Upaya Meningkatkan Aktivitas Dan Hasil Belajar Perpajakan Mahaisswa Dengan Menggunakan Strtagei Pembelajaran Model Kooperatif Grup Investigation

Abstract

<jats:p>This study aims at improving the activities and learning outcomes of student taxation by applying the learning strategy of the Investigation Group (IG).This type of research is classroom action research (CAR) which has been carried out in two cycles.The research strategy used is the learning structure of the Investigation Group (IG) with the research design of the Two Groups; Pre-test and post-test design.The results of observations that have been conducted show that student learning activities were improved, namely cycle I there were 5 students or 20.84% ​​for active and very active criteria while in the second cycle of meeting I there are 14 students or as many as 58.33% for active and very active criteria, then on cycle IIon meeting II, there are 17 students or as many as 70.83% for active and very active criteria. This shows an increase in student activity from the first and second cycle. Whereas the results of student learning tests that were carried out obtained data on the average value of learning outcomes in the pre-test with an average score of 49.16 or as many as 16.66%. In the post-test cycle I the researchers applied the learning structure the Investigation Group (IG), there was an increase in scores with an average of 67.08 or as many as 62.5% of the total population while the average value in the second cycle increased to 74.16 or as many as 79.165%. Based on the results of the above research it can be concluded that applying the learning strategy the Investigation Group (IG) can improve student activities and learning outcomes.</jats:p>

Application of the C5.0 Algorithm to Determine the Level of Public Satisfaction with the E-KTP Recording Service at the Bandar Sub-District Office

“…From the problems above, the authors use classification data mining techniques with the C5.0 Algorithm to get a more efficient solution to community satisfaction with e-KTP recording services. The C5.0 algorithm is a decision tree-based algorithm which is a refinement of the ID3 and C4.5 algorithms formed by Ross Quinlan in 1987 [10], [11], [12]. The C5.0 algorithm can be used to examine various things, including research related to predicting cleaning service acceptance, the results obtained from the research can be seen from the criteria of the employment contract [10].…”