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All Journal Jurnal Teknik Elektro ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Jurnal Teknik Elektro
PERMANA, ZENDI ZAKARIA RAGA
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Electrical & Electronics Engineering Engineering

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Echocardiogram Image Quality Enhancement using Upsampling and Histogram Matching Methods Permana, Zendi Zakaria Raga; Puspasari, Ira
Jurnal Teknik Elektro Vol 14, No 2 (2022): Jurnal Teknik Elektro
Publisher : Jurusan Teknik Elektro, Fakultas Teknik, Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jte.v14i2.42081

Abstract

The prevalence of heart disease has been increasing in the last ten years. One of the cardiac diagnostic tools is echocardiography. Echocardiogram medical images provide essential information, including shape, size, pumping capacity, heart function abnormalities, and location of heart damage, but echocardiogram images have high noise content and poor contrast, as well as limitations due to differences in anatomy or body mass. This will affect the reading results of patient diagnosis. Therefore, image quality improvement is needed by removing noise and increasing image contrast. This research has improved image quality using a method with low mathematical complexity and a fast computational process. The method used is the Upsampling method to generate a reference image. The quality of the image produced was the Nearest Neighbor upsampling method: 2.8 dB, Bi-linear Interpolation: 2.78 dB, and Bi-cubic Interpolation: 2.73 dB. Furthermore, the image with the highest SNR value is processed with Histogram Matching to accelerate improving image quality. The Histogram Matching image increases quality by more than 50% with a SSIM value of 0.54. The required computational process to apply this method to each medical image has an average duration of 0.4 s. This result provides a higher value than several methods using linear scaling and speckle reducing.
Prediksi Jarak Bola pada Citra Kamera Katadioptrik menggunakan metode Artifical Neural Network PERMANA, ZENDI ZAKARIA RAGA; RASMANA, SUSIJANTO TRI; PUSPASARI, IRA
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 9, No 2: Published April 2021
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v9i2.279

Abstract

ABSTRAKSaat ini, kecerdasan buatan memungkinkan untuk dikembangkan dalam dunia robotika, khususnya untuk pengaturan gerakan robot berdasarkan pengolahan citra. Penelitian ini mengembangkan sebuah mobile robot yang dilengkapi dengan kamera katadioptrik dengan sudut pandang 3600. Citra yang didapatkan, dikonversi dari RGB menjadi HSV. Selanjutnya disesuaikan dengan proses morfologi. Nilai jarak yang terbaca oleh kamera (piksel) dengan jarak sebenarnya (cm) dihitung menggunakan Euclidean Distance. Nilai ini sebagai ekstraksi ciri data jarak yang dilatihkan pada sistem. Sistem yang dibuat pada penelitian ini memiliki iterasi sebanyak 1.000.000, dengan tingkat kelinieran R2=0.9982 dan keakuratan prediksi sebesar 99,03%.Kata kunci: Robot, HSV, Euclidean Distance, Kamera katadioptrik, Artifical Neural NetworkABSTRACTRecently, artificial intelligence is possible to be developed in robotic, specifically for robot movements control based on image processing. This research develops a mobile robot with a 3600 perspective catadioptric camera is equipped. The camera captured images were converting from RGB to HSV. Furthermore, it adapted to the morphological process. The distance value read by the camera (pixels) to the actual distance (cm) is measured using Euclidean Distance. This value is a feature extraction of distance data that has training on the system. The system built in this study has 1,000,000 iterations, with a linearity level of R2 = 0.9982 and prediction accuracy of 99.03%.Keywords: Robot, HSV, Euclidean Distance, Catadioptric Camera, Artifical Neural Network
Externally Validated Deep Learning Model for Multi-Disease Classification of Chest X-Rays Kusumawati, Weny Indah; Permana, Zendi Zakaria Raga; Puspasari, Ira
Jurnal Teknik Elektro Vol. 17 No. 2 (2025)
Publisher : LPPM Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jte.v17i2.29892

Abstract

Accurate classification of chest X-ray (CXR) images is vital for early detection of thoracic diseases such as COVID-19, Tuberculosis, and Pneumonia, particularly in regions with limited radiological expertise. While deep learning has shown promise in CXR interpretation, many existing models rely solely on internal datasets, risking overfitting and poor generalizability. Furthermore, inadequate tuning of network architectures may limit robustness across varied imaging conditions. This study presents an externally validated deep learning framework based on Convolutional Neural Networks (CNNs) for multi-disease CXR classification. This study compared a baseline CNN with two convolutional layers against a tuned architecture with three layers across multiple image resolutions (64×64, 112×112, 224×224). The proposed model employs transfer learning with a pre-trained CNN, fine-tuned for four-class classification using a softmax output layer. Training was performed with the Adam optimizer (learning rate: 0.0001, batch size: 32) and categorical cross-entropy loss, for up to 50 epochs with early stopping. Internal validation showed the tuned model outperformed the baseline, achieving 0.97 accuracy and an F1-score of 0.89. External validation confirmed superior generalizability, with the tuned model attaining an F1-score of 0.83 and an AUC of 0.97 at 112×112 resolution, compared to the baseline’s F1-score of 0.79 and AUC of 0.94. These results highlight the potential of optimized CNN architectures as reliable, scalable tools for radiological decision support in resource-limited healthcare systems. Future work will incorporate explainable AI methods and real-world clinical validation to ensure safe, interpretable deployment.
Co-Authors Ira Puspasari Susijanto Tri Rasmana Weny Indah Kusumawati