Penerapan Algortima Support Vector Machine (SVM) Untuk Prediksi Tingkat Kelulusan Siswa SMA
DOI:
https://doi.org/10.30865/resolusi.v4i4.1753Keywords:
Graduation; Data Mining; Prediction; Support Vector MachineAbstract
Graduation is the desire of every student to be able to complete their studies, and to achieve graduation, students must complete stages such as taking 6 semesters of learning with a school exam score for each subject above 70, and this is a rule in the school. In this study, researchers used student data for the 2022/2023 school year, which researchers took in senior high school number one Lubuklinggau. The method used by the researchers is data mining. Data mining is a term used to describe knowledge discovery in databases. The algorithm the researchers use to predict graduation is the Support Vector Machine (SVM) algorithm because it is able to predict good graduation. In predicting graduation, the accuracy value is 98.81% for XIIth grade students, 96.49% for XIth grade students, and 98.25% for Xth grade students.
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O. Bangun, H. Mawengkang, and S. Efendi, “Metode Algoritma Support Vector Machine (SVM) Linier Dalam Memprediksi Kelulusan Mahasiswa,” J. Media Inform. Budidarma, vol. 6, no. 4, p. 2006, 2022, doi: 10.30865/mib.v6i4.4572.
I. S. K. Idris, Y. A. Mustofa, and I. A. Salihi, “Analisis Sentimen Terhadap Penggunaan Aplikasi Shopee Mengunakan Algoritma Support Vector Machine (SVM),” Jambura J. Electr. Electron. Eng., vol. 5, no. 1, pp. 32–35, 2023, doi: 10.37905/jjeee.v5i1.16830.
P. Arsi and R. Waluyo, “Analisis Sentimen Wacana Pemindahan Ibu Kota Indonesia Menggunakan Algoritma Support Vector Machine (SVM),” J. Teknol. Inf. dan Ilmu Komput., vol. 8, no. 1, p. 147, 2021, doi: 10.25126/jtiik.0813944.
S. M. Fani, R. Santoso, and S. Suparti, “Penerapan Text Mining Untuk Melakukan Clustering Data Tweet Akun Blibli Pada Media Sosial Twitter Menggunakan K-Means Clustering,” J. Gaussian, vol. 10, no. 4, pp. 583–593, 2021, doi: 10.14710/j.gauss.v10i4.30409.
A. I. Tanggraeni and M. N. N. Sitokdana, “Analisis Sentimen Aplikasi E-Government pada Google Play Menggunakan Algoritma Naïve Bayes,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 9, no. 2, pp. 785–795, 2022, doi: 10.35957/jatisi.v9i2.1835.
D. A. Agustina, S. Subanti, and E. Zukhronah, “Implementasi Text Mining Pada Analisis Sentimen Pengguna Twitter Terhadap Marketplace di Indonesia Menggunakan Algoritma Support Vector Machine,” Indones. J. Appl. Stat., vol. 3, no. 2, p. 109, 2021, doi: 10.13057/ijas.v3i2.44337.
Z. Zulfikar, E. S. Podungge, M. I. Saleh, and ..., “Penerapan Data Mining Untuk Memprediksi Tingkat Kelulusan Siswa Menggunakan Algoritma Neural Network,” J. Elektron. Sist. …, vol. 5, no. 1, pp. 7–13, 2022, [Online]. Available: http://jesik.web.id/index.php/jesik/article/view/91
N. Khasanah, A. Salim, N. Afni, R. Komarudin, and Y. I. Maulana, “Prediksi Kelulusan Mahasiswa Dengan Metode Naive Bayes,” Technol. J. Ilm., vol. 13, no. 3, p. 207, 2022, doi: 10.31602/tji.v13i3.7312.
M. R. Qisthiano, P. A. Prayesy, and I. Ruswita, “Penerapan Algoritma Decision Tree dalam Klasifikasi Data Prediksi Kelulusan Mahasiswa,” G-Tech J. Teknol. Terap., vol. 7, no. 1, pp. 21–28, 2023, doi: 10.33379/gtech.v7i1.1850.
