CD Skripsi

Klasifikasi Jenis Jerawat Berbasis Cnn Menggunakan Inception-V3 Transfer Learning Untuk Merekomendasikan Jenis Perawatannya

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ABSTRACT
Acne is a global skin problem with high prevalence, and in Indonesia, the increasing trend is significant, exacerbated by limited access to professional diagnosis. Therefore, this research aims to develop an accurate automated acne classification and recommendation system. The method used is a Convolutional Neural Network (CNN) with Inception-V3-based transfer learning for classifying five types of acne (blackheads, whiteheads, papules, pustules, and nodules) based on skin images, along with a rule-based system for treatment recommendations. The results show that the CNN model achieved a training accuracy of 96.98% and a validation accuracy of 89.08%, with an average class accuracy of 95.59%, demonstrating good capability in identifying acne. The integration with the rule-based recommendation system was also successful, with a 100% suitability rate. In conclusion, this system provides a comprehensive solution for acne diagnosis and treatment recommendations and contributes to the development of deep learning methods in medical imaging.
Kata Kunci: Convolutional Neural Network, Inception-V3, acne classification, rule-based recommendation system, transfer learning.

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

2107112730

Penerbit

Pekanbaru : Universitas Riau – F.TEKNIK – INFORMATIKA., 2025

Deskripsi Fisik

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Bahasa

Indonesia

ISBN/ISSN

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Klasifikasi

2107112730

Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

TEKNIK INFORMATIKA

Info Detail Spesifik

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Pernyataan Tanggungjawab

daus

Versi lain/terkait

Lampiran Berkas

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