The quality of students in school has a lot of diversity, this makes students have different levels of understanding. This can be seen from the variety of student scores obtained on report card scores, this needs to be a concern for the school, especially teachers. One of them is by forming effective study groups so that every student has the opportunity to excel. So this research was carried out with the aim of helping schools, especially teachers, to map student study groups evenly based on student report cards obtained in Semester I to Semester IV. The method used was clustering with the K-Means algorithm on the report card scores of Class IX.C students at SMP Pembangunan Laboratorium UNP. The results in this study obtained 3 clusters of students, namely students with High Achievement, Achievement and Less Achievement. This research can be used as a guide for teaching teachers in making decisions about the formation of student study groups in Class IX.C.

The quality of students in school has a lot of diversity, this makes students have different levels of understanding. This can be seen from the variety of student scores obtained on report card scores, this needs to be a concern for the school, especially teachers. One of them is by forming effective study groups so that every student has the opportunity to excel. So this research was carried out with the aim of helping schools, especially teachers, to map student study groups evenly based on student report cards obtained in Semester I to Semester IV. The method used was clustering with the K-Means algorithm on the report card scores of Class IX.C students at SMP Pembangunan Laboratorium UNP. The results in this study obtained 3 clusters of students, namely students with High Achievement, Achievement and Less Achievement. This research can be used as a guide for teaching teachers in making decisions about the formation of student study groups in Class IX.C.

This research is motivated by low students' critical thinking ability. This is caused by the ineffectiveness of learning in the classroom in the learning process with conventional model while the students who receive it more passive and afraid to express the assumption because they think that math is hard to learn. So teachers need to apply a fun learning system and more real in improving the ability to think critically by using Realistic Mathematics Education Approach (RME) to help critical thinking of students to be more critical. This research is an experimental research. The population in this research is all students of class VII SMPN 2 Kota Jambi Academic Year 2017/2018. Sampling using Random Sampling technique, which is taken class VII A and VII D where two classes are given different treatment. This study aims to determine the difference of Realistic Mathematics Education (RME) approach to critical thinking skills of the seventh grade students of SMPN 2 Kota Jambi, the subject of the set. Based on Post-test results can be seen that the critical thinking ability of students of class VIIIA (experiment) SMP Negeri 2 Kota Jambi on the set material, the average value is 76.84 with standard deviation 6.96 whereas in class VIIID (Control) average of 72.75 with standard deviation of 11.64. Based on these calculations there is a difference between the experimental class and the control class, this is the influence of the approach of Realistic Mathematics Edication (RME).Keywords: Realistic Mathematics Education (RME) Approach, Student Critical Thinking Ability

Forest fires are the most common cause of deforestation in Indonesia. This condition has a negative impact on the survival of living things. Of course, this has received special attention from various parties. One effort that can be made for prevention is to group these points into areas with the potential for fire using the clustering method. In this research, a comparative study of the clustering algorithm between K-Means and K-Medoids was conducted on hotspot location data obtained from Global Forest Watch (GFW). Besides that, important variables that affect the clustering process are also analyzed in terms of feature importance. There are nine important variables used in the clustering process, of which the Acq_time variable is the most important. The cluster quality of both algorithms is evaluated using the silhouette coefficient (SC). Both algorithms are capable of producing strong clusters. The best number of clusters is six clusters. The K-medoids algorithm is better at grouping data than K-means.