The increase of the classification accuracy level has become an important problem in machine learning especially in diverse data-set that contain the outlier data. In the data stream or the data from sensor readings that produce large data, it allows a lot of noise to occur. It makes the performance of the machine learning model is disrupted or even decreased. Therefore, clean data from noise is needed to obtain good accuracy and to improve the performance of the machine learning model. This research proposes a two-stages for detecting and removing outlier data by using the covariance estimator and isolation forest methods as pre-processing in the classification process to determine fine motor skill (FMS). The dataset was generated from the process of recording data directly during cursive writing by using a digitizer. The data included the relative position of the stylus on the digitizer board. x position, y position, z position, and pressure values are then used as features in the classification process. In the process of observation and recording, the generated data was very huge so some of them produce the outlier data. From the experimental results that have been implemented, the level of accuracy in the FMS classification process increases between 0.5-1% by using the Random Forest classifier after the detection and outlier removal by using covariance estimator and isolation forest. The highest accuracy rate achieves 98.05% compared to the accuracy without outlier removal, which is only about 97.3%.

This paper aims to investigate the important factors that affect the value of the minimum passing level (MPL) of competency achievement and find the best method to predict it. The MPL of competency achievement is the value that represents the minimum passing score of examination related to the competency. Different schools may have a different value of the MPL because the MPL is defined based expert opinion on several uncertainty aspects and conditions at each school. This paper proposes the combination of rough sets and fuzzy signature method to predict the category of the MPL. The rough sets method is applied to reduce unnecessary features for classification and find the important factors to predict the MPL. The fuzzy signature is employed to predict the category of MPL based on the selected features. The method proposed in this paper consists of several stages, namely data collection and pre-processing, features selection, predict the category of the MPL using the combination of rough sets and fuzzy signatures method, and performance evaluation. Fifteen headmasters and sixty teachers of elementary schools participated in the data collection process. Based on the experiment with 203 objects data we achieved 97% accuracy in the prediction of MPL. The proposed method succeeded to identify the important factors on predicting the MPL on the complexity of competency and resource capacity of the school aspect. We obtained the improvement for accuracy of the complexity of competency prediction of 8.5% from the best method in the previous research.