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

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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.
Digitalisasi Portofolio Siswa Berbasis Website di SMK Informatika Wonosobo Riadi, Imam; Umar, Rusydi; Muis, Alwas; Yunus, Muhajir
Jurnal Pengabdian Masyarakat Bumi Rafflesia Vol. 6 No. 3 (2023): Desember : Jurnal Pengabdian Kepada Masyarakat Bumi Raflesia
Publisher : Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jpmbr.v6i3.5869

Abstract

Siswa dilatih untuk memiliki skill dan pengetahuan agar dapat bekerja pada industri. Setiap siswa dituntut memiliki pengetahuan yang dapat digunakan untuk mencari kerja setelah lulus. Kebutuhan akan adaptasi terhadap perkembangan teknologi informasi dan komunikasi dalam dunia pendidikan semakin meningkat. Sehingga dibutuhkan pelatihan terkait penggunaan teknologi informasi untuk membantu siswa beradaptasi dengan perkembangan teknologi. Tujuan pelatihan ini yaitu untuk memperkenalkan siswa tentang konsep portofolio digital berbasis website dan membekali mereka dengan keterampilan menggunakan teknologi informasi yang relevan untuk membangun dan mengelola portofolio. Manfaat pelatihan ini yaitu membantu siswa untuk mempresentasikan karya-karya mereka secara efektif kepada pihak-pihak yang berkepentingan seperti calon perguruan tinggi dan pemberi kerja. Metode analisis data pada pelatihan ini menggunakan metode likert dengan memberikan pernyataan dan memberikan jawaban mulai dari sangat setuju, setuju, netral, tidak setuju, dan sangat tidak setuju. Siswa berhasil membangun portofolio digital yang menarik dan profesional, memamerkan karya-karya mereka dengan efektif. Metode pengumpulan data pada kegiatan ini yaitu menggunakan kuesioner. ini menunjukkan bahwa pelatihan portofolio menggunakan website sangat mudah dipahami oleh siswa. Selain itu, seluruh siswa berharap pelatihan seperti sering diadakan untuk membantu dalam memanfaatkan teknologi informasi untuk pengembangan diri. Penerapan pelatihan ini di SMK Informatika Wonosobo memberikan manfaat yang signifikan bagi siswa dalam menghadapi tantangan di era digital.   Kata Kunci: Google sites, pelatihan, portofolio, website
Early Detection of Brain Tumors: Performance Evaluation of AlexNet and GoogleNet on Different Medical Image Resolutions Muis, Alwas; Rustiawan, Angga; Oyeyemi, Babatunde Bamidele; Syukur, Abdul; Furizal
Scientific Journal of Engineering Research Vol. 1 No. 3 (2025): July
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v1i3.2025.10

Abstract

Early detection of brain tumors through medical imaging is crucial to improving treatment success rates. This study aims to classify brain tumors using two deep learning models, AlexNet and GoogleNet, by testing three image sizes. The dataset used consists of four classes: glioma, no tumor, meningioma, and pituitary. The test results show that the AlexNet model achieves the best accuracy of 98% at a resolution of 150x150, while GoogleNet shows stable performance with the highest accuracy of 96% at both 150x150 and 200x200 resolutions. The medium resolution (150x150) proves to be optimal for both models, providing the best balance between visual information and processing efficiency. This study highlights the potential use of AlexNet and GoogleNet in brain tumor classification, with opportunities for performance improvement through further development, such as ensemble techniques and the use of a larger dataset.
Co-Authors Abdul Syukur Anton Yudhana Arqam, Mhd Lailan Asno Azzawagama Firdaus Atmojo, Ahmad Muslih Furizal Furizal, Furizal Imam Riadi Muhajir Yunus Oyeyemi, Babatunde Bamidele Palahuddin, Palahuddin Rustiawan, Angga Rusydi Umar Sunardi Sunardi