Rancang Bangun Sistem Deteksi Dini Dan Prediksi Osteoarthritis Lutut Menggunakan Convolutional Neural Network Berbentuk Aplikasi Berbasis Web

Nashir, Muhammad Sulthon (2022) Rancang Bangun Sistem Deteksi Dini Dan Prediksi Osteoarthritis Lutut Menggunakan Convolutional Neural Network Berbentuk Aplikasi Berbasis Web. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Osteoarthritis adalah penyakit yang menyebabkan pembengkakan, pelunakan, radang, kekakuan, pada satu sendi atau lebih. Osteoarthritis lutut berarti osteoarthritis yang terjadi pada sendi lutut manusia. Osteoarthritis lebih banyak dijumpai pada lanjut usia dan secara umum semakin parah seiring bertambahnya usia. Salah satu faktor yang mempengaruhi kondisi arthritis seseorang adalah kapan perawatan osteoarthritis diberikan. Semakin dini perawatan diberikan, semakin bagus kondisi osteoarthritis seseorang. Jika perawatan telat dapat terjadi dampak yang tidak dapat disembuhkan, seperti pengikisan tulang, penyatuan tulang, bahkan penghalangan pergerakan total pada sendi yang terkena osteoarthritis. Oleh karena itu, pada Tugas Akhir ini dikembangkan program untuk deteksi dini osteoarthritis menggunakan convolutional neural network. Convolutional neural network dipilih karena cara kerjanya yang dapat mengurangi jumlah parameter yang sangat banyak dalam suatu gambar. Program ini membaca foto sinar-X pasien melalui aplikasi berbasis web. Setelah diunggah, foto akan dibandingkan dengan model yang didapatkan dari sistem convulated neural network. Hasil akan ditampilkan pada aplikasi web. Hasil penelitian berupa satu model dengan tingkat akurasi 83.8% yang melakukan deteksi pada aplikasi web. Pemilihan aplikasi web sebagai media tatap muka memudahkan pengguna dalam melakukan deteksi dan prediksi, serta memudahkan penyimpanan data deteksi.
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Osteoarthritis is a disorder that causes swelling, tenderness, inflammation, and stiffness in one or more joints. Knee osteoarthritis is osteoarthritis affecting the knee joint. Osteoarthritis is more common in older people and typically worsens with age. The key factor in how much osteoarthritis affects someone is how early osteoarthritis is given. The faster treatment is given, the better the result is. If treatment is given late, an unrecoverable side effect could happen such as bone scraping, calcification, bone locking, or even total paralyzation. From the cause stated above, this Final Project is developed to implement the usage of a convolutional neural network for the early detection of knee osteoarthritis. This system will make it possible in achieving that by removing any outside factors such as human errors and difference between diagnosis from a different medical expert by using the system’s deep learning and convolutional neural network algorithm. Convolutional neural network is chosen for its ability to greatly reduce parameters inside an image data in an array. This system will read uploaded knee joint sinar-X image using the web application. After successfully uploaded, images then will be processed and compared with a model created from convolutional neural network algorithm dataset training to detect early sign of osteoarthritis. The result will be displayed on the web application. The result of this research is a model for detecting and predicting knee osteoarthritis in human with 83.8% accuracy. This model, coupled with a web application interface, ease the detection and prediction du

Item Type:	Thesis (Other)
Additional Information:	RSTI 006.3 Nas r-1 2022
Uncontrolled Keywords:	Lutut, Osteoarthritis, Convoluted Neural Network, Deteksi Dini, Aplikasi Web, Knee, Osteoarthritis, Convulated Neural Network, Early Detection, Web Application
Subjects:	Q Science > QA Mathematics > QA76.87 Neural networks (Computer Science)
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Information Technology > 59201-(S1) Undergraduate Thesis
Depositing User:	Mr. Marsudiyana -
Date Deposited:	22 Apr 2026 06:02
Last Modified:	22 Apr 2026 06:02
URI:	http://repository.its.ac.id/id/eprint/132861

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