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All Journal Jurnal Ilmu Dasar Newton-Maxwell Journal of Physics
Arsyad, Ya' Muhammad
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Astronomy Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Mathematics Mechanical Engineering 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.
Dependence of Crystallinity and Crystallite Size of Hydroxyapatite from Chicken Eggshell on Calcination Time : A Comparative Study on Scherrer Approach Arsyad, Ya' Muhammad; Mega Nurhanisa; Elsa Narulita; Ayunda Dwi Handayani; Frinelda Rehulina Barus; Tri Rahma Febrianti Maharani; Hilyana Agis Risla; Latifah Tri Amanda; Delia Amanda; Zahraini Tasya Siregar
Newton-Maxwell Journal of Physics Vol. 6 No. 2: Oktober 2025
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/nmj.v6i2.43228

Abstract

The increasing demand for bone graft materials has driven the development of synthetic alternatives that closely mimic the mineral structure of natural bone and dental tissues. Hydroxyapatite (HAp) is a calcium phosphate material whose crystal structure closely resembles that of bone and dental tissue, making it highly suitable for various biomedical applications. In this study, calcium oxide (CaO) was obtained from calcined chicken eggshells, with calcination durations of 2, 3, and 4 hours, followed by the synthesis of HAp using the hydrothermal method at 160  for 24 hours. X-ray diffraction (XRD) analysis was performed to evaluate the effects of calcination time on crystallinity and crystallite size. The results showed that increasing the calcination time led to higher crystallinity, ranging from 46% to 54%. Crystallite size was estimated using three Scherrer-based methods. The straight-line Scherrer method produced values ranging from 1733.17 to 4621.8 nm, the average Scherrer method from 11.33 to 11.74 nm, and the modified Scherrer method from 8.49 to 9.11 nm. All three methods consistently indicated a decrease in crystallite size with longer calcination durations. These findings demonstrate that prolonged calcination enhances crystallinity and reduce crystallite size, underscoring the critical role of calcination time in shaping structural characteristics of HAp.
Co-Authors Ayunda Dwi Handayani Delia Amanda Dwiria Wahyuni Elsa Narulita Frinelda Rehulina Barus Hilyana Agis Risla Latifah Tri Amanda Mega Nurhanisa Nurhasanah Nurhasanah Putri, Evi Pania Riza Adriat Siti Helmyati Tri Rahma Febrianti Maharani Zahraini Tasya Siregar