DATA MINING KLASIFIKASI KEPRIBADIAN  SISWA SMP NEGERI 5 JEPARA MENGGUNAKAN  METODE DECISION TREE ALGORITMA C4.5

Adjie Kukuh Wahyudi; Noor Azizah; Heru Saputro

doi:10.34001/jister.v2i2.392

Adjie Kukuh Wahyudi Universitas Islam Nahdlatul Ulama Jepara
Noor Azizah Universitas Islam Nahdlatul Ulama Jepara
Heru Saputro Universitas Islam Nahdlatul Ulama Jepara https://orcid.org/0000-0002-2626-494X

DOI: https://doi.org/10.34001/jister.v2i2.392

Abstract

In the world of education, there are many supporting factors to achieve success in the learning process, from teachers, materials, to students. The thing that still a problem for many educators is how to provide material or learning with various personality characteristics of students who arrive is still a difficulty for educators, this is what encourages the author to carry out this research, so that it can help educators to know the personality of each student. So research Data Mining Personality Classification of SMP Negeri 5 Jepara Students Using Decision Tree Algorithm C4.5 method. By using the decision method tree algorithm C4.5, this data mining can produce perfect output in classifying students' personalities, in this case study at SMP N 5 Jepara.These results are indicated by filling out questionnaires by researchers to 100 students and 1 teacher BP SMP Negeri 5 Jepara.

References

Yohanni Syahra. (2018). Penerapan Data Mining Dalam Pengelompokkan Data Nilai Siswa Untuk Penentuan Jurusan Siswa Pada SMA Tamora Menggunakan Algoritma K-Means Clustering, E-ISSN : 2615-3475.
Nadya Andhika Putri. (2018). Sistem Pakar Untuk Mengidentifikasi Kepribadian Siswa Menggunakan Metode Certainty Factor Dalam Mendukung Pendekatan Guru. Journal of Information Technology and Computer Science 1(1): 78-90.
Wildan Hakim, Turmudi, Wahyu H. Irawan. (2015). Sistem Pendukung Keputusan Metode Sugeno dalam Menentukan Tingkat Kepribadian Siswa Berdasarkan Pendidikan (Studi Kasus di MI Miftahul Ulum Gondanglegi Malang).
Didik Setiyadi, Ali Nurdin. (2012). Data Mining Potensi Akademik Siswa Berbasis Online. Didik Setiyadi, Ali Nurdin.
Imam Sutoyo. 2018. Implementasi Algoritma Decision Tree Untuk Klasifikasi Data Peserta Didik. Jurnal PILAR Nusa Mandiri Vol. 14
Kusrini dan Emha Taufiq Luthfi. 2009. Algoritma Data Mining. Penerbit , Yogyakarta.
Dewi, M.R., Mudakir, I. and Murdiyah, S. Pengaruh Model Pembelajaran Kolaboratif berbasis Lesson Study terhadap Kemampuan Berpikir Kritis Siswa. Jurnal Edukasi, 3 (2), pp.29-33, 2016.
B. Liu. Sentiment Analysis and Opinion Mining. USA: Morgan &
Mello, R.F. and Gašević, D., 2019, October. What is the effect of a dominant code in an epistemic network analysis?. In International Conference on Quantitative Ethnography (pp. 66-76). Springer, Cham.
Shaffer, D.W., Collier, W. and Ruis, A.R. A tutorial on epistemic network analysis: Analyzing the structure of connections in cognitive, social, and interaction data. Journal of Learning Analytics, 3 (3), pp.9-45, 2016.
Breiman, L.. Random forests. Machine learning, 45 (1), pp.5-32, 2001.
Arifin, O. and Sasongko, T.B. Analisa Perbandingan Tingkat Performansi Metode Support Vector Machine dan Naïve Bayes Classifier Untuk Klasifikasi Jalur Minat SMA. SEMNASTEKNOMEDIA ONLINE, 6 (1), pp.1-2, 2018.
Sari, A.P., Saptono, R. and Suryani, E. The Implementation of Jaro Winkler Distance and Naive Bayes Classifier for Identification System of Pests and Diseases on Paddy. ITSMART: Jurnal Teknologi dan Informasi, 7 (1), pp.1-7, 2018.
Lestari, A.R.T., Perdana, R.S. and Fauzi, M.A. Analisis Sentimen Tentang Opini Pilkada DKI 2017 pada Dokumen Twitter Berbahasa Indonesia Menggunakan Naive Bayes dan Pembobotan Emoji. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer e-ISSN, 2548, p. 964X. 2017.
Ling, J., Kencana, I.P.E.N. and Oka, T.B. Analisis Sentimen Menggunakan Metode Naïve Bayes Classifier dengan Seleksi Fitur Chi Square. E Jurnal Matematika, 3(3), pp.92-99, 2014.
Sari, R. and Hayuningtyas, R.Y. Penerapan Algoritma Naive Bayes Untuk Analisis Sentimen Pada Wisata TMII Berbasis Website. Indonesian Journal on Software Engineering (IJSE), 5(2), pp.51-60, 2019.
Marquart, C.L. (2019). rENA: Epistemic Network Analysis. [ONLINE] Available at: https://cran.rproject.org/web/packages/rENA/index.html.
Leydesdorff, L. and Welbers, K. The semantic mapping of words and co-words in contexts. Journal of Informetrics, 5(3), pp.469-475, 2011.
Swiecki, Z. and Shaffer, D.W. iSENS: an integrated approach to combining epistemic and social network analyses. In Proceedings of the Tenth International Conference on Learning Analytics & Knowledge (pp. 305-313), 2020.