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Wirawan Setyo Prakoso
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Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Control & Systems Engineering

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KLASIFIKASI CITRA ULTRASONOGRAFI UNTUK DETEKSI NODUL TIROID BERDASARKAN ECHOGENICITY Wirawan Setyo Prakoso; Alva Rischa Qhisthana Pratika
Hexagon Vol 6 No 1 (2025): HEXAGON - Edisi 11
Publisher : Fakultas Teknologi Lingkungan dan Mineral - Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36761/hexagon.v6i1.5052

Abstract

One of the important features for diagnosing malignant thyroid nodules is based on their echogenicity characteristics, namely the grey intensity of each nodule. Therefore, computer-aided diagnosis (CADx) is required to select important features and classify nodules that are more likely to be malignant or benign and the form of treatment is known. This study proposes classifying thyroid nodules based on texture features from histogram, GLCM and GLRLM into 4 classes: anechoic, hyperechoic, hypoechoic and very hypoechoic. Ultrasound is the best way to filter information about the characteristics of the degree of malignancy of thyroid nodules used by doctors. The doctor's decision takes a long time. Early detection is necessary, so that doctors can provide treatment and prevention quickly. This study uses machine learning for early detection of the level of malignancy of thyroid nodules using ultrasound images with a dataset obtained from Sardjito Hospital, Yogyakarta Radiology Department. The results showed that the Linear SVM method is the best method to classify the level of malignancy of thyroid nodules based on the dataset which has been divided into 4 classes with 64 features resulting in an accuracy of 68.3%, a positive predictive value of 70% and a sensitivity value of 68%. Keywords: Ultrasound, Thyroid nodules, Classification, Early detection of disease, Feature extraction, Machine learning techniques, SVM echogenicity classification.
Pendeteksian Glaukoma Menggunakan Artificial Neural Network dengan Analisis Optic Cup dan Optic Disk pada Citra Fundus Retina Alva Rischa Qhisthana Pratika; Wirawan Setyo Prakoso
Hexagon Vol 6 No 1 (2025): HEXAGON - Edisi 11
Publisher : Fakultas Teknologi Lingkungan dan Mineral - Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36761/hexagon.v6i1.5099

Abstract

Glaukoma merupakan penyakit mata dengan adanya peningkatan tekanan intraokular yang menyebabkan kerusakan saraf optik dan dapat menyebabkan kebutaan secara permanen. Untuk mendeteksi glaukoma, dokter mata umumnya menghitung rasio Cup to Disc Ratio (CDR) melalui analisis citra fundus retina secara manual. Metode ini memerlukan waktu lama dan bergantung pada keahlian dokter, sehingga kurang efektif dan kurang akurat. Oleh karena itu, diperlukan sistem otomatis yang dapat mendeteksi glaukoma dengan akurat dan efisien. Penelitian ini bertujuan untuk menyebarkan jumlah dan kombinasi fitur ekstraksi terbaik dalam mendeteksi glaukoma, dengan menggunakan fitur-fitur seperti Rim to Disc Ratio (RDR), Cup to Disk Ratio (CDR), Vertical Cup to Disc Ratio (VCDR), Horizontal Cup to Disc Ratio (HCDR), dan Horizontal to Vertical CDR (HV CDR), serta mengimplementasikan metode klasifikasi Artificial Neural Network (ANN) Back Propagation dengan jumlah data inputan 160 citra, 80 citra normal dan 80 citra glaukoma. Pada penelitian ini menghasilkan akurasi sebesar 97.50% dengan kombinasi fitur ekstraksi ciri CDR dan VCDR
Co-Authors Alva Rischa Qhisthana Pratika