Deteksi osteoporosis pada citra radiograf panoramik dental menggunakan algoritme J48 dan Learning Vector Quantization

Jurnal Teknologi dan Sistem Komputer

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Title Deteksi osteoporosis pada citra radiograf panoramik dental menggunakan algoritme J48 dan Learning Vector Quantization
Osteoporosis detection on the dental panoramic radiographic images using J48 algorithm and Learning Vector Quantization
 
Creator Sela, Enny Itje
 
Subject osteoporosis; dental panoramik; indeks radio morfometri; analisis tekstur
osteoporosis; radio morphometry index; texture analyis; J48; LVQ
 
Description Osteoporosis merupakan salah satu jenis penyakit yang tidak mudah terdeteksi. Penyakit ini dapat menyebabkan patah tulang bagi penderitanya. Deteksi dini osteoporosis sangat penting untuk mencegah patah tulang. Penelitian ini bertujuan untuk melakukan deteksi osteoporosis melalui fitur-fitur yang diekstrak pada tulang kortikal dan trabekula pada citra panoramik dental. Hasil ekstraksi fitur tersebut dilatih menggunakan jaringan syaraf tiruan. Berdasarkan hasil penelitian, fitur yang dominan untuk deteksi osteoporosis adalah fitur indeks morfometri radio (IRM) dan morfologi. Tingkat akurasi pengujian menggunakan LVQ dapat mendeteksi penyakit osteoporosis, dengan tingkat akurasi mencapai 83,33 %. Sedangkan nilai sensitivitas sebesar 78.57 % dan spesifisitas mencapai 100,00 %.
Osteoporosis is one type of disease that is not easily detected. This disease can cause fractures for the sufferer. Early detection of osteoporosis is crucial to prevent fractures. This study aims to detect osteoporosis through features extracted from cortical bone and trabeculae in dental panoramic images. The results of the selected feature extraction are trained using an artificial neural network. Based on the study results, the dominant features for osteoporosis detection are radio morphometric index and morphological features. The accuracy, sensitivity, and specificity of the J48 and Learning Vector Quantization (LVQ) are 83.88 %, 78.57 %, and 100 %, respectively.
 
Publisher Departemen Teknik Komputer, Fakultas Teknik, Universitas Diponegoro
 
Date 2021-10-30
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion

 
Identifier https://jtsiskom.undip.ac.id/index.php/jtsiskom/article/view/14197
10.14710/jtsiskom.2021.14197
 
Source Jurnal Teknologi dan Sistem Komputer; 2021: Publication In-Press
Jurnal Teknologi dan Sistem Komputer; 2021: Publication In-Press
2338-0403
 
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Rights Copyright (c) 2021 The Authors. Published by Department of Computer Engineering, Universitas Diponegoro
https://creativecommons.org/licenses/by-sa/4.0
 

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