Penerapan Data Mining Klasifikasi Tingkat Pemahaman Siswa Pada Pelajaran Matematika

 (*)Tri Novika Mail (STIKOM Tunas Bangsa, Pematangsiantar, Indonesia)
 Poningsih Poningsih (AMIK Tunas Bangsa, Pematangsiantar, Indonesia)
 Harly Okprana (STIKOM Tunas Bangsa, Pematangsiantar, Indonesia)
 Agus Perdana Windarto (STIKOM Tunas Bangsa, Pematangsiantar, Indonesia)
 Hasudungan Siahaan (STIKOM Tunas Bangsa Pematangsiantar, Indonesia)

(*) Corresponding Author

Submitted: September 15, 2020; Published: January 22, 2021

DOI: http://dx.doi.org/10.30865/mib.v5i1.2498

Abstract

The purpose of the research is to classify the concept of understanding students in Mathematics lessons. In the learning process teaching students understanding learning materials is very important. The attainment of student understanding is a function of the being of an educator. Many formulas and concepts to understand make it difficult for students to solve math problems. The data source was obtained from the results of a math comprehension questionnaire of eighth graders at Tamansiswa Tapian Dolok Private Junior High School. The classification method used is the C4.5 Algorithm and assisted with RapidMiner software. Attributes used are student interests, how students learn, student motivation, how to teach teachers, learning media, and infrastructure facilities. The results of the calculation of entropy values and attribute gains obtained 15 rules of mathematical comprehension decisions with 9 rules of understanding status and 6 rules of inconsistency status. Classification modeling with C4.5 Algorithm on RapidMiner obtained 96.00% accuracy Classification with C4.5 Algorithm can be applied and provide new information about the classification of student comprehension concepts in math lessons

Keywords


Data Mining; Classification; Algorithm C4.5; Student Comprehension; Mathematics

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References

S. M. Khasanah, “Pengaruh Pemahaman Konsep dan Motivasi Belajar Terhadap Hasil Belajar Materi Trigonometri Siswa Kelas X MIA 2 di MAN 3 Tulungagung,” INSTITUT AGAMA ISLAM NEGERI TULUNGAGUNG, 2018.

P. P. P. A. N. . F. I. R. . Zer, D. Hartama, and S. R. Andani, “Analisa Faktor Dominan Mahasiswa Kesulitan Memahami Bahasa Pemrograman,” in Prosiding Seminar Nasional Riset Information Science (SENARIS), 2019, no. September, pp. 492–500.

R. T. Vulandari, Data Mining Teori dan Aplikasi Rapidminer, 1st ed. Yogyakarta: Peberbit Gaya Media, 2017.

S. Hawani, D. Hartama, A. P. Windarto, and Solikun, “PENERAPAN C 4.5 UNTUK MENENTUKAN CALON SUAMI TERBAIK DALAM PERNIKAHAN PADA KANTOR KUA SIANTAR MARTOBA PEMATANGSIANTAR,” vol. 1, pp. 64–68, 2016.

N. Rofiqo, A. P. Windarto, and E. Irawan, “Penerapan Algoritma C4 . 5 pada Penentuan Tingkat Pemahaman Mahasiswa Terhadap Matakuliah,” in Prosiding Seminar Nasional Riset Information Science (SENARIS), 2019, no. September, pp. 307–317.

D. R. Sitorus, A. P. Windarto, D. Hartama, and I. S. Damanik, “PENERAPAN KLASIFIKASI C4.5 DALAM MENINGKATKAN SISTEM PEMBELAJARAN MAHASISWA,” KOMIK (Konferensi Nas. Teknol. Inf. dan Komputer), vol. 3, no. 1, pp. 593–597, 2019.

A. P. Windarto, U. Indriani, M. R. Raharjo, and L. S. Dewi, “Bagian 1: Kombinasi Metode Klastering dan Klasifikasi (Kasus Pandemi Covid-19 di Indonesia),” J. Media Inform. Budidarma, vol. 4, no. 3, p. 855, 2020.

S. H. Waluyo and Prihandoko, “Klasifikasi Pemanfaat Program Beras Sejahtera ( RASTRA ) Berdasarkan Tingkat Kemiskinan Dengan Menggunakan Algoritma Decision Tree C4 . 5 Berbasis Particle Swarm Optimization,” vol. 7, no. 2, pp. 19–24, 2017.

D. I. G. Hts, “Penerapan Algoritma C4.5 Untuk Penerimaan Siswa Baru Pada SMA Al-Azhar Medan,” J. Sains Komput. Inform., vol. 2, no. 2, pp. 130–139, 2018.

I. S. Damanik, A. P. Windarto, A. Wanto, Poningsih, S. R. Andani, and W. Saputra, “Decision Tree Optimization in C4.5 Algorithm Using Genetic Algorithm,” in Journal of Physics: Conference Series, 2019, vol. 1255, no. 1.

N. Khotimah and D. Istiawan, “Perbandingan Algoritma C4.5, Naïve Bayes dan K-Nearest Neighbour untuk Prediksi Lahan Kritis di Kabupaten Pemalang,” Urecol, vol. 7, no. 1, pp. 41–50, 2018.

Taufiq and Y. Yudihartanti, “Penerapan Algoritma C4.5 Klasifikasi Tingkat Kelulusan Mahasiswa,” in Seminar Nasional Ilmu Komputer (SOLITER), 2019, vol. 2, pp. 153–162.

A. Rohman, V. Suhartono, and C. Supriyanto, “Penerapan Agoritma C4.5 Berbasis Adaboost Untuk Prediksi Penyakit Jantung,” J. Teknol. Inf., vol. 13, pp. 13–19, 2017.

D. Nofriansyah and G. W. Nurcahyo, Algoritma Data Mining Dan Pengujian. Deepublish, 2019.

N. Yahya and A. Jananto, “Komparasi Kinerja Algoritma C.45 dan Naive Bayes UNTUK Prediksi Kegiatan Penerimaan Mahasiswa Baru (Studi Kasus : Universitas STIKUBANK Semarang),” in SENDI_U, 2019, pp. 221–228.

H. M. Nawawi, S. Rahayu, M. J. Shidiq, and J. J. Purnama, “Algoritma C4.5 untuk Memprediksi Pengambilan Keputusan Memilih Deposito Berjangka,” J. Techno Nuasa Mandiri, vol. 16, no. 1, pp. 65–72, 2019.

A. H. Nasrullah, “Penerapan Metode C4.5 Untuk Klasifikasi Mahasiswa Berpotensi Drop Out,” Ilk. J. Ilm., vol. 10, no. 2, pp. 244–250, 2018.

N. Anwar, A. Pranolo, and R. Kurnaiwan, “Grouping the community health center patients based on the disease characteristics using C4.5 decision tree,” in IOP Conference Series: Materials Science and Engineering, 2018.

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