Classification Analysis of Unilak Informatics Engineering Students Using Support Vector Machine (SVM), Iterative Dichotomiser 3 (ID3), Random Forest and K-Nearest Neighbors (KNN)
DOI:
https://doi.org/10.25299/itjrd.2022.8912Keywords:
Classification, Study Period, Support Vector Machine (SVM), Itterative Dichotomiser 3, Random Forest, K-Nearest Neighbor (KNN)Abstract
This research is entitled “Classification Analysis of the Study Period of Informatics Engineering Study Program Students at Unilak with the Support Vector Machine (SVM), Iterative Dichotomiser 3 (ID3), Random Forest and K-Nearest Neighbors (KNN)" method. an attempt to understand whether there are factors that influence the length of a student's study period. Basically, the length of the study period is not a measure of a student's non-academic academic ability, but most people judge that students with a study period of more than 8 semesters or long are not good. Therefore, the researcher chose to classify the factors that affect the length of the student's study period at the Faculty of Computer Science, Lancang Kuning University. This study uses 4 (four) calculation methods. With the several methods used, the authors can compare the results of the four calculation methods so that they can determine which method is better calculated. The result of this research is a comparison between 4 (four) calculation methods in determining which method has good classification ability
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R. A. Permana and S. Sahara, “Metode Support Vector Machine Sebagai Penentu Kelulusan Mahasiswa pada Pembelajaran Elektronik,” Jurnal Khatulistiwa Informatika, vol. 7, no. 1, pp. 50–58, 2019, doi: 10.31294/jki.v7i1.5743.
A. M. Pravina, I. Cholissodin, and P. P. Adikara, “Analisis Sentimen Tentang Opini Maskapai Penerbangan pada Dokumen Twitter Menggunakan Algoritme Support Vector Machine ( SVM ),” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer (J-PTIIK) Universitas Brawijaya, vol. 3, no. 3, pp. 2789–2797, 2019.
N. A. Setiawan, P. A. Venkatachalam, and A. F. M. Hani, “Diagnosis of Coronary Artery Disease Using Artificial Intelligence Based Decision Support System,” Proceedings of the International Conference on Man-Machine Systems (ICoMMS), no. October, pp. 1C3 1-1C3 5, 2009.
B. Santosa, “1 . Ide Dasar Support Vector Machine,” no. x, 2015.
D. Ispriyanti and A. Hoyyi, “Analisis klasifikasi masa studi mahasiswa prodi statistika undip dengan metode support vector machine (svm) dan id3 (iterative dichotomiser 3) 1,2,” vol. 9, no. 1, pp. 15–29, 2016, doi: 10.14710/medstat.9.1.15-29.
A. E. Khedr, A. M. Idrees, and A. I. el Seddawy, “Enhancing Iterative Dichotomiser 3 algorithm for classification decision tree,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 6, no. 2, pp. 70–79, 2016, doi: 10.1002/widm.1177.
R. A. Saputra et al., “PENERAPAN METODE ITERATIVE DICHOTOMIZER 3 ( ID 3 ) UNTUK MENENTUKAN BEASISWA BERPRESTASI PADA SMP PGRI,” vol. 15, no. 1, pp. 35–40, 2019.
N. K. Dewi, U. D. Syafitri, S. Y. Mulyadi, M. D. Statistika, and D. Statistika, “Penerapan Metode Random Forest Dalam Driver Analysis,” Forum Statistika Dan Komputasi, vol. 16, no. 1, pp. 35–43, 2011.
I. M. Budi Adnyana, “Prediksi Lama Studi Mahasiswa Dengan Metode Random Forest (Studi Kasus : Stikom Bali),” CSRID (Computer Science Research and Its Development Journal), vol. 8, no. 3, pp. 201–208, 2016, doi: 10.22303/csrid.8.3.2016.201-208.
K. A. Sambodo, M. I. Rahayu, N. Indriasari, and M. Natsir, “Klasifikasi Hutan-Non Hutan Data Alos Palsar Menggunakan Metode Random Forest,” Prosiding Seminar Nasional Penginderaan Jauh 2014, pp. 120–127, 2014.
R. A. Permana and S. Sahara, “Metode Support Vector Machine Sebagai Penentu Kelulusan Mahasiswa pada Pembelajaran Elektronik,” Jurnal Khatulistiwa Informatika, vol. 7, no. 1, pp. 50–58, 2019, doi: 10.31294/jki.v7i1.5743.
A. M. Pravina, I. Cholissodin, and P. P. Adikara, “Analisis Sentimen Tentang Opini Maskapai Penerbangan pada Dokumen Twitter Menggunakan Algoritme Support Vector Machine ( SVM ),” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer (J-PTIIK) Universitas Brawijaya, vol. 3, no. 3, pp. 2789–2797, 2019.
N. A. Setiawan, P. A. Venkatachalam, and A. F. M. Hani, “Diagnosis of Coronary Artery Disease Using Artificial Intelligence Based Decision Support System,” Proceedings of the International Conference on Man-Machine Systems (ICoMMS), no. October, pp. 1C3 1-1C3 5, 2009.
B. Santosa, “1 . Ide Dasar Support Vector Machine,” no. x, 2015.
D. Ispriyanti and A. Hoyyi, “Analisis klasifikasi masa studi mahasiswa prodi statistika undip dengan metode support vector machine (svm) dan id3 (iterative dichotomiser 3) 1,2,” vol. 9, no. 1, pp. 15–29, 2016, doi: 10.14710/medstat.9.1.15-29.
A. E. Khedr, A. M. Idrees, and A. I. el Seddawy, “Enhancing Iterative Dichotomiser 3 algorithm for classification decision tree,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 6, no. 2, pp. 70–79, 2016, doi: 10.1002/widm.1177.
R. A. Saputra et al., “PENERAPAN METODE ITERATIVE DICHOTOMIZER 3 ( ID 3 ) UNTUK MENENTUKAN BEASISWA BERPRESTASI PADA SMP PGRI,” vol. 15, no. 1, pp. 35–40, 2019.
N. K. Dewi, U. D. Syafitri, S. Y. Mulyadi, M. D. Statistika, and D. Statistika, “Penerapan Metode Random Forest Dalam Driver Analysis,” Forum Statistika Dan Komputasi, vol. 16, no. 1, pp. 35–43, 2011.
I. M. Budi Adnyana, “Prediksi Lama Studi Mahasiswa Dengan Metode Random Forest (Studi Kasus : Stikom Bali),” CSRID (Computer Science Research and Its Development Journal), vol. 8, no. 3, pp. 201–208, 2016, doi: 10.22303/csrid.8.3.2016.201-208.
K. A. Sambodo, M. I. Rahayu, N. Indriasari, and M. Natsir, “Klasifikasi Hutan-Non Hutan Data Alos Palsar Menggunakan Metode Random Forest,” Prosiding Seminar Nasional Penginderaan Jauh 2014, pp. 120–127, 2014.
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