Optimasi Correlation-Based Feature Selection Untuk Perbaikan Akurasi Random Forest Classifier Dalam Prediksi Performa Akademik Mahasiswa
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KPAI. 2021. Survei Pelaksanaan Pembelajaran Jarak Jauh (PJJ) dan Sistem Penilaian Jarak Jauh Berbasis Pengaduan KPAI [pdf] Komisi Perlindungan Anak Indonesia. Tersedia di: https://bankdata.kpai.go.id/files /2021/02/ Paparan-Survei-PJJ-KPAI-29042020_Final-update.pdf.
Magdalena, I., Ridwanita, A., & Aulia, B. (2020). Evaluasi Belajar Peserta Didik. PANDAWA, 2(1), 117-127.
Hermawati, F.A. (2013). Data Mining. Yogyakarta: Penerbit Andi.
Xing, W., Chen, X., Stein, J., & Marcinkowski, M. (2016). Temporal predication of dropouts in MOOCs: Reaching the low hanging fruit through stacking generalization. Computers in Human Behavior, 58, 119-129.
Abubakar, Y., & Ahmad, N. B. H. (2017). Prediction of Students Performance in E-Learning Environment Using Random Forest. Inter-national Journal of Innovative Computing, 7(2).
Breiman, L. (2001). Random Forests. Machine learning, 45(1), 5-32. Springer.
Batool, S., Rashid, J., Nisar, M. W., Kim, J., Mahmood, T., & Hussain, A. (2021). A Random Forest students’ performance prediction (rfspp) model based on students’ demographic features. In 2021 Mohammad Ali Jinnah University International Conference on Compu-ting (MAJICC) (pp. 1-4). IEEE
Linawati, S., Nurdiani, S., Handayani, K., & Latifah, L. (2020). Prediksi Prestasi Akademik Mahasiswa Menggunakan Algoritma Random Forest Dan C4. 5. Jurnal Khatulistiwa Informatika, 8(1).
Daniya, T., Geetha, M., & Kumar, K. S. (2020). Classification and regression trees with Gini index. Advances in Mathematics: Scientific Journal, 9(10), 8237-8247.
Djatna, T, & Yasuhiko M. (2008). "Pembandingan Stabilitas Algoritma Seleksi Fitur Menggunakan Transformasi Ranking Normal." Jurnal Ilmiah Ilmu Komputer, vol. 6, no. 2.
Ali, H., Salleh, M. M., Saedudin, R., Hussain, K., & Mushtaq, M. F. (2019). Imbalance class problems in data mining: a review. Indone-sian Journal of Electrical Engineering and Computer Science, 14(3), 1560-1571.
Suyanto, S. A. N. (2018). Klasifikasi Jenis Infeksi Berdasarkan Hasil Pemeriksaan Leukosit Menggunakan K-Nearest Neighbor (KKN). Tersedia di: https://repositori.usu.ac.id/ handle/123456789/11694 [Diakses 28 Januari 2018]
DOI: http://dx.doi.org/10.26798/jiko.v6i2.651
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