Utilization of the C4.5 Algorithm for Classifying Students Who Are Eligible to Apply for Obtaining PIP

Penulis

  • Rizky Nazwa Fazira STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • Anjar Wanto STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • Indra Gunawan STIKOM Tunas Bangsa, Pematangsiantar, Indonesia

DOI:

https://doi.org/10.55123/jomlai.v2i2.3187

Kata Kunci:

Algorithm C4.5, Classification, Student, Educational Scholarships, PIP

Abstrak

This research aims to optimize the selection process for eligible students for the Smart Indonesia Program (PIP) using the C4.5 algorithm. PIP is a social assistance program aimed at students who meet specific criteria. The C4.5 algorithm was chosen because it can produce a decision model that can be used to classify students based on various assessment factors. This study involved collecting student data based on predetermined PIP criteria. This data is then used as input to train the model using the C4.5 algorithm. The training involves identifying patterns and relationships between significant variables in determining a student's eligibility for PIP. This research resulted in a decision regarding PIP (Smart Indonesia Program) recipients with a low parental income classification who were entitled to assistance. It is hoped that the results of this research can contribute to increasing efficiency and accuracy in the selection process for PIP recipient students. Using the C4.5 algorithm is expected to produce more objective decisions and identify students who qualify for assistance more precisely. Apart from that, this research can also provide new insights regarding implementing algorithms in the context of education policy and social assistance.

Referensi

N. Arminarahmah, A. D. GS, G. W. Bhawika, M. P. Dewi, and A. Wanto, ‘Mapping the Spread of Covid-19 in Asia Using Data Mining X-Means Algorithms’, IOP Conference Series: Materials Science and Engineering, vol. 1071, no. 1, p. 012018, 2021, doi: 10.1088/1757-899x/1071/1/012018.

T. H. Sinaga, A. Wanto, I. Gunawan, S. Sumarno, and Z. M. Nasution, ‘Implementation of Data Mining Using C4.5 Algorithm on Customer Satisfaction in Tirta Lihou PDAM’, Journal of Computer Networks, Architecture, and High-Performance Computing, vol. 3, no. 1, pp. 9–20, 2021, doi: 10.47709/cnahpc.v3i1.923.

A. Pradipta, D. Hartama, A. Wanto, S. Saifullah, and J. Jalaluddin, ‘The Application of Data Mining in Determining Timely Graduation Using the C45 Algorithm’, IJISTECH (International Journal of Information System & Technology), vol. 3, no. 1, pp. 31–36, 2019, doi: 10.30645/ijistech.v3i1.30.

N. A. Febriyati, A. D. Gs, and A. Wanto, ‘GRDP Growth Rate Clustering in Surabaya City uses the K- Means Algorithm’, International Journal of Information System & Technology, vol. 3, no. 2, pp. 276–283, 2020, doi: 10.30645/ijistech.v3i2.60.

I. Parlina et al., ‘Naive Bayes Algorithm Analysis to Determine the Percentage Level of visitors the Most Dominant Zoo Visit by Age Category’, in Journal of Physics: Conference Series, Aug. 2019, p. 012031. doi: 10.1088/1742-6596/1255/1/012031.

S. R. Ningsih, R. Wulansari, D. Hartama, A. P. Windarto, and A. Wanto, ‘Analysis of PROMETHEE II Method on Selection of Lecturer Community Service Grant Proposals’, in Journal of Physics: Conference Series, Aug. 2019, p. 012004. doi: 10.1088/1742-6596/1255/1/012004.

P. Alkhairi, L. P. Purba, A. Eryzha, A. P. Windarto, and A. Wanto, ‘The Analysis of the ELECTREE II Algorithm in Determining the Doubts of the Community Doing Business Online’, in Journal of Physics: Conference Series, Institute of Physics Publishing, Sep. 2019, p. 012010. doi: 10.1088/1742-6596/1255/1/012010.

R. Watrianthos, W. A. Ritonga, A. Rengganis, A. Wanto, and M. Isa Indrawan, ‘Implementation of PROMETHEE-GAIA Method for Lecturer Performance Evaluation’, Journal of Physics: Conference Series, vol. 1933, no. 1, p. 012067, 2021, doi: 10.1088/1742-6596/1933/1/012067.

S. Sundari, A. Wanto, Saifullah, and I. Gunawan, ‘Sistem Pendukung Keputusan Dengan Menggunakan Metode Electre Dalam Merekomendasikan Dosen Berprestasi Bidang Ilmu Komputer (Study Kasus di AMIK & STIKOM Tunas Bangsa)’, in Seminar Nasional Multi Disiplin Ilmu, 2017, pp. 1–6. doi: 10.17605/OSF.IO/4TWG6.

M. Widyasuti, A. Wanto, D. Hartama, and E. Purwanto, ‘Rekomendasi Penjualan Aksesoris Handphone Menggunakan Metode Analitycal Hierarchy Process (AHP)’, Konferensi Nasional Teknologi Informasi dan Komputer (KOMIK), vol. I, no. 1, pp. 27–32, 2017.

