Article Per Year (5 Year)
p-Index From 2019 - 2024
1.606
P-Index
This Author published in this journals
All Journal EKSAKTA: Journal of Sciences and Data Analysis Jurnal Ilmu Komputer dan Informasi JIPM (Jurnal Ilmiah Pendidikan Matematika) JPPM (JURNAL PENELITIAN DAN PEMBELAJARAN MATEMATIKA) Didaktis: Jurnal Pendidikan dan Ilmu Pengetahuan MUST: Journal of Mathematics Education, Science and Technology Journal of Education and Instruction (JOEAI) Jounal of Education and Teaching (JET) Dedikasi Sains dan Teknologi (DST) Pedagogy : Jurnal Pendidikan Matematika Jurnal Pendidikan Matematika
Wahyuni Suryaningtyas, Wahyuni
Universitas Muhammadiyah Surabaya
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

Published : 18 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : EKSAKTA: Journal of Sciences and Data Analysis

 1 
Data Mining Approach for Educational Decision Support Sinta Septi Pangastuti; Kartika Fithriasari; Nur Iriawan; Wahyuni Suryaningtyas
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 2, ISSUE 1, February 2021
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol2.iss1.art5

Abstract

data mining techniques in education sector have begun to evolve, along with the development of technology and the amount of data that can be stored in an education database storage system. One of them is a database of Bidikmisi scholarships in Indonesia. The Bidikmisi data used in this study will be classified using classification data mining technique. The technique that used in this study is random forest in combination with boosting algorithm and bagging algorithms. These algorithms also combine with SMOTE algorithm to handling the imbalance class in dataset. Based on the performance criteria G-mean and AUC, the algorithm combines with SMOTE tended to be better. The classification accuracy of each method being more than 90%
Co-Authors Afrenda Miftahul Janna Agus Solikin Agus solikin Anindya Apriliyanti Pravitasari Brodjol Sutija Suprih Ulama Chusnal Ainy Davi Maya Maghrobi Arum Endang Suprapti Endang Suprapti Endang suprapti Erna Lus Diana Febriana Kristanti Fithriasari, Kartika Heny Faridah Iis Holisin Irwan Susanto Kalam Ilmiawan Marissa Yuliana, Marissa Muhammad Rois Rafiqurrahman Musnidatul Millah Arief Nur Iriawan Nur Laili Nurusy Syahrotir Rohmah Sandha Soemantri Sandha Soemantri Sandha Soemantri Shoffan Shoffa Sinta Septi Pangastuti Syifaul Khudriyah Wudjud Soepeno Diharjo