The Application of C4.5 Algorithm for Selecting Scholarship Recipients

Authors

  • Fristi Riandari STMIK Pelita Nusantara
  • Sarjon Defit Universitas Putra Indonesia YPTK Padang

DOI:

https://doi.org/10.21512/comtech.v13i1.7307

Keywords:

C4.5 algorithm, scholarship program, data mining, decision tree, data classification

Abstract

The scholarship program is one of the promotional techniques used by many universities, and the right scholarship award will certainly be an attraction for many people. STMIK Pelita Nusantara is one of the universities that organizes a scholarship program. In the current difficult economic conditions, the scholarship program is the target of many prospective students who want to continue their education in higher education. However, the absence of tools to process large amounts of data make determining scholarship recipients less effective and time-consuming. This situation is seen by the fact that some students are still unable to maintain the scholarships they receive. In the research, a classification model was proposed using the C4.5 algorithm approach by utilizing past data to facilitate the decision making of the scholarship program. This classification process produced a decision tree that could be used as a decision-making tool. Scholarships were awarded based on several criteria: academic potential, vocational potential, parents’ income, number of dependents, and employment status. Based on the data processing results of students who apply for scholarships in 2020 with predetermined criteria, the highest root is obtained. It consists of node 1 for academic potential, node 1.1 for vocational potential, and node 1.2 for parental income. The resulting decision tree model is expected to help to make decisions quickly and on target.

Dimensions

Plum Analytics

Author Biographies

Fristi Riandari, STMIK Pelita Nusantara

Computer Engineering

Sarjon Defit, Universitas Putra Indonesia YPTK Padang

Faculty of Computer Science

References

Afrianto, E., Suseno, J. E., & Warsito, B. (2020). Decision tree method with C4.5 algorithm for students classification who is entitled to receive Indonesian Smart Card (KIP). In IOP Conference Series: Materials Science and Engineering. IOP Publishing. https://doi.org/10.1088/1757-899X/879/1/012072

Ariawan, P. A. (2019). Optimasi pengelompokan data pada metode K-means dengan analisis outlier. Jurnal Nasional Teknologi & Sistem Informasi, 5(2), 88–95. https://doi.org/10.25077/teknosi.v5i2.2019.88-95

Azmi, Z., & Dahria, M. (2013). Decision tree berbasis algoritma untuk pengambilan keputusan. Jurnal SAINTIKOM, 12(3), 157–164.

Bedregal-Alpaca, N., Cornejo-Aparicio, V., Zárate-Valderrama, J., & Yanque-Churo, P. (2020). Classification models for determining types of academic risk and predicting dropout in university students. International Journal of Advanced Computer Science and Applications(IJACSA), 11(1), 266–272. https://doi.org/10.14569/ijacsa.2020.0110133

Condrobimo, A. R., Sano, A. V. D., & Nindito, H. (2016). The application of K-means algorithm for LQ45 index on Indonesia Stock Exchange. ComTech: Computer, Mathematics and Engineering Applications, 7(2), 151–159. https://doi.org/10.21512/comtech.v7i2.2256

Dardzinska, A., & Zdrodowska, M. (2020). Classification algorithms in the material science and engineering data mining techniques. In IOP Conference Series: Materials Science and Engineering. IOP Publishing. https://doi.org/10.1088/1757-899X/770/1/012096

Dhika, H., & Destiawati, F. (2015). Application of data mining algorithm to recipient of motorcycle installment. ComTech: Computer, Mathematics and Engineering Applications, 6(4), 569–579. https://doi.org/10.21512/comtech.v6i4.2192

Effendy, F., & Purbandini. (2018). Klasifikasi rumah tangga miskin menggunakan ordinal class classifier. Jurnal Nasional Teknologi & Sistem Informasi, 4(1), 30–36. https://doi.org/10.25077/teknosi.v4i1.2018.30-36

Fiandra, Y. A., Defit, S., & Yuhandri. (2017). Penerapan algoritma C4.5 untuk klasifikasi data rekam medis berdasarkan International Classification Diseases (ICD-10). Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 1(2), 82–89. https://doi.org/10.29207/resti.v1i2.48

Florence, A. M., & Savithri, R. (2013). Talent knowledge acquisition using C4. 5 classification algorithm. International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS), 4(4), 406–410.

Guntur, M., Santony, J., & Yuhandri. (2018). Prediksi harga emas dengan menggunakan metode Naïve Bayes dalam investasi untuk meminimalisasi resiko. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 2(1), 354–360. https://doi.org/10.29207/resti.v2i1.276

Haryati, S., Sudarsono, A., & Suryana, E. (2015). Implementasi data mining untuk memprediksi masa studi mahasiswa menggunakan algoritma C4.5 (Studi kasus: Universitas Dehasen Bengkulu). Jurnal Media Infotama, 11(2), 130–138.

Hidayad, A., Defit, S., & Sumijan, S. (2020). Penerapan algoritma K-means clustering untuk melihat hubungan kegiatan Tahfiz dengan hasil belajar (Studi kasus Madrasah Aliyah Negeri 1 Bukiktinggi). Jurnal Sistim Informasi dan Teknologi, 2(2), 41–47. https://doi.org/10.37034/jsisfotek.v2i2.34

Putra, R. A., & Defit, S. (2019). Data mining menggunakan rough set dalam menganalisa modal upah produksi pada industri seragam sekolah. Jurnal Sistim Informasi dan Teknologi, 1(4), 72–78. https://doi.org/10.35134/jsisfotek.v1i4.18

Rahmayuni, I. (2014). Perbandingan performansi algoritma C4.5 dan Cart dalam klasifiksi data nilai mahasiswa Prodi Teknik Komputer Politeknik Negeri Padang. Teknoif, 2(1), 40–46.

Riandari, F., & Simangunsong, A. (2019). Penerapan algoritma C4.5 untuk mengukur tingkat kepuasan mahasiswa. CV. Rudang Mayang.

Santoso, H., Hariyadi, I. P., & Prayitno. (2016). Data mining analisa pola pembelian produk dengan menggunakan metode algoritma Apriori. Semnasteknomedia Online, 4(1), 19–24.

Sulastri, H., & Gufroni, A. I. (2017). Penerapan data mining dalam pengelompokan penderita thalassaemia. Jurnal Nasional Teknologi & Sistem Informasi, 3(2), 299–305. https://doi.org/10.25077/teknosi.v3i2.2017.299-305

Virgo, I., Defit, S., & Yunus, Y. (2020). Klasterisasi tingkat kehadiran dosen menggunakan algoritma K-means clustering (Studi kasus Institut Agama Islam Batusangkar). Jurnal Sistim Informasi dan Teknologi, 2(1), 23–28. https://doi.org/10.37034/jsisfotek.v2i1.22

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Published

2022-02-03

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