Klasifikasi penyakit Tuberkulosis berdasarkan citra x-ray menggunakan metode Elman Recurrent Neural Network

Tuberkulosis merupakan penyakit yang dapat menular melalui udara yang memiliki tingkat kematian tertinggi di dunia. World Health Organization menyatakan bahwa pada tahun 2019 Indonesia termasuk dalam urutan ketiga besar dengan jumlah kasus penderita tuberkulosis terbanyak. Hal ini terjadi karena kurangnya kesadaran masyarakat mengenai penanganan dan pengetahuan tentang penyakit tuberkulosis. Salah satu pemeriksaan terhadap penyakit tuberkulosis yaitu dengan chest x-ray. Pada penelitian ini akan dilakukan klasifikasi penyakit tuberkulosisi berdasarkan chest x-ray dengan menggunakan Elman RNN untuk mengetahui seseorang normal atau terdeteksi tuberkulosis. Model terbaik dari hasil klasifikasi terletak pada sudut orientasi 45 dengan node hidden layer 20 dan 50 pada learning rate 0.5 diperoleh accuracy sebesar 95.4773%, sensitivity sebesar 97.9591%, specificity sebesar 97.9591%

Prediksi Kecepatan Arus Laut dengan Menggunkan Metode Backpropagation (Studi Kasus: Labuhan Bajo)

One factor that is very influential on the dynamics of the waters is the speed of ocean currents. The speed of the ocean currents has an impact on activities around the coast that is for tourists to get information about the condition of the movement of the sea. One of them is in Labuhan Bajo. Labuhan Bajo is a tourist area that has a variety of natural beauty where visitors increase every year. The influence of the west monsoon wind in Labuan Bajo is very large on the condition of sea movement, especially on ocean currents. Predictions about the speed of ocean currents are very important in marine activities, especially diving because it is an effort to prevent the occurrence of things that are not desirable because of the condition of the sea that is not conducive. In this study the method used in predicting the current speed is the Backpropagation method. By testing the hidden layer nodes and the learning rate on the Backpropagation method the best MAPE results are obtained from sharing 70% of training data with 100 hidden layer nodes and the learning rate of 0.1 is 7.59%. Whereas by sharing 80% of the best MAPE training data, there are 100 hidden layer nodes and the learning rate of 0.1 is 0.57%. Then from 90% of the data sharing training data obtained the best MAPE results in the hidden layer node 100 and a learning rate of 0.4 out of 6.65%, this shows that the Backpropagation method is very well used in predicting the speed of ocean currents