PEMODELAN PERENCANAAN TERINTEGRASI UNTUK RANTAI SUPLAI DAN STOK PENGAMAN MULTI ESELON

Business environment has strong competition from year to year. This is because various changes and uncertainties fill the competition. Most of the changes or uncertainties in the business world are caused by the increasing of consumer bargaining power in business practices. Consumers have high power in determining their requests that must be fulfilled by business people. Changes or uncertainties are most of the main factors that cannot be anticipated when the business world has strong and uncertain competition. These uncertainties require business people to design an appropriate plan in order to minimize costs, especially inventory costs with consumer demand are still fullfilled. In that design plan, business people must be able to optimize the supply chain. In industrial systems, supply chain optimization and its response are strongly influenced by inventories. Inventories and its numbers are important issues in the supply chain that must be integrated with the optimization of the supply chain to manage demand uncertainty and to maintain customer service levels. This study designs an integrated planning model for supply chain and multi-echelon inventory in determining the location and the numbers of inventory in a supply chain with a general configuration with considering the uncertainty of consumer demand or  all things coming from production tim

Experimenting Diabetic Retinopathy Classification Using Retinal Images

Along with many complications, diabetic patients have a high chance to suffer from critical level vision loss and in worst case permanent blindness due to Diabetic Retinopathy (DR). Detecting DR in the early stages is a challenge, since it has no visual indication of this disease in its preliminary stage, thus becomes an important task to accomplish in the health sector. Currently, there have been many proposed DR classifier models but there is a lot of room to improve in terms of efficiency and accuracy. Despite having strong computational power, current deep learning algorithm is not able to gain the trust of the medical experts in classifying DR. In this work, we investigate the possibility of classifying DR using deep learning with Convolutional Neural Network (CNN). We implement preprocessing combined with InceptionV3 and VGG16 models. Experimental results show that InceptionV3 outperforms VGG16. InceptionV3 model achieves an average training accuracy of 73.5 % with a validation accuracy of 68.7%. VGG16 model achieves an average training accuracy of 66.4% with a validation accuracy of 63.13%. The highest training accuracy for InceptionV3 and VGG16 is 79% and 81.2%, respectively. Overall, we achieve an accuracy of 66.6% on 52 images from 3 different classes