Estimasi Keberhasilan Siswa dalam Pemodelan Data Berbasis Learning Menggunakan Algoritma Support Vector Machine

Suryani Suryani(1), Mustakim Mustakim(2*),

(1) Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru
(2) Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru
(*) Corresponding Author

Abstract


SMK Negeri 5 Pekanbaru aims to prepare competent graduates who can compete in the global market. The realization of these goals is influenced by student achievement at school. Student achievements determine the ability of students to work in certain fields. Based on observations, it is known that student achievement at SMK Negeri 5 Pekanbaru tend to be low. This is also shown by the data that has been collected through the Curriculum section. Based on the data, there can be extraction using the supervised learning method to make a classification model of student achievements. The supervised learning algorithm used in this research is a Support Vector Machine (SVM). The data used in this study are student's data grade X SMK Negeri 5 Pekanbaru in 2020 totaling 160 data. The classification process is carried out by applying the GridSearch method to find the best kernel to be implemented. Based on the implementation of GridSearch, the kernel to be used is Radial Basis Function (RBF) with Cost (C) and Gamma (?) parameters. Based on 16 experiments with different parameter values, the best classification results are obtained using the value of Cost (C) = 0.1 and the value of Gamma (?) = 0.01, with accuracy values of 94%.


Keywords


Classification; Students Achievement; Radial Basis Function; Supervised Learning; Support Vector Machine

Full Text:

PDF

References


M. Prasojo and J. Triwidianti, Prediksi Prestasi Siswa SMK Masuk Pasar Kerja Menggunakan Teknik Data Mining (Studi Kasus SMKN 1 KotaAgung Timur Tanggamus, Lampung), Seminar Nasional Hasil Penelitian dan Pengabdian Masyarakat, pp. 134150, 2021.

SMK Negeri 5 Pekanbaru Official Web. https://smkn5pekanbaru.sch.id/ (accessed Jul. 28, 2022).

J. Triwidianti, F. Y. Alfian, and M. Prasojo, Perbandingan Metode Data Mining Untuk Prediksi Prestasi Siswa Tingkat Pendidikan Menengah Kejuruan Pada Sekolah Menengah Kejuruan Negeri (SMKN 1) Gadingrejo Pringsewu Lampung, Seminar Nasional Hasil Penelitian dan Pengabdian Masyarakat, pp. 126133, 2021.

S. Marpaung, I. Sekolah Tinggi Ilmu Komputer Stikom Tunas Bangsa Jl Sudirman, S. Barat, K. Pematang Siantar, and S. Utara, PENERAPAN METODE NAVE BAYES DALAM MEMPREDIKSI PRESTASI SISWA DI SMA NEGERI 1 PANOMBEIAN PANEI, Jurnal Sistem Informasi dan Ilmu Komputer Prima, vol. 4, no. 2, 2021.

A. Kesumawati and D. T. Utari, Predicting patterns of student graduation rates using Nave bayes classifier and support vector machine, in AIP Conference Proceedings, Oct. 2018, vol. 2021. doi: 10.1063/1.5062769.

E. Haryatmi and S. Pramita Hervianti, Penerapan Algoritma Support Vector Machine Untuk Model Prediksi Kelulusan Mahasiswa Tepat Waktu, Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 5, no. 2, pp. 386392, Apr. 2021, doi: 10.29207/resti.v5i2.3007.

S. Widaningsih, PERBANDINGAN METODE DATA MINING UNTUK PREDIKSI NILAI DAN WAKTU KELULUSAN MAHASISWA PRODI TEKNIK INFORMATIKA DENGAN ALGORITMA C4,5, NAVE BAYES, KNN DAN SVM, Jurnal Tekno Insentif, vol. 13, no. 1, pp. 1625, Apr. 2019, doi: 10.36787/jti.v13i1.78.

S. Dewi Purba, L. Harahap, J. Franky, and R. Panggabean, PREDICTION OF STUDENTS DROP OUT WITH SUPPORT VECTOR MACHINE ALGORITHM, 2021.

W. S. Dharmawan, KOMPARASIALGORITMA KLASIFIKASISVM-PSO DAN C4.5-PSO DALAMPREDIKSI PENYAKIT JANTUNG, Jurnal Informatika, Manajemen dan Komputer, vol. 13, no. 2, pp. 3141, 2021.

A. Pratama, R. Cahya Wihandika, and D. E. Ratnawati, Implementasi Algoritme Support Vector Machine (SVM) untuk Prediksi Ketepatan Waktu Kelulusan Mahasiswa, 2018. [Online]. Available: http://j-ptiik.ub.ac.id

V. Riyanto, A. Hamid, and R. Ridwansyah, Prediction of Student Graduation Time Using the Best Algorithm, Indonesian Journal of Artificial Intelligence and Data Mining, vol. 2, no. 1, pp. 19, Mar. 2019, doi: 10.24014/ijaidm.v2i1.6424.

