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Horticulture is one of the important commodities in the agricultural sector. Developments of horticulture data collection have been conducting to improve planning. One of which is a method of Rumpun Counting (RC) to estimate productivity of horticultural commodities. However, the RC method is still facing difficulties in its application, for example the observation for commodity repetitive crop harvesting, such as chili. This study attempts to find out an appropriate statistical model among several alternatives for chili productivity in district Cianjur, West Java, Indonesia. We found that each group possesses a specific appropriate model.

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Logistic regression analysis is one of classification methods which is both most popular and common used. This classifier works well when the class distribution in response variable is balanced. In many real cases, the imbalanced class dataset frequently was found. This problem can affect of being difficult at obtaining a good predictive model for minority class dataset. The prediction accuracy generated will be good for majority class but not for minority class. SMOTEBagging is a combination of SMOTE and Bagging algorithm which is used to solve this problem. The purpose of this study is to create a powerful model at classifying the imbalanced data and to improve the classification performance of weak classifier. This study used credit scoring data which is imbalanced data consisting of 17 explanatory variables involved. The result from this study showed that the sensitivity and AUC value from SMOTEBagging Logistic Regression 6858 Fithria Siti Hanifah et al.(SBLR) model is greater than the sensitivity and AUC value of logistic regression model. Moreover, SMOTEBagging algorithm can increase the accuracy of minority class.

Analysis of time series data requires some assumptions that stationary and homogeneity of variance. In many cases is rarely found time series data that satisfy those assumptions. Those are due to the complex non-linear relationship between the multidimensional features of the time series data. KNN method is one of the Learning Machine algorithm (LM) which is considered as a simple method to be applied in the analysis of data with many dimensions variable. This method can be used when it does not meet the classical assumptions. This study aims to see the performance of KNN and ensemble KNN. Although this method is simple but this method has advantages over other method. For instance, it can generalize from a relatively small training set. In This method is very important to choose the number of k-nearest neighbors. Ensemble technique is a method that has accuracy of prediction and efficiently used in the KNN method, so it is not necessary to search the optimal number k. The result shows that MAPE, MAE, and RMSE of prediction will be small if the number of k-nearest neighbors large. Overall, KNN ensemble method has a better performance than KNN method. 7994Dewi Sinta et al.