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One of the main causes of death in the world is caused by cancer. And one of them is Lung Cancer. According to World Health Organization, in 2014 the death rate caused by lung cancer in Indonesia was 21.8% in men and 9.1% in women with 30,865 cases of death caused by lung cancer each year in men and women. Many studies have been carried out on computational lung cancer. One of them is by implementing machine learning in detecting lung cancer. However, there are obstacles, namely the imbalance in the amount of data between patients and non-patients. So it takes an approach to overcome this imbalance. One of these methods is ADASYN which is then combined with the Random Forest classification algorithm. In this study, we will compare the results of the classification model performance in Random Forest before and after the ADASYN sampling method was used in the training process. The results of this study showed an increase in the performance of the Random Forest classification model with an AUC value of 0.859 after the ADASYN sampling method was carried out with an error rate of 4.9%. [ABSTRACT FROM AUTHOR]

The C4.5 algorithm still has weaknesses in predicting or classifying data if the amount is large. It is necessary to improve the performance of the C4.5 algorithm with the selected split attribute using application of the average gain value to perform the classification. The C4.5 algorithm is one of the Decision Tree methods in the classification process using entropy. The result of the classification obtained from the analysis can be a classification of 8 student data from 100 student data that is tested to produce information on Sanguine, Choleric, Melancholy, and Phlegmatic. From the result of the Decision Tree classification algorithm C4.5 has an accuracy rate of 86.36% with an application error rate or error of 13.64%. [ABSTRACT FROM AUTHOR]

The Tomato plant is a horticultural commodity that is easily cultivated in the highlands and lowlands. Although easy to cultivate, Tomato plants are susceptible to disease, especially on the leaves. The emergence of early symptoms of Tomato leaf disease can be used as a form of prevention by farmers so that the disease is not contagious on healthy leaves. So it takes accuracy in seeing the form of leaf damage to be able to take the right decisions in preventing Tomato plants. One solution to this problem is to classify Tomato leaf diseases based on Tomato leaf images using the Convolutional Neural Network algorithm. The results of the experiment on the Tomato leaf disease classification process which was carried out by dividing the image into training data and testing data of 80% and 20% resulted in the best performance at epoch 50 epochs, batch sizes 16 and 95 at step per epoch. The accuracy produced in this study was 96.6%, and precision, recall, and F1-Score were 96.3%. [ABSTRACT FROM AUTHOR]

The color characteristics of pesticide vegetables can be seen with the naked eye, but there are inaccuracies in the assessment that continue to be carried out so that time efficiency is not met. KNN processes data from the color of vegetables with pesticides by classifying all images of vegetables. The results of the analysis showed that in this test the authors found that cauliflower vegetables did not contain pesticides, where the results obtained from color extraction were carried out from training results, and the test stated that cauliflower vegetables were vegetables that did not contain pesticides or were suitable for consumption. The results of this test use 30 test data with attributes and 3 species in the data classification. The test results show the K-Nearest Neighbor method in data classification has a good percentage of accuracy when using random data. The percentage of variation in the value of K K-Nearest Neighbor 3,5,7,8,9 as a percentage of 100%. [ABSTRACT FROM AUTHOR]