Article Per Year (5 Year)
p-Index From 2021 - 2026
1.048
P-Index
This Author published in this journals
All Journal Emerging Science Journal Journal of Innovation Information Technology and Application (JINITA) Scientific Journal of Engineering Research
Furizal
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Environmental Science

Published : 6 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Scientific Journal of Engineering Research

 1 
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): September
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.
Classification for Waste Image in Convolutional Neural Network Using Morph-HSV Color Model Fahmi, Miftahuddin; Yudhana, Anton; Sunardi; Abdel-Nasser Sharkawy; Furizal
Scientific Journal of Engineering Research Vol. 1 No. 1 (2025): March
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v1i1.2025.12

Abstract

Waste management is essential in preserving nature to be cleaner and more well-maintained. Waste management runs slower than the speed of waste accumulation. One reason is slow waste sorting. This problem can be overcome by building a learning machine that can sort the types of waste. The type of waste often separated in the first sorting is waste based on its type, namely organic and inorganic. The classification model used is the CNN with image processing Morph-HSV color model. The data obtained from Kaggle is collected and processed using Python. The processed image is trained using a CNN classification model. The results of this study are an accuracy of 99.58% and a loss of 1.57%. With this research, it is hoped that it can accelerate waste sorting performance using the most efficient ML based on image processing and its classification model.
Co-Authors Abdel-Nasser Sharkawy Abdul Syukur Alfian Ma’arif Anton Yudhana Arif Rahman Fahmi, Miftahuddin Imam Riadi Maghfiroh, Hari Muis, Alwas Nugraha, Ikhwan Oyeyemi, Babatunde Bamidele Puriyanto, Riky D. Rustiawan, Angga Sunardi Suprihatin Suwarno, Iswanto Tawar Yunandha, Isro D.