The Central Bureau of Statistics of Indonesia (BPS) classified the target households into three different categories which were very poor households (RSTM), poor households (RTM), and nearly-poor households (RTSM). BPS need some method that can accelerate the classification process to assist the performance of BPS in order to shorten the processing time. The data scale that used in the classification of poor households was ordinal. Generally, calculations of classification using ordinal asscales only can be found in the software WEKA Ordinal Class Classifier (OCC) that was one of the existing classification in WEKA. OCC could be resolve to attributes that are nominal, numerical, and ordinal. So in this research, OCC would be using to classify poor households. By comparing the algorithms performance there were several stages that need to be traversed. The first was the data collection stage, the second was the data processing stage and information by using preprocessing, the third was the analysis stage with tools WEKA. The fourth was a test stage by counting the value of accuracy, precision, and recall. The last stage was evaluation by comparing actual data with predictive data of the result of calculating system. From the classification process, it can be concluded that OCC has the highest accuracy, precision, and recall level which is 90% (3803) of training set and 10% (423) of testing set with accuracy of 90.5437%, precision 0.919, and recall 0.905.
Analysis of Representative Values in Clustering using the CURE Algorithm