A. Mailana, A. A. Putra, S. Hidayat, and A. Wibowo, Comparison of C4.5 Algorithm and Support Vector Machine in Predicting the Student Graduation Timeliness, Jurnal Online Informatika, vol. 6, no. 1, pp. 1116, Jun. 2021, doi: 10.15575/join.v6i1.608.

R. H. Sukarna and Y. Ansori, IMPLEMENTASI DATA MINING MENGGUNAKAN METODE NAIVE BAYES DENGAN FEATURE SELECTION UNTUK PREDIKSI KELULUSAN MAHASISWA TEPAT WAKTU, 2022.

Gunawan, Hanes, and Catherine, C4.5, K-Nearest Neighbor, Nave Bayes and Random Forest Algorithms Comparison to Predict Students On Time Graduation, Indonesian Journal of Artificial Intelligence and Data Mining (IJAIDM), vol. 4, no. 2, pp. 6271, 2021, doi: 10.24014/ijaidm.v4i2.10833.

F. Elfaladonna and A. Rahmadani, ANALISAMETODE CLASSIFICATION-DECISSION TREE DAN ALGORITMA C.45 UNTUK MEMPREDIKSI PENYAKIT DIABETES DENGAN MENGGUNAKAN APLIKASI RAPID MINER, Sciene and Information Technology Journal, vol. 2, no. 1, pp. 1017, 2019.

R. Febryani and T. Arifin, OPTIMASI NAVE BAYES MENGGUNAKAN PSOUNTUK TINGKATKEBERHASILAN CRYOTHERAPY PADA PENYAKIT KUTIL, JURNAL RESPONSIF, vol. 3, no. 2, 2021, [Online]. Available: http://ejurnal.ars.ac.id/index.php/jti

R. Thaniket and E. Taufik Luthf, PREDIKSI KELULUSAN MAHASISWA TEPAT WAKTU MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE, 2020.

O. Tias Mugi Rahayu, B. Arnawisuda Ningsi, and I. Arofah, KLASIFIKASI KETEPATAN WAKTU KELULUSAN MAHASISWA DENGAN METODE NAVE BAYES, vol. 15, no. 8, pp. 49935000, 2021, [Online]. Available: http://ejurnal.binawakya.or.id/index.php/MBI

G. Sailasya and G. L. Aruna Kumari, Analyzing the Performance of Stroke Prediction using ML Classification Algorithms, 2021. [Online]. Available: www.ijacsa.thesai.org

H. C. Husada and A. S. Paramita, Analisis Sentimen Pada Maskapai Penerbangan di Platform Twitter Menggunakan Algoritma Support Vector Machine (SVM), Teknika, vol. 10, no. 1, pp. 1826, Feb. 2021, doi: 10.34148/teknika.v10i1.311.

N. Fitriyah, B. Warsito, D. Asih, and I. Maruddani, ANALISIS SENTIMEN GOJEK PADA MEDIA SOSIAL TWITTER DENGAN KLASIFIKASI SUPPORT VECTOR MACHINE (SVM), JURNAL GAUSSIAN, vol. 9, no. 3, pp. 376390, 2020, [Online]. Available: https://ejournal3.undip.ac.id/index.php/gaussian/

N. Rachmalia Feta and A. Rahmat Ginanjar, KOMPARASI FUNGSI KERNEL METODE SUPPORT VECTOR MACHINE UNTUK PEMODELAN KLASIFIKASI TERHADAP PENYAKIT TANAMAN KEDELAI, BRITech (Jurnal Ilmiah Ilmu Komputer, Sains dan Teknologi Terapan), vol. 1, no. 1, 2019.

A. Kesumawati and D. T. Utari, Predicting patterns of student graduation rates using Nave bayes classifier and support vector machine, in AIP Conference Proceedings, Oct. 2018, vol. 2021. doi: 10.1063/1.5062769.

E. Supriyadi and D. I. Sensuse, OPTIMASI ALGORITMA SUPPORT VECTOR MACHINE DENGAN PARTICLE SWARM OPTIMIZATION DALAM MENDETEKSI KETEPATAN WAKTU KELULUSAN MAHASISWA : STUDI KASUS POLTEK LP3I JAKARTA KAMPUS DEPOK, 2015. [Online]. Available: http://www.nusamandiri.ac.id,




DOI: http://dx.doi.org/10.61944/bids.v1i2.36

Refbacks

  • There are currently no refbacks.


Copyright (c) 2022 Suryani Suryani, Mustakim Mustakim

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Bulletin of Informatics and Data Science
Asosiasi Peneliti Data Science Indonesia
Email: pdsi.bids@gmail.com
This work is licensed under a Creative Commons Attribution 4.0 International License.