Sanayi Üretim Endeksinin Kural Tabanlı Sınıflandırma Algoritmaları ile Analizi

Öz

Sanayi üretim endeksi, bir ülkenin ekonomik büyümesi ve kalkınma dinamikleri hakkında stratejik bilgiler sunan en temel makroekonomik göstergelerden biridir. Bu çalışmanın temel amacı, Türkiye’de Temmuz 2013 ile Ocak 2026 dönemini kapsayan sanayi üretim endeksindeki değişimi, kural tabanlı öğrenme algoritmaları kullanarak başarılı bir şekilde sınıflandırmaktır. Analiz sürecinde 41 farklı ekonomik değişken kullanılmış; RIPPER, MOEFC ve NNge algoritmaları yardımıyla farklı Hold-Out oranları (%50, %66, %70, %80) ile analizler yapılmıştır. Araştırma sonucunda elde edilen bulgular, kural tabanlı algoritmaların Türkiye’nin sanayi üretim endeksindeki karmaşık ve doğrusal olmayan ilişkiler için sınıflandırmada oldukça başarılı olduğunu ortaya koymuştur. Özellikle MOEFC algoritması, tüm test senaryolarında en istikrarlı performansı sergileyerek %96,0’lık bir doğru sınıflandırma oranına ve 0,9610 ROC eğrisi değerine ulaşmıştır. Elde edilen sonuçlar, kural tabanlı yaklaşımların sanayi üretimi gibi dinamik süreçlerin sınıflandırılmasında politika yapıcılar ve araştırmacılar için güvenilir, yorumlanabilir ve güçlü bir alternatif sunduğunu ortaya koymaktadır.

Anahtar Kelimeler

• Sanayi Üretim Endeksi
• Kural Tabanlı Öğrenme
• MOEFC
• Sınıflandırma

Destekleyen Kurum

Yok

Etik Beyan

Etik Kurul onayına gerek olmayan çalışmadır.

Teşekkür

yok

Analysis of Industrial Production Index with Rule-Based Classification Algorithms

Öz

The industrial production index is one of the most fundamental macroeconomic indicators, offering strategic insights into a country's economic growth and development dynamics. The primary objective of this study is to successfully classify the changes in Türkiye's industrial production index covering the period from July 2013 to January 2026, using rule-based learning algorithms. During the analysis process, 41 different economic variables were utilized, and evaluations were conducted using RIPPER, MOEFC, and NNge algorithms across various Hold-Out ratios (50%, 66%, 70%, and 80%). The findings reveal that rule-based algorithms are highly effective in classifying the complex and non-linear relationships inherent in Turkey's industrial production index. Notably, the MOEFC algorithm exhibited the most stable performance across all test scenarios, achieving an impressive classification accuracy of 96.0% and a ROC curve value of 0.9610. These results demonstrate that rule-based approaches provide a reliable, interpretable, and robust alternative for policymakers and researchers in classifying dynamic processes such as industrial production.

Anahtar Kelimeler

• Industrial Production Index
• Rule-Based Learning
• MOEFC
• Classification

Destekleyen Kurum

No

Etik Beyan

This study does not require Ethics Committee approval.

Teşekkür

no

Kaynakça

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