OPTIMALISASI KLASIFIKASI CITRA MEDIS MENGGUNAKAN CNN DAN ADAM OPTIMIZER DENGAN PARAMATER MINIMUM
Abstract
Abstract: Research in the field of imaging, especially in medical terms, is expected to have a positive impact on the treatment and diagnosis of diseases in the medical world. Medical image classification is a topic that is often researched, this is indicated by the many national and international journals that discuss this topic. Research on medical image classification using Convolutional Neural Network (CNN) usually focuses on the use of maximum parameters (hyper parameters) to get the best results. However, those that use minimal parameters and the smallest resources are still lacking. Based on the existing problems, it is carried out to obtain optimization in the medical image classification process. The classification of medical images in this study focuses on brain tumor images consisting of three classes, namely meningioma, glioma and pituitary tumor. The approach taken in this study is to use the CNN model and Adaptive Moment Estimation (Adam) Optimizer. The study was conducted by combining the smallest parameters from the Adam Optimizer. The parameters combined are Epoch and Convolution Layer. Where 3 Epoch categories (1,5,10) and 5 convolution layers (1,2,3,4,5) are used. From the tests carried out, the highest accuracy results obtained were 92.8% with epoch parameters of 10 and three convolution layers. Meanwhile, the highest average accuracy was recorded at 90.7% with epoch parameters of 10. The fastest computation time required for model creation was 24.83 seconds, and the lowest CPU resource usage during the model creation process was 16.45%.
Keywords: Image Classification, CNN Optimization, Adam Optimizer, Brain Tumor, Minimum Parameters
Abstrak: Penelitian dibidang citra khususnya dalam hal medis diharapkan dapat membawa dampak baik bagi penanganan dan diagnosis penyakit dalam dunia medis. Klasifikasi citra medis menjadi topik yang cukup sering diteliti, hal ini ditandai dengan banyaknya jurnal baik nasional maupun internasional yang membahas mengenai topik ini. Penelitian mengenai klasifikasi citra medis menggunakan Convolutional Neural Network (CNN) biasanya berfokus pada penggunaan paramater maksimal (hyper parameter) untuk mendapatkan hasil terbaik. Namun yang menggunakan paramater minimal dan sumber daya terkecil masih belum ada. Berdasarkan permasalahan yang ada maka dilakukan untuk mendapatkan optimalisasi dalam proses kalsifikasi citra medis. Klasifikasi citra medis dalam penelitian ini difokuskan pada citra tumor otak yang terdiri dari tiga kelas yaitu, meningioma, glioma dan pituitary tumor. Pendekatan yang dilakukan dalam penelitian ini adalah menggunakan model CNN dan Adaptive Moment Estimation (Adam) Optimizer. Penelitian dilakukan dengan melakukakn kombinasi paramater terkecil dari Optimizer Adam. Paramater yang dikombinasikan yaitu Epoch dan Lapisan konvolusi. Dimana digunakan 3 kategori Epoch (1,5,10) serta 5 lapisan konvolusi (1,2,3,4,5). Dari pengujian yang dilkaukan didapatkan hasil Akurasi tertinggi yang diperoleh adalah 92,8% dengan parameter epoch 10 dan tiga lapisan konvolusi. Sementara itu, akurasi rata-rata tertinggi tercatat sebesar 90,7% dengan parameter epoch 10. Waktu komputasi tercepat yang diperlukan untuk pembuatan model adalah 24,83 detik, dan penggunaan sumber daya CPU terendah selama proses pembuatan model adalah 16,45%.
Kata kunci: Klasifikasi Citra, Optimalisasi CNN, Adam Optimizer, Tumor Otak, Paramater Minimum
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DOI: https://doi.org/10.54314/jssr.v8i1.2515
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