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All Journal Jurnal Ilmu Dasar Jurnal Fisika FLUX
Arsyad, Ya' Muhammad
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Control & Systems Engineering Earth & Planetary Sciences Education Materials Science & Nanotechnology Mathematics Physics

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Classification of Malignancy of Lung Cancer Using Backpropagation Algorithm on CT-Scan Images Putri, Evi Pania; Nurhasanah, Nurhasanah; Wahyuni, Dwiria; Hasanuddin, Hasanuddin; Adriat, Riza; Arsyad, Ya' Muhammad
Jurnal ILMU DASAR Vol 25 No 2 (2024)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/jid.v25i2.39054

Abstract

In this study, we investigate the classification of lung cancer CT scan images based on malignancy level using a backpropagation artificial neural network (ANN). Lung cancer is a deadly disease characterized by the growth of abnormal lung cells. The proposed method involves preprocessing to enhance image quality, followed by feature extraction using the Gray Level Co-occurrence Matrix (GLCM) method with angle variations of 0°, 45°, 90°, 135°, and d=1. The extracted features include energy, contrast, correlation, and homogeneity. The energy value range in malignant cancer is 0.27 to 0.81, while in benign cancer it is 0.26 to 0.73. The contrast in benign cancer ranges from 1.38 to 11.87, while in malignant cancer it is 1.47 to 13.67. The image correlation for malignant cancer is between 0.63 to 0.94, while for benign cancer it is 0.69 to 0.96. Homogeneity in malignant cancer has a value range between 0.67 to 0.91, while in benign cancer it ranges from 0.70 to 0.92. The classification of lung cancer malignancy is restricted to benign and malignant levels using a network architecture of [4 10 2], maximum iteration of 100000, and learning rate of 0.001. The accuracy of the testing data from the ANN is between 90% and 100%. These results demonstrate the effectiveness of the GLCM method and backpropagation algorithm in accurately classifying the malignancy level of lung cancer, which could aid in the early detection and treatment of the disease.
Quality Study of Activated Carbon/TiO2 Composite Based on Activated Carbon Granule Size Nurhanisa, Mega; Wahyuni, Dwiria; Arsyad, Ya' Muhammad
Jurnal Fisika Flux: Jurnal Ilmiah Fisika FMIPA Universitas Lambung Mangkurat Vol 22, No 1 (2025): Jurnal Fisika Flux: Jurnal Ilmiah Fisika FMIPA Universitas Lambung Mangkurat
Publisher : Lambung Mangkurat University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/flux.v21i3.17999

Abstract

Activated carbon (AC) composited with TiO2 photocatalyst can be used as a material that can purify wastewater. This research aims to obtain the optimum particle size of AC as a buffer for AC. The work began by making AC from oil palm empty fruit bunches (OPEFB) waste with various particle sizes, namely 50 mesh, 100 mesh, 150 mesh, and 200 mesh. AC was synthesized by carbonizing the raw materials at 500 °C and then activated using a microwave oven at 500 watts for 15 minutes. Furthermore, AC/TiO2 composites were synthesized using a microwave oven under similar conditions. Spectroscopy Electron Microscopy (SEM) characterization was done to see the morphology of AC and AC/TiO2 composites. To determine the performance of AC and AC/TiO2 composites, the degradation process of methylene blue (MB) solution was carried out. Characterization with a UV-Vis spectrophotometer was done as a quantitative method to measure the level of MB degradation. The results of MB degradation for 6 hours of irradiation showed that 200 mesh particles achieved the highest efficiency of 86.19%. Thus, using TiO2 has been shown to improve the performance of AC, with those of 200 mesh degrading MB the most.
Co-Authors Dwiria Wahyuni Mega Nurhanisa Nurhasanah Nurhasanah Putri, Evi Pania Riza Adriat Siti Helmyati