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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA International Journal of Social Service and Research
Muis, Alwas
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences

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Medical Image Classification of Brain Tumors using Convolutional Neural Network Algorithm Muis, Alwas; Sunardi, Sunardi; Yudhana, Anton
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.6939

Abstract

Brain tumor is a highly dangerous and deadly disease. It can occur due to the abnormal growth of cells or tissues in the head. Treatment for brain tumor is done with surgery and chemotherapy aimed at killing or destroying the cells that affect the growth process of brain tumor. Diagnosis of brain tumor is done using medical scans such as MRI, CT Scan, and PET Scan by analyzing the resulting images. Another method used to detect brain tumors is through biopsy, which is a process of taking cells or tissue from the body for examination in the laboratory. However, this method takes a long time because the cells taken from the patient will be examined in the laboratory. Therefore, a technique is needed to speed up accurate brain tumor diagnosis in order to obtain quick treatment. Machine learning can solve this problem with the classification of images produced by MRI. The classification technique that can be used is the GoogLeNet architecture in CNN. Because GoogLeNet is the algorithm that won the ImageNet Large Scale Visual Recognition Challenge (ILSVC) in 2014 The purpose of this study is to classify brain images using the GoogLeNet architecture. The dataset used in this study consists of 7023 images, consisting of 6320 images for training the model and 703 for testing the model. The results of this study obtained an accuracy percentage of 96%. This result is higher than previous studies that obtained an accuracy value of 94%.
PUBLIC STIGMA ABOUT POLYGAMY BASED ON ISLAMIC-MUHAMMADIYAH VIEWS USING SENTIMENT ANALYSIS APPROACH Arqam, Mhd Lailan; Firdaus, Asno Azzawagama; Palahuddin, Palahuddin; Furizal, Furizal; Muis, Alwas; Atmojo, Ahmad Muslih
International Journal of Social Service and Research Vol. 4 No. 8 (2024): International Journal of Social Service and Research
Publisher : Ridwan Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46799/ijssr.v4i8.896

Abstract

Social media is very important to control the development of issues that occur today. With social shifts and changing societal values, polygamy has become a complex issue and attracts the attention of many people around the world discussed through social media platforms. This research contributes to the field by applying a sentiment analysis approach to automatically detect and analyze public sentiment regarding polygamiy content on Twitter, particularly in the context of Islamic-Muhammadiyah views. This study used decision tree classification methods, support vector machines, and random forests with the best analysis accuracy obtained at SVM 77.4%. Furthermore, the results of the sentiment class obtained were analyzed according to the views of Muhammadiyah. The results obtained in the analysis 77% commented negatively and 23% commented positively. In addition, this research can be used as a reference for future research on sentiment analysis cases to training and testing classroom models.
Co-Authors Anton Yudhana Arqam, Mhd Lailan Asno Azzawagama Firdaus Atmojo, Ahmad Muslih Furizal, Furizal Palahuddin, Palahuddin Sunardi Sunardi