Klasifikasi Keterlambatan Pembayaran Sumbangan Pembinaan Pendidikan Menggunakan Algoritma Naïve Bayes dan Support Vector Machine
- Huzaifah Umar UIN Maulana Malik Ibrahim, Malang, Indonesia
-
R. Kusumawati
*
UIN Maulana Malik Ibrahim, Malang,
Indonesia
- M. Imamudin UIN Maulana Malik Ibrahim, Malang, Indonesia
- Moh. Ainur Rohman Universitas Jember, Kota Jember, Indonesia (*) Corresponding Author
Abstract
Payment delinquency of SPP is a commonly occurring issue in school. It affects the salary of teachers and staffs alongside school’s various development program. The study aims to classify payment delinquencies using Naïve Bayes and Support Vector Machine. Research methode is Cross-Industry Standard Process for Data Mining (CRISP-DM). Method testing was carried out with 5 trials. Based on the test results, the average performance of Naïve Bayes is accuracy (62,88%), precision (65,27%), recall (77,42%) dan f1-score (70,75%). Meanwhile, the average performance of the Support Vector Machine is accuracy 63,51%), precision (62,25%), recall (94,48%) dan f1-score (75,04%).
Downloads
References
Abdullah et al. (2019). Prediksi Keterlambatan Pembayaran SPP Sekolah Dengan Metode K-Nearest Neighbor (Studi Kasus SMK Al Islam Surakarta). Informasi Interaktif, Vol 4, No 3, 160–164.
Anam, C., & Santoso, H. B. (2018). Perbandingan Kinerja Algoritma C4.5 dan Naive Bayes untuk Klasifikasi Penerima Beasiswa. Jurnal Energy Universitas Panca Marga, 8(1).
Apandi, T. H., Maulana, R. B., Piarna, R., & Vernanda, D. (2019). Menganalisis Kemungkinan Keterlambatan Pembayaran Spp Dengan Algoritma C4.5 (Studi Kasus Politeknik Tedc Bandung). Jurnal Techno Nusa Mandiri, 16(2), 93–98. https://doi.org/10.33480/techno.v16i2.659
Bahtiar, A., & Firmansyah, D. (2017). Klasifikasi Ketepatan Waktu Pembayaraan SPP di Pondok Pesantren Al-Arifah Menggunakan Algoritma Naive Bayes. Jurnal Ilmiah Manajemen Informatika dan Komputer, 01(01).
Bustami. (2014). Penerapan Algoritma Naive Bayes Untuk Mengklasifikasi Data Nasabah Asuransi. Jurnal Informatika, 8(1).
Ginting, V. S., Kusrini, K., & Taufiq, E. (2020). Implementasi Algoritma C4.5 untuk Memprediksi Keterlambatan Pembayaran Sumbangan Pembangunan Pendidikan Sekolah Menggunakan Python. Inspiration: Jurnal Teknologi Informasi dan Komunikasi, 10(1). https://doi.org/10.35585/inspir.v10i1.2535
Indrayuni, E. (2018). Komparasi Algoritma Naive Bayes dan Support Vector Machine Untuk Analisa Sentimen Review Film. Jurnal Pilar Nusa Mandiri, 14(2), 175. https://doi.org/10.33480/pilar.v14i2.918
Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093–1113. https://doi.org/10.1016/j.asej.2014.04.011
Muqorobin, M., Kusrini, K., & Luthfi, E. T. (2019). Optimasi Metode Naive Bayes Dengan Feature Selection Information Gain Untuk Prediksi Keterlambatan Pembayaran SPP Sekolah. Jurnal Ilmiah SINUS, 17(1), 1. https://doi.org/10.30646/sinus.v17i1.378
Noble, W. S. (2006). What is a support vector machine? Nature Biotechnology, 24(12), 1565–1567. https://doi.org/10.1038/nbt1206-1565
Norhalimi, M., & Siswa, T. A. Y. (2022). Optimasi Seleksi Fitur Information Gain pada Algoritma Naïve Bayes dan K-Nearest Neighbor. JISKA (Jurnal Informatika Sunan Kalijaga), 7(3), 237–255. https://doi.org/10.14421/jiska.2022.7.3.237-255
Nugraha, Y. R. A. (2021). Implementasi Algoritma Support Vector Machine Untuk Klasifikasi Penyesuaian Uang Kuliah Tunggal Terdampak Pandemi Covid-19. Jurnal Teknik Informatika Kaputama (JTIK), Vol. 5(No. 2).
Paramitha, N. Y., Nuryaman, A., Faisol, A., & Setiawan, E. (2023). Klasifikasi Penyakit Stroke Menggunakan Metode Naïve Bayes. 04(01).
Prabowo, C., Hakim, L. N., Rohmat, C. L., & Rinaldi, A. R. (2021). Teknik Klasifikasi Pembayaran SPP Berdasarkan Tingkat Ketepatan Pembayaran. Jurnal Data Science dan Informatika (JDSI), 1(1).
Rosiana, L. (2022). Analisis Kemungkinan Keterlambatan Pembayaran SPP Menggunakan Algoritma Support Vector Machine (Studi Kasus: Smp Perintis 2 Bandar Lampung). Jurnal Ilmu Data, Vol 2(No. 9).
Shafique, U., & Qaiser, H. (2014). A Comparative Study of Data Mining Process Models (KDD, CRISP-DM and SEMMA). International Journal of Innovation and Scientific Research, 12(1), 6.
Sukesa, I. K. (2022). CRISP DM Sebagai Salah Satu Standard untuk Menghasilkan Data Driven Decision Making yang Berkualitas. Diakses pada 11 Januari 2024 dari https://www.djkn.kemenkeu.go.id/artikel/baca/15134/CRISP-DM-Sebagai-Salah-Satu-Standard-untuk-Menghasilkan-Data-Driven-Decision-Making-yang-Berkualitas.html
Widayani, W., & Harliana, H. (2021). Analisis Support Vector Machine Untuk Pemberian Rekomendasi Penundaan Biaya Kuliah Mahasiswa. Jurnal Sains dan Informatika, 7(1), 20–27. https://doi.org/10.34128/jsi.v7i1.268
Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G. J., Ng, A., Liu, B., Yu, P. S., Zhou, Z.-H., Steinbach, M., Hand, D. J., & Steinberg, D. (2008). Top 10 algorithms in data mining. Knowledge and Information Systems, 14(1), 1–37. https://doi.org/10.1007/s10115-007-0114-2
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Klasifikasi Keterlambatan Pembayaran Sumbangan Pembinaan Pendidikan Menggunakan Algoritma Naïve Bayes dan Support Vector Machine
Pages: 709-718
Copyright (c) 2024 Huzaifah Umar, R. Kusumawati, M. Imamudin, Moh. Ainur Rohman
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
