Skripsi

PERBANDINGAN PERFORMA ALGORITMA DECISION TREE C4.5 DAN RANDOM FOREST UNTUK DETEKSI PENYAKIT DIABETES

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Penilaian

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Diabetes is a chronic disease with a continuously increasing global prevalence. Early detection poses a major challenge because symptoms often appear only when the condition is already severe. The classification of diabetes plays a crucial role in recognizing the disease early to allow for faster and more accurate interventions. According to data from the World Health Organization in 2020, over the past three decades, the prevalence of diabetes has significantly increased in various countries. This research is conducted to compare the performance of two classification methods, that are Decision Tree C4.5 and Random Forest, in classifying diabetes disease based on patients’ health data records. This research includes the design stage of classification system, software implementation, and the performance evaluation of each method. the dataset used is from processed health data records, even though the quality is considered less than ideal. The test results show that the Random Forest algorithm achieved the highest accuracy of 89.61%, while the C4.5 Decision Tree algorithm reached an accuracy of 88.63%. In addition, the Random Forest algorithm also demonstrated a higher precision compared to the C4.5 Decision Tree. This is due to Random Forest using an ensemble approach that employs many Decision Trees, resulting in more accurate predictions.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507003755	T176905	T1769052025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1769052025

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xv, 160 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

Text

Media Type

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Carrier Type

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Edition

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Subject(s)

Teknik Informatika

Specific Detail Info

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Statement of Responsibility

MI

Other version/related

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