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Klasifikasi Keberhasilan Melanjutkan Pendidikan Tingkat SMA Provinsi Banten Menggunakan CART dan Random Forest Muhammad Amirullah Yusuf Albasia; Budi Susetyo; I. Made Sumertajaya
Xplore: Journal of Statistics Vol. 7 No. 3 (2018): 31 Desember 2018
Publisher : Department of Statistics, IPB

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Abstract

Dropout rate in Indonesia has a higher percentage as education levels grow. The percentage of continuing education to senior high school in Indonesia is at 77.50%. Banten is one of the provinces that has a higher dropout percentage when the education level is also higher. Beside that, Banten is the second lowest province in Indonesia in the percentage of continuing education to senior high school that is 68.92%. The study examines importance variables and performance classification that is generated by classification tree and random forest. The results showed that importance variables that is generated by both methods were same, that is per capita expenditure (X8) and proportion of household members who are less educated than senior high school (X10). Then, based on the AUC value that obtained by 10-fold cross validation showed that random forest is better than classification tree. Experiments with values ​​of accuracy, sensitivity, and specificity at some cuts off values ​​also show that random forest can provide more optimum prediction performance than classification tree.
Penerapan Regresi Peubah Ganda untuk Menentukan SNP yang Berpengaruh terhadap Prestasi Akademik SMA/MA Wulan Andriyani Pangestu; Budi Susetyo; Rahma Anisa
Xplore: Journal of Statistics Vol. 7 No. 3 (2018): 31 Desember 2018
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/xplore.v7i3.130

Abstract

The evaluation step in school accreditation process includes eight components of national education standard (SNP). The result of accreditation from the evaluation is believed to explicate the academic achievement of student, in this case is National Examination (UN). Thus, it is necessary to further observe the relation between the accreditation results and the score of national examination. One of the analysis that can be used is regression analysis, it is used to observe the relation between the accreditation result and the sroce of national examination also to identify the SNP components that affect the national examination score. However, since the study was conducted at senior high school level where the national examination score for this level covers six subjects, the analysis used is no longer a simple regression but a multiple variable regression. It is because of the relationship between the score of the national examination that characterizes an academic achievement. The application result of multiple variable regression method shows that there is a relation between SNP and national examination score.
Faktor-Faktor yang Berpengaruh dalam Mendapatkan Pekerjaan bagi Lulusan Statistika IPB dengan Menggunakan Metode CHAID Aulia Dwi Oktavia; Aam Alamudi; Budi Susetyo
Xplore: Journal of Statistics Vol. 8 No. 1 (2019): 30 April 2019
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/xplore.v8i1.156

Abstract

Unemployment is one of the economic problems in Indonesia. Judging from the level of education that was completed there were unemployment from the level of college graduates. This encourages the level of competition in getting jobs to be more stringent, so that college graduates (bachelor of Statistics in IPB) must have the preparation of various factors to maintain the quality of their graduates. The quality of college graduates can be seen from the length of time waiting to get a job. This study aims to determine the influential factors in getting a job for graduates of the IPB Statistics degree, so that the CHAID method can be used in this study. The results of CHAID's analysis in this study in the form of tree diagrams using α = 10% explained that the factors influencing the waiting period variables were sex, internship, and the ability to master statistical software, where the accuracy value generated by the classification model was 79.3 %.