The growth of children under the age of three (toddlers) is one of the determinants of children's development in the future. One of the parameters of toddler growth assessment is determined by gender, age, height and weight. This research makes a system that can monitor toddler growth with web-based. The research method used is the System Life Development Cycle, which consists of planning, analysis, design, implementation and use. This system also uses the Tsukamoto fuzzy method to determine the membership set of each input variable. The gender criteria are divided into two classes, male and female, the age criteria are divided into three classes, the height criteria are three classes, and the weight criteria are divided into three classes. Based on the division of classes, the output of this study is the growth status of toddlers, namely poor growth, poor, normal and more. Based on the results of input data criteria and calculations using Tsukamoto fuzzy, the output obtained in the form of the status of the child's growth.
Panel data describes a condition in which there are many observations with each observation observed periodically over a period of time. The observation clustering context based on this data is known as Clustering of Time Series Data. Many methods are developed based on fluctuating time series data conditions. However, missing data causes problems in this analysis. Missing data is the unavailability of data value on an observation because there is no information related to it. This study attempts to provide an alternative method of clustering observations on data with time series containing missing data by utilizing correlation matrices converted into Euclid distance matrices which are subsequently applied by the hierarchical clustering method. The simulation process was done to see the goodness of alternative method with common method used in data with 0%, 10%, 20% and 40% missing data condition. The result was obtained that the accuracy of the observation bundling on the proposed alternative method is always better than the commonly used method. Furthermore, the implementation was done on the annual gini ratio data of each province in Indonesia in 2007 to 2017 which contained missing data in North Kalimantan Province. There were 2 clusters of province with different characteristics.
Determining the segmentation and positioning of the lecturers in selecting the thesis supervisor is very important to do. It is because, with this information, the supervision process in thesis writing can run well. This study intends to analyze the segmentation and positioning of lecturers related to determine the thesis supervisor using the Clusterwise Bilinear Spatial Multidimensional Scaling Model (CBSMSM) method. The data used is survey data for fifth-semester bachelor students of the 2019/2020 academic year of the Department of Computer Science, Pakuan University. One hundred sixty-one student observations provide an assessment of 10 attributes regarding the characteristics of 32 lecturers of the department. Furthermore, the estimation of the segment coordinate parameters, lecturer coordinates, dimensions, and attributes simultaneously uses the alternating least square (ALS) algorithm. The number of segments and dimensions are selected based on the smallest sum square error (SSE) value for combining segments and other dimensions. As a result, we get four segments and four dimensions with an SSE value of 4864.003. Furthermore, the department can use this result to illustrate student assessments of their lecturers' characteristics regarding thesis supervision.
Logistic regression has become a popular method for handling predictive modeling when the response variable has a categorical scale. The difference in category proportion in response variable could influence the prediction accuracy. This research applied the model averaging approach for logistic regression in purpose to improve the prediction accuracy in different proportion of each category. Model averaging has the idea to combine some model candidates based on the specified weight to be the final model. The model candidate in model averaging generated based on all possibilities variable selection in the model. AIC weight is chosen to apply in the combination of all possible model candidates. It is illustrated with an application to data from a classification of Autistic Spectrum Disorder data. The result of this case indicated that the logistic model averaging had better performances.
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