D. Y. Kardono, Y. M. Pranoto, and E. Setyati, “Prediksi Kecocokan Jurusan Siswa SMK Dengan Support Vector Machine dan Random Forest,” Teknika, vol. 12, no. 1, pp. 11–17, 2023, doi: 10.34148/teknika.v12i1.567.
A. S. Rahayu, A. Fauzi, and R. Rahmat, “Komparasi Algoritma Naïve Bayes Dan Support Vector Machine (SVM) Pada Analisis Sentimen Spotify,” J. Sist. Komput. dan Inform., vol. 4, no. 2, p. 349, 2022, doi: 10.30865/json.v4i2.5398.
J. Sumpena and N. Kurnia H., “Analisis Prediksi Kelulusan Siswa PKBM Paket C Dengan Metoda Algoritma Naive Bayes,” Tedc, vol. 13, no. 2, pp. 127–133, 2019, [Online]. Available: http://ejournal.poltektedc.ac.id/index.php/tedc/article/view/13
S. N. Hakim, “ANALISIS SENTIMEN PERSEPSI PENGGUNA MYINDIHOME MENGGUNAKAN METODE SUPPORT VECTOR MACHINE (SVM) DAN NAÏVE BAYES CLASSIFIER (NBC) TUGAS,” p. 6, 2021.
S. R. Rani, S. R. Andani, and D. Suhendro, “Penerapan Algoritma K-Nearest Neighbor untuk Prediksi Kelulusan Siswa pada SMK Anak Bangsa,” Pros. Semin. Nas. Ris. Inf. Sci., vol. 1, no. September, p. 670, 2019, doi: 10.30645/senaris.v1i0.73.
R. Thaniket, Kusrini, and E. T. Luthf, “Prediksi Kelulusan Mahasiswa Tepat Waktu Menggunakan Algoritma Support Vector Machine,” J. FATEKSA J. Teknol. dan rekayasa, vol. 13, no. 2, pp. 69–83, 2019.
A. FATIHIN, “Analisis Sentimen Terhadap Ulasan Aplikasi Mobile Menggunakan Metode Support Vector Machine (Svm) Dan Pendekatan Lexicon Based,” p. 103, 2022.
M. Hudha, E. Supriyati, and T. Listyorini, “Analisis Sentimen Pengguna Youtube Terhadap Tayangan #Matanajwamenantiterawan Dengan Metode Naïve Bayes Classifier,” JIKO (Jurnal Inform. dan Komputer), vol. 5, no. 1, pp. 1–6, 2022, doi: 10.33387/jiko.v5i1.3376.
H. O. Lingga Wijaya, T. H. B. Aviani, A. Saputra, and Z. R. Saputra Elsi, “Penerapan Unified Modeling Language Pada Perancangan Sistem Informasi Kartu Kendali Berbasis Web,” Jusikom J. Sist. Komput. Musirawas, vol. 5, no. 2, pp. 145–149, 2020, doi: 10.32767/jusikom.v5i2.1099.
C. Wulandari, M. N. Alamsyah, and L. Layla, “Prediksi Penjualan Kopi Pada Pt. Kopi Cap Lesung Lubuklinggau Menggunakan Metode Single Moving Average (Sma),” JUSIM (Jurnal Sist. Inf. Musirawas), vol. 7, no. 2, pp. 143–154, 2022, doi: 10.32767/jusim.v7i2.1701.
R. Ridwan, H. Lubis, and P. Kustanto, “Implementasi Algoritma Neural Network dalam Memprediksi Tingkat Kelulusan Mahasiswa,” J. Media Inform. Budidarma, vol. 4, no. 2, p. 286, 2020, doi: 10.30865/mib.v4i2.2035.
E. Haryatmi and S. Pramita Hervianti, “Penerapan Algoritma Support Vector Machine Untuk Model Prediksi Kelulusan Mahasiswa Tepat Waktu,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 2, pp. 386–392, 2021, doi: 10.29207/resti.v5i2.3007.
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