K. Fatmawati et al., ‘Analysis of Promothee II Method in the Selection of the Best Formula for Infants Under Three Years’, Journal of Physics: Conference Series, vol. 1255, no. 1, p. 012009, Aug. 2019, doi: 10.1088/1742-6596/1255/1/012009.

I. A. R. Simbolon, F. Yatussa’ada, and A. Wanto, ‘Penerapan Algoritma Backpropagation dalam Memprediksi Persentase Penduduk Buta Huruf di Indonesia’, Jurnal Informatika Upgris, vol. 4, no. 2, pp. 163–169, 2018, doi: 10.26877/jiu.v4i2.2423.

W. Saputra, J. T. Hardinata, and A. Wanto, ‘Resilient method in determining the best architectural model for predicting open unemployment in Indonesia’, IOP Conference Series: Materials Science and Engineering, vol. 725, no. 1, p. 012115, Jan. 2020, doi: 10.1088/1757-899X/725/1/012115.

E. Hartato, D. Sitorus, and A. Wanto, ‘Analisis Jaringan Saraf Tiruan Untuk Prediksi Luas Panen Biofarmaka Di Indonesia’, SemanTIK, vol. 4, no. 1, pp. 49–56, 2018, doi: 10.55679/semantik.v4i1.4201.

B. K. Sihotang and A. Wanto, ‘Analisis Jaringan Syaraf Tiruan Dalam Memprediksi Jumlah Tamu Pada Hotel Non Bintang’, Jurnal Teknologi Informasi Techno, vol. 17, no. 4, pp. 333–346, 2018, doi: 10.33633/tc.v17i4.1762.

I. S. Purba and A. Wanto, ‘Prediksi Jumlah Nilai Impor Sumatera Utara Menurut Negara Asal Menggunakan Algoritma Backpropagation’, Jurnal Teknologi Informasi Techno, vol. 17, no. 3, pp. 302–311, 2018, doi: 10.33633/tc.v17i3.1769.

A. Wanto, S. Defit, and A. P. Windarto, ‘Algoritma Fungsi Perlatihan pada Machine Learning berbasis ANN untuk Peramalan Fenomena Bencana’, RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 5, no. 2, pp. 254–264, 2021, doi: 10.29207/resti.v5i2.3031.

A. Wanto and J. T. Hardinata, ‘Estimations of Indonesian poor people as poverty reduction efforts facing industrial revolution 4.0’, IOP Conference Series: Materials Science and Engineering, vol. 725, no. 1, p. 012114, Jan. 2020, doi: 10.1088/1757-899X/725/1/012114.

Yadi Yadi, ‘Implementation Algorithm C4.5 Classification Of Prospective Scholarship Recipients’, Jurnal SimanteC, vol. 11, no. 1, pp. 27–32, 2022, doi: 10.21107/simantec.v11i1.14289.

N. I. Nurhidayati, Y. Yahya, F. Fathurrahman, L. . Samsu, and W. Amnia, ‘Implementasi Algoritma Naive Bayes Untuk Klasifikasi Penerima Beasiswa (Studi Kasus Universitas Hamzanwadi)’, Infotek: Jurnal Informatika dan Teknologi, vol. 6, no. 1, pp. 177–188, 2023, doi: 10.29408/jit.v6i1.7529.

N. Aprilyani, I. Zulfa, and H. Syahputra, ‘Penerapan Algoritma Decision Tree C4.5 Untuk Model Penentuan Penerima Beasiswa Program Indonesia Pintar (PIP) Studi Kasus Sma Negeri 3 Timang Gajah’, Jurnal Teknik Elektro dan Informatika, vol. 5, no. 1, pp. 96–109, 2022, doi: 10.55542/jurtie.v5i1.452.

S. P. Sipayung, T. P. Sihaloho, A. Purba, and J. R. Tarigan, ‘Analisa Algoritma C.45 Terhadap penentuan Rekomendasi Penerima Beasiswa SMP Swasta Methodist-8 Medan’, LOFIAN: Jurnal Teknologi Informasi dan Komunikasi, vol. 3, no. 1, pp. 20–24, 2023, doi: 10.58918/lofian.v3i1.217.

Unduhan

Diterbitkan

2023-06-30

Cara Mengutip

Fazira, R. N., Wanto, A. ., & Gunawan, I. (2023). Utilization of the C4.5 Algorithm for Classifying Students Who Are Eligible to Apply for Obtaining PIP. JOMLAI: Journal of Machine Learning and Artificial Intelligence, 2(2), 151–160. https://doi.org/10.55123/jomlai.v2i2.3187

Terbitan

Vol 2 No 2 (2023): June

Bagian

Articles

Lisensi

Hak Cipta (c) 2023 Rizky Nazwa Fazira, Anjar Wanto, Indra Gunawan

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