimi
KW  - Yapay Sinir Ağları
N2  - The housing sector plays a very important role in the social and economic development of a country.The sector contributes to economic growth and development through its impact on major macroeconomic indicators such as employment, savings, investment and labor productivity.With the advancement of information technology, the application of machine learning in the housing industry, especially for housing sales analysis, has become increasingly important.This research aims to analyze the housing sales amount by province using Artificial Neural Networks (ANN) method.The data used are industrial production index, consumer confidence index, construction confidence index, economic confidence index, exchange rate and housing sales amount by province.Analysis of the algorithms is performed by comparing the performance test results of both algorithms using performance metrics for regression models such as Mean Square Error (MSE), Root Mean Square Error (RMSE) and R-Square (R2).Additionally, this research analyzes which data ratio among training, validation, and test data yields the best results.Research findings show that the performance criterion of feed forward neural network with 10-2 network produces the best performance within the ANN with MSE 0.0011 and RMSE 0.0331 values.
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UR  - https://doi.org/10.17828/yalovasosbil.1747075
L1  - https://dergipark.org.tr/tr/download/article-file/5074283
ER  -