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All Journal KALAMATIKA Jurnal Pendidikan Matematika INTERNAL (Information System Journal)
Dwi Sulistya Kusumaningrum
Universitas Buana Perjuangan Karawang
Computer Science & IT Education Mathematics Other

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PERBANDINGAN KEBIASAAN BERPIKIR DAN HASIL BELAJAR MATEMATIK ANTARA SISWA SANTRI DAN NON-SANTRI Santi Arum Puspita Lestari; Dwi Sulistya Kusumaningrum
KALAMATIKA Jurnal Pendidikan Matematika Vol 4 No 2 (2019): KALAMATIKA November 2019
Publisher : FKIP Universitas Muhammadiyah Prof. DR. HAMKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (424.722 KB) | DOI: 10.22236/KALAMATIKA.vol4no2.2019pp141-150

Abstract

This study aims to examine differences in mathematical habits of mind and mathematical learning outcomes between santri students and non-santri students. The design of this study is comparative research. The research data was collected by interviewing a mathematics teacher and spreading the scale of mathematical habits of mind. This research was conducted in two schools in Karawang precisely in Rawamerta. The schools used are private schools run by pesantren foundations for the sample of santri students and public schools as samples of non-santri students. From the results of data processing in the results obtained that there is no difference in mathematical habits of mind between santri students and non-santri students. In addition, there is no difference in mathematical learning outcomes between students of santri and non-santri. Meanwhile, there is an association between mathematical habits of mind and mathematical learning outcomes of students.
The Performance Comparison of Classification Algorithm in Order to Detecting Heart Disease Chepy Sonjaya; Anis Fitri Nur Masruriyah; Dwi Sulistya Kusumaningrum; Adi Rizky Pratama
INTERNAL (Information System Journal) Vol. 5 No. 2 (2022)
Publisher : Masoem University

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Abstract

Heart disease in Indonesia, especially in the productive age, there is always an increase in the number of cases. The main cause of the increase in the number of heart patients is an unhealthy lifestyle and diet. The increase in patients with heart disease also has an impact on decreasing the standard of living. With this in mind, there is a need for research related to comparing classification methods on heart disease datasets. The dataset obtained is not balanced so that an oversampling technique is needed. The oversampling technique used is SMOTE. This research method uses Support Vector Machine (SVM) and Logistic Regression (LR). In order for this research method to be applied successfully, the data acquisition, data pre-processing and data transformation techniques are used to ensure accurate results. The model evaluation technique used is K-Fold Cross Validation. Based on the results of the analysis, it showed that the data partition using k-fold cross validation without oversampling gets the same accuracy value but the precision value is quite low. Conversely, if using the SMOTE technique, the accuracy value is as good as the precision value. The results of the SVM accuracy value get a value of 91.69%. LR is 91.76%. While the results of the SVM precision value of 57.81% and LR 54.82%. If using the SVM oversampling technique, the score is 75.79% and the LR is 75.84%. Meanwhile, the precision value obtained in SVM is 75.74%. At LR by 74.77%.
Co-Authors Adi Rizky Pratama Anis Fitri Nur Masruriyah Chepy Sonjaya Lestari, Santi Arum Puspita