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Indonesia with abundant natural resources, certainly have a lot of plants are innumerable. To clasify the plants into different clusters can use several methods. Methods used are K-Means and Fuzzy C-Means. However, this methods have difference. Not only in terms of algorithms, but in terms of value calculation on the root mean square error (RMSE) also different. To calculate the value of RMSE there are two indicators are required, namelt the training data and the checking data. Of discussion, the Fuzzy C-Means method has RMSE values smaller than the K-Means method, namely on 80 training data and 70 checking data with RMSE value 2,2122E-14. This indicates that the Fuzzy C-Means method has a higher level of accuracy than the K-Means method

The increasing number of breast cancer in recent years has attracted numerous researchers' attention. Several techniques of Computer Aided Diagnosis System have been proposed as alternative solutions to diagnose breast cancer. The flaw of simply using the naked eye to see the differences between normal and with cancer mammogram images makes the texture analysis play an important role in classifying breast cancer. In this study, the results of the classification were compared using various methods of texture analysis in extracting a feature of the mammogram image. Some texture analysis methods, including first order, which consist of GLCM, GLRLM, and GLDM, have successfully extracted features based on their characteristics. The statistical features of these methods are used as input for the ECOC SVM classification, which three kernel comparisons; linear, RBF, and polynomial, build the classification. The results show that the best kernel is polynomial kernels with statistical features built by GLRLM with 93.9757% accuracy value.

Personality is one of the important variables for predicting student academic success. The purpose of the research is to examine the Big Five personality test as a predictor on the academic achievement of State Islamic Senior High School students in Indonesia. This research used a quantitative method which used a survey of the Big Five Personality Test and learning achievement on 5 subjects. The subjects of this study were the 2145 sample students of 23 State Islamic Senior High School of Insan Cendekia (SISHS-IC) around Indonesia. The results of this study indicate that all dimensions of Big Five Personality traits; openness to experience, conscientiousness, extraversion, agreeableness, and emotional stability have a significant effect as predictors of students' academic achievement. While in parts of each dimension, the most significant predictor of students' academic achievement is the emotional stability and openness to experience. These findings are very important for teachers and schools to pay much more attention to emotional stability and openness to experience as predictors of student academic achievement.

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