MANAN, AOUDIN DAFFA CAHYA PRATAMA (2025) KLASIFIKASI PENYAKIT MENGGUNAKAN HYBRID RANDOM FOREST DAN AUTOENCODER PADA DATA RADIOLOGI BERBASIS CITRA. S1 thesis, Universitas Mercu Buana Jakarta - Menteng.
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Abstract
Citra radiologi, seperti X-ray, MRI, dan CT scan, menjadi salah satu alat penting dalam mendukung diagnosis penyakit. Namun, kompleksitas data citra medis sering kali menjadi tantangan dalam membangun model klasifikasi yang akurat dan andal. Penelitian ini bertujuan untuk mengembangkan model hybrid yang menggabungkan Autoencoder dan Random Forest untuk klasifikasi penyakit berdasarkan citra radiologi. Autoencoder digunakan untuk mengekstraksi fitur mendalam dari citra, sementara Random Forest berperan sebagai algoritma klasifikasi yang efisien. Tahapan penelitian meliputi preprocessing data citra radiologi, pengembangan dan pelatihan model Autoencoder untuk menghasilkan representasi fitur, serta implementasi Random Forest sebagai model klasifikasi. Penelitian ini juga melakukan evaluasi terhadap model yang dikembangkan dengan membandingkan performanya dengan metode baseline seperti CNN dan Random Forest tunggal menggunakan metrik akurasi, sensitivitas, dan spesifisitas. Penelitian ini diharapkan dapat memberikan kontribusi pada pengembangan teknologi kecerdasan buatan untuk mendukung diagnosis penyakit berbasis citra medis. Model yang diusulkan diharapkan mampu menjadi solusi yang efisien dan andal dalam pengolahan data citra medis untuk keperluan diagnosis di bidang kesehatan. Radiology images, such as X-rays, MRIs, and CT scans, are essential tools in supporting disease diagnosis. However, the complexity of medical image data often poses challenges in building accurate and reliable classification models. This study aims to develop a hybrid model that combines an Autoencoder and Random Forest for disease classification based on radiology images. The Autoencoder is used to extract deep features from the images, while the Random Forest serves as an efficient classification algorithm. The research stages include preprocessing radiology image data, developing and training the Autoencoder model to generate feature representations, and implementing Random Forest as the classification model. This study also evaluates the proposed model by comparing its performance with baseline methods such as CNNs and standalone Random Forest models, using metrics like accuracy, sensitivity, and specificity. This research is expected to contribute to the development of artificial intelligence technology to support disease diagnosis based on medical images. The proposed model is anticipated to provide an efficient and reliable solution for processing medical image data for diagnostic purposes in the healthcare field.
| Item Type: | Thesis (S1) |
|---|---|
| NIM/NIDN Creators: | 41521010010 |
| Uncontrolled Keywords: | klasifikasi penyakit, Autoencoder, Random Forest, citra radiologi, model hybrid. disease classification, Autoencoder, Random Forest, radiology images, hybrid model. |
| Subjects: | 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika |
| Divisions: | Fakultas Ilmu Komputer > Informatika |
| Depositing User: | NAIMAH NUR ISLAMIDIYANAH |
| Date Deposited: | 04 Aug 2025 02:44 |
| Last Modified: | 04 Aug 2025 02:44 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/96506 |
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