Implementasi Metode Decision Tree Klasifikasi Data Mining Untuk Prediksi Peminatan Jurusan Robotika oleh Mahasiswa

Mugi Raharjo, Ridwan ridwan, Jordy Lasmana Putra, Tommi Alfian Armawan Sandi

Abstract


Specialization of majors in a study program becomes something important must be an option for a student, for that they must think carefully before choosing the majors. Because later this thing can determine the success or failure of a student to understand what they learned to apply to during the final project. In the past few years there has been a question about the problem of electing majors in the Computer Technology Study Program. Because almost every year the majority of interest voters in majors are interested in computer network majors rather than robotics majors. majoring in majors, so the authors analyzed and retrieved data from 145 student samples in the electronic practicum course and chose 7 attributes in this study because this course was very influential on the interest in the robotics department in the Computer Technology study program. The author uses the classification tree Decision method to predict interest in students. Therefore, with this research, the authors hope that in the future with the results of this analysis can be found a solution to the problem of why students are more inclined to choose the interests of departments other than robotics, whether due to factors or other factors.

 

Keywords: Computer Technology, Analysis, Classification


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References


,2,3 1. (2017), 10, 2013–2016.

Ariestya, W. W., & Gunadarma, U. (2017). DECISION TREE LEARNING UNTUK PENENTUAN JALUR KELULUSAN Decision Tree Learning Untuk Penentuan Jalur Kelulusan Mahasiswa, (May 2016).

https://doi.org/10.22441/fifo.v8i1.1304

Fakultas, M., Dan, K., & Surakarta, U. M. (2014). Prosiding Seminar Nasional Aplikasi Sains & Teknologi (SNAST) 2014 Yogyakarta, 15 November 2014 ISSN: 1979-911X, (November), 1–6.

Goyal, V. K. (n.d.). A Comparative Study of Classification Methods in Data Mining using RapidMiner Studio.

Han, J. (2015). Data Mining : Concepts and Techniques.

Kamagi, D. H., & Hansun, S. (2014). Implementasi Data Mining dengan Algoritma C4 . 5 untuk Memprediksi Tingkat Kelulusan Mahasiswa, VI(1), 15–20.

Penelitian, J., Masyarakat, P., Mipa, F., Pgri, U. I., Kel, R. T., Timur, J., & Mining, D. (2014). Harry Dhika Tarigan, 2 Fitriana Destiawati, 3 Aswin Fitriansyah, 80–86.

Prosiding Seminar Nasional Aplikasi Sains & Teknologi (SNAST) 2014 Yogyakarta, 15 November 2014 ISSN: 1979-911X. (2014), (November).

Zaki, M. J. (n.d.). DATA MINING Fundamental Concepts and Algorithms.




DOI: https://doi.org/10.31294/jtk.v5i2.4852

Copyright (c) 2019 Mugi Raharjo, Ridwan ridwan, Jordy Lasmana Putra, Tommi Alfian Armawan Sandi

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ISSN: 2442-2436 (print), and 2550-0120


 dipublikasikan oleh LPPM Universitas Bina Sarana Informatika Jakarta

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