The 2019 coronavirus pandemic (Covid-19) has been declared a health emergency by WHO with the death rate steadily increasing worldwide, various efforts have been made to deal with this pandemic, from prediction to receiving medical imaging. CT Scan and chest X-Ray images have been proven to be accurate to help medical personnel diagnose COVID, in this paper, we propose a convolutional neural network (CNN) approach and the DenseNet transfer learning model series which aims to understand and find the best classification for COVID or Non-COVID detection. On CT scan chest images, we made two special models in the Descent series, then compared the CNNs in both models by calculating the Accuracy, Precision, Recall, and F1-Score values and presented the results in the confusion matrix. The testing framework is carried out on CNN and the first model of the DenseNet series uses adam optimization, the input function is 244x244x3, the soft-max function is applied as an activity with losses across entropy categories, epoch 50, and batch size for training and testing 16 while validation uses batch size 8, the EarlyStopping function also determined, From the test results, the CNN model is superior to the Densenet series of the first model with an accuracy of about 0.76 (76%), when testing the second model, we carried out the shifting, zooming process and changed the input function to 64x64x3, epoch 30 by adding 4 layers. The second model approach produces better accuracy than CNN and the first DenseNet series, but not as good as expected, based on the test results on the second model produces an accuracy of 0.90 (90%) on Densenet169, Densenet121 around 0.88 (88%) and last Densenet201 is about 0.83 83%), so it is superior to simple CNN models

ABSTRAKPenelitian ini bertujuan untuk (1) Mengetahui karakteristik petani di Kecamatan Simpang Empat, Kabupaten Asahan. (2) Mengetahui atribut apakah yang paling dianggap penting oleh petani dalam memilih benih kelapa sawit. Teknik pengumpulan data menggunakan wawancara, kuisioner dan observasi. Jumlah sampel dalam penelitian ini adalah 86 orang ditentukan menggunakan Rumus Slovin. Hasil penelitian ini adalah atribut yang paling berpengaruh pada Sikap Petani Kelapa Sawit Dalam Pembelian Bibit Kelapa Sawit adalah dimulai dari atribut Harga Jual, Merek, Mutu Bibit, Ketahanan terhadap Hama Dan Penyakit, Potensi produksi Dan Promosi.Kata kunci:Sikap, Atribut, Bibit ABSTRACTThis study aims to (1) determine the characteristics of farmers in Simpang Empat District, Asahan Regency. (2) Knowing what attributes are most considered important by farmers in choosing oil palm seeds. Data collection techniques using interviews, questionnaires and observation. The number of samples in this study was 86 people determined using the Slovin formula. The results of this study are the attributes that have the most influence on the Attitude of Oil Palm Farmers in Purchasing Oil Palm Seeds, starting from the attributes of Selling Price, Brand, Seed Quality, Resistance to Pests and Diseases, Production Potential and Promotion.Keywords:Attitudes, Attributes, Seed