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Dergiler / Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi / 2026 / Cilt: 16 Sayı: 1

Regresyon Analizi ile MİB Performans Puanını Tahmin Etme

Predicting CPU Performance Score with Regression Analysis

Güney Kaya(CELAL BAYAR UNIVERSITY, HASAN FERDİ TURGUTLU FACULTY OF TECHNOLOGY, DEPARTMENT OF SOFTWARE ENGINEERING)0009-0007-7687-7350
Emre Şen(CELAL BAYAR UNIVERSITY, HASAN FERDİ TURGUTLU FACULTY OF TECHNOLOGY, DEPARTMENT OF SOFTWARE ENGINEERING)0009-0006-8640-8374
Osman Altay(CELAL BAYAR UNIVERSITY, HASAN FERDİ TURGUTLU FACULTY OF TECHNOLOGY, DEPARTMENT OF SOFTWARE ENGINEERING)0000-0003-3989-2432
Sayfa
1–11
Yayın Tarihi
2025-03-26
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Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Sonuç olarak, belirleme katsayısı en yüksek olan 0,976 ile YSA'dır ve onu 0,958 ile GAR takip etmektedir.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Bu araştırmanın amacı, bir MİB'in performans puanını özelliklerine göre tahmin etmek için regresyon analizini kullanmaktır.
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Temel Bulgular & Veri:Bir bilgisayar seçerken MİB performansının yanı sıra sistem yapılandırması ve tasarımının değerlendirilmesi son derece önemlidir.
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Sonuç & Katkı:Sonuç olarak, belirleme katsayısı en yüksek olan 0,976 ile YSA'dır ve onu 0,958 ile GAR takip etmektedir.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Regresyon Analizi · Makine Öğrenimi · Veri Madenciliği

Özet

Bu araştırmanın amacı, bir MİB'in performans puanını özelliklerine göre tahmin etmek için regresyon analizini kullanmaktır. Bir bilgisayar seçerken MİB performansının yanı sıra sistem yapılandırması ve tasarımının değerlendirilmesi son derece önemlidir. Destek Vektör Regresyonu (DVR), Rastgele Orman Regresyonu (ROR), Çoklu Doğrusal Regresyon (ÇDR), Gradyan Arttırma Regresyonu (GAR) ve Yapay Sinir Ağları Regresyonu (YSA), MİB'in performans puanını tahmin etmek için kullanılmıştır. Algoritmaları test etmek için veri setinin yüzde 30'u test verileri, yüzde 70'i ise eğitim verileri olarak rastgele ayrıldı. Sonuç olarak, belirleme katsayısı en yüksek olan 0,976 ile YSA'dır ve onu 0,958 ile GAR takip etmektedir. ÇDR, ROR ve DVR algoritmaları sırasıyla 0,952, 0,934 ve 0,865 R-kare puanına sahiptir.

Abstract

The purpose of this research is to use regression analysis to predict a CPU's performance score based on its features. CPU performance is incredibly important to evaluate when choosing a computer, along with system configuration and design. Support Vector Regression (SVR), Random Forest Regression (RFR), Multiple Linear Regression (MLR), Gradient Boosting Regression (GBR) and Neural Network Regression (NNR) are used to estimate the CPU's performance score. To test the algorithms, 30 percent of the data set was selected as test data and 70 percent as training data, separated randomly. As a result, the NNR has the highest of the coefficient of determination score which is 0.976, followed by GBR, 0.958. MLR, RFR and SVR algorithms have the R-squared score of 0.952, 0.934 and 0.865, respectively.

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Bu Makaleye Atıf Yap

Kaya, G., Şen, E., Altay, O. (2025). Regresyon Analizi ile MİB Performans Puanını Tahmin Etme. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, Cilt: 16 Sayı: 1, 1–11. https://doi.org/10.24012/dumf.1493049

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