Pengembangan Aplikasi Web Berbasis Algoritma C5.0 Memprediksi Prestasi Siswa Berdasarkan Gaya Belajar
Azkiya Zahra, Yusuf Ramadhan Nasution, Aidil Halim Lubis
Abstract
The application of data mining in education has recently been developed. It is used to predict many aspects of learning. This research aims at finding out how to develop a web-based application using algorithm C5.0 to predict students’ achievement based on their learning styles. This study was conducted in Madrasah Tsanawiyah Darul Ilmi Batangkuis, Kabupaten Deli Serdang, Sumatera Utara. This research uses a CRISP-DM method to develop a web-based application. The result of data analysis shows that data mining can be used to predict students learning achievement based on their learning styles, which are categorized as visual, auditory, and kinesthetic. Predictions on students’ learning achievement can be found by using this application. The accuracy level of this application gains 100% and the error deviation is 0%. The data manual calculation of this research shows that the learning style that can be developed is kinesthetic.
Keywords
CRISP-DM, data mining, learning style, learning achievement, web application development, Algoritmh C5.0
Chapman, & Hall. (2011). Handbook of Educational Data Mining (C. Romero, S. Ventura, M. Pechenizkiy, & R. Baker (ed.)). CRC Press, Yaylor & Francis Group.
Jesús R. Sifonte, J. V. R.-P. (2017). Reliability Centered Maintenance-Reengineered (1st Editio). Productivity Press. https://doi.org/https://doi.org/10.1201/9781315207179
Kurilovas, E. (2019). Advanced machine learning approaches to personalise learning: learning analytics and decision making. Behaviour and Information Technology, 38(4), 410–421. https://doi.org/10.1080/0144929X.2018.1539517
Pratiwi, R., Hayati, M. N., & Prangga, S. (2020). PERBANDINGAN KLASIFIKASI ALGORITMA C5 . 0 DENGAN CLASSIFICATION AND REGRESSION TREE ( STUDI KASUS : DATA SOSIAL KEPALA KELUARGA MASYARAKAT DESA TELUK BARU KECAMATAN MUARA ANCALONG TAHUN 2019 ) Comparison of C5 . 0 Algorithm Classification with Classificat. 14(2), 267–278.