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All Journal Sistemasi: Jurnal Sistem Informasi Building of Informatics, Technology and Science Bulletin of Network Engineer and Informatics (BUFNETS)
Tamuntuan, Virginia
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Computer Science & IT Electrical & Electronics Engineering

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Journal : Building of Informatics, Technology and Science

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Analisis Perbandingan Kinerja Algoritma Klasifikasi Pada Mahasiswa Berpotensi Dropout Tamuntuan, Virginia; Kusrini, Kusrini; Kusnawi, Kusnawi
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5658

Abstract

This research aims to compare the performance levels of two data mining classification algorithms, namely Support Vector Machine and Neural Network Backpropagation, using the K-fold cross-validation method. The data used consists of graduates from 2019 to 2023 at STMIK Multicom Bolaang Mongondow. A total of 80% of the 200 data points were used as training data, while the remaining 20% were used as testing data. K-fold cross-validation was conducted with K set to 5. The results of the study indicate that the Support Vector Machine algorithm achieved an accuracy of 80%, recall of 80%, and precision of 35%, while the Neural Network Backpropagation algorithm achieved an accuracy of 77%, recall of 63%, and precision of 44%.
Co-Authors Ardiansyah, Fachri Kusnawi Kusnawi Kusrini, Kusrini Mamangki, Hamdi Sentoso, Thedjo Tuntun, Ritham Wangsa, Sabda Sastra