Plants are the most important part in life on earth as oxygen supplier to breathe, groceries, fuel, medicine and more. Plants can be classified based on its leaves shape. Classification process is required well data extraction feature, so it needs fixing feature process at pre-processing level. Combining median filter and image erosion is used for fixing feature process. Whereas for feature extraction is used invariant moment method. In this research, it is used leaves classification based on leaves edge shape. K-Nearest Neighbor Method (KNN) is used for leaves classification process. KNN method is chosen because this method is known rapid in training data, effective for large training data, simple and easy to learn. Testing the result of leaves classification from image which is on dataset has been built to get accuracy value about 86,67%. Index Terms—Classification, Median Filter, Invariant Moment, K-Nearest Neighbor.
Acute myeloid leukemia (AML) M4, M5, and M7 are subtypes of leukemia derived from myeloid cell derivatives that influences the results of the identification of AMLs, which includes myeloblast, monoblast, and megakaryoblast. Furthermore, they are divided into more specific types, including myeloblasts, promyelocytes, monoblasts, promonocytes, monocytes, and megakaryoblasts, which must be clearly identified in order to further calculate the ratio value in the blood. Therefore, this research aims to classify these cell types using the K-nearest neighbor (KNN) algorithm. Three distance metrics are tested, namely, Euclidean, Chebychev, and Minkowski, and both the weighted and unweighted were tested. The features used as parameters are area, nucleus ratio, circularity, perimeter, mean, and standard deviation, and about 1,450 objects are used as training and testing data. In addition, to ensure that the classification is not overfitting, K-fold cross validation was conducted. The results show that the unweighted Minkowski distance acquired about 240 of 290 objects at K = 19, which is the best. Therefore, the unweighted Minkowski distance is selected for further analysis. The accuracy, recall, and precision values of KNN with unweighted Minkowski distance obtained from fivefold cross validation are 80.552, 44.145, and 42.592%, respectively.
Abstract— After being introduced in 2008, the rise in the price of bitcoin and the popularity of other cryptocurrencies triggered a growing discussion about how much energy was consumed during the production of this currency. Making cryptocurrency the most expensive and most popular, both the business world and the research community have begun to study the devel-opment of bitcoin. In this study bitcoin price predictions are performed using the double exponential smoothing method based on the mean absolute percentage error (MAPE). The MAPE value is used to find the best alpha (α) parameter as the basis for bitcoin price forecasting. The dataset used is the price of bitcoin from 2017 to 2019. The dataset was obtained from www.cryptocompare.com. As for the value of the alpha parameter (α), using a value of 0.1 to 0.9. Based on the test results using the double exponential smoothing method obtained the smallest MAPE value of 2.89%, with the best alpha (α) at 0.9. The prediction is done to see the price of bitcoin on January 1, 2020. The error rate generated on the predicted price of bitcoin uses an amount of 0.0373%. This shows that the system built can be used as a support for decision making when trading bitcoin.
Coronavirus disease (COVID-19) was first discovered in December 2019 in Wuhan, China, and spread so quickly into a pandemic. This outbreak has spread to 24 other countries, including Indonesia. Its spread is very fast, so a co-19 prediction study is needed to be able to make the right policy. To be able to predict the number of COVID-19 cases can be done with the Forecasting Technique. The purpose of this study is to forecast and compare Single Exponential Smoothing and Double Exponential Smoothing ¬ against the number of COVID-19 cases in Indonesia. The results of this study can be used as consideration for policymaking in dealing with the spread of COVID-19. Distribution predictions are based on data released by the Indonesian National Disaster Management Agency (BNPB) in the first 100 days of COVID-19 deployment. The results of this study are the Double Exponential Smoothing method is more accurate than the Single Exponential Smoothing method because the forecasting results show an increase from the previous data. And the percentage of errors (MAPE) obtained is significantly smaller.
The Covid-19 pandemic in Indonesia has emerged starting in 2020. To know the development of cases, a good calculation is needed. A prediction system can help in analyzing accurate data on positive causes, cures, and deaths. The right prediction or forecast can be the answer to the question of the impact that will occur, forecasting will provide an overview to the government and the community so that it is hoped that related parties can prepare for future impacts or even reduce the number of cases growth. In this study, the Exponential Smoothing method was used as a prediction calculation. This method is simple but effective in producing accurate predictions. Forecasting data used comes from the Indonesian government with the assumption that the data is valid and reliable. Based on research that has been carried out to predict the increase in new cases of the Indonesian National Covid-19, the best alpha (α) value is 0.33 with an SSE of 1048027,939. This shows that the number of cases is increasing. The results of forecasting in this study using the time series approach and the SES method are more suitable for predicting the percentage increase in cases than knowing the exact number.
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