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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 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]

Nowadays, because of the high demand for car transportation, not everyone has the purchasing power of cash, and people sometimes pay for it on credit. ACC is a finance company that acts as a facilitator for car loan financing. ACC collects car loans through collectors who are assigned an area in Medan City and also the address of the customer who will be billed in installments. To maximize the installment collection process, the collector's journey needs to be optimized. A metaheuristic algorithm can solve the collector-collection trip optimization problem. The metaheuristic algorithm used to solve this problem is the Steepest Ascent Hill Climbing algorithm and the genetic algorithm to find the optimum route from the collector billing path. After the two algorithms are implemented and tested, the final result of this research is the complexity of the Steepest Ascent Hill Climbing algorithm is θ(n) with a running time of 429.1936 ms on a 5-point test, while the complexity of the genetic algorithm is θ(n3) with a running time of 8921.8021 ms on a 5-point test. [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 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]

Indonesia Central Bureau of Statistics February 2020's data showed us that there were 6.88 million Indonesian citizens who were still unemployed. One of the factors that cause unemployment is the difficulty to find job vacancies that are suitable for job seekers' skills. Generally, job seekers will find job vacancies that offer a good salary and many facilities. But, often they forget to consider the required qualifications and decide the best job vacancies for themselves based on intuition only. This is quite natural considering the limited human cognitive ability. But, decision-making that relies on only intuition alone can cause fatal consequences. Especially considering number of job vacancies is quite limited. Therefore, we can develop a decision support system to help job seekers to find job vacancies that offer the best facilities and still consider the required qualifications. Decision support system is expected to provides the best job vacancies recommendations based on criteria weight that was obtained from a survey of 100 job seekers. Criteria used to evaluate job vacancies data are salary, number of employees, number of the offered facility, number of mandatory skills, and minimum work experience. EDAS (Evaluation based on Distance from Average Solution) method is chosen to be implemented in this decision support system. EDAS method uses two measuring distances to evaluate every job vacancies data, specifically Positive Distance from Average (PDA) and Negative Distance from Average (NDA). After conducting several tests on the system by using the manual calculation method and black-box testing method, it can be concluded that the developed decision support system can give job vacancies recommendations for users. [ABSTRACT FROM AUTHOR]