Implementasi Penerapan Metode C4.5 dan Naïve Bayes Dalam Tingkat Kelulusan Akreditasi Lembaga PAUD Pada Badan Akreditasi Nasional

Authors

  • Lenggo Genisa STIKOM CKI, Jakarta
  • Dadang Iskandar Mulyana STIKOM CKI, Jakarta

DOI:

https://doi.org/10.30865/mib.v5i4.3267

Keywords:

Accreditation, Algorithm, C4.5, Naive Bayes, Implementation

Abstract

Education from an early age is one way to stimulate children's potential. This is explained in the Law of the Republic of Indonesia Number 20 of 2003 concerning the National Education System which states that Early Childhood Education (PAUD) is a coaching effort aimed at children from birth to the age of six which is carried out through the provision of educational stimuli to help growth and physical and spiritual development so that children have readiness to enter further education. The National Accreditation Board for Early Childhood Education and Non-Formal Education, hereinafter referred to as BAN PAUD and PNF, is an independent evaluation body that determines the feasibility of PAUD and PNF programs and/or units. BAN PAUD and PNF were formed based on Permendikbud Number 52 of 2015 concerning the National Accreditation Board for Early Childhood Education and Non-Formal Education which is a substitute for Permendikbud 59 of 2012. Improving the quality of the implementation of PAUD and PNF Accreditation can be done by increasing the availability of non-formal education accreditation services. Other things that can be done to improve the quality of the implementation of PAUD accreditation are by providing certainty and guarantee of obtaining non-formal education accreditation services and improving a reliable governance system in ensuring the implementation of non-formal education accreditation services. This study uses data mining techniques in predicting the accreditation status of PAUD education units. First, preprocessing is used to get a quality dataset. Second, the data is processed to get a series of predictions. In this step, two data mining algorithms are applied, namely the Naïve Bayes Algorithm and the C4.5 Algorithm with the aim of knowing the performance of the two algorithms with a greater level of accuracy will be recommended in solving the problem of predicting the accreditation of PAUD education units in BAN PAUD and PNF DKI Jakarta Province. Then the third, the results will be in the Conffusion Matrix to validate the accuracy of the prediction results. And the results of the assessment show that the C4.5 and Naïve Bayes Algorithm methods can be used to predict the accreditation status of PAUD education units with an accuracy of 99.00%

References

D. P. Mulya, “Analisa Dan Implementasi Association Rule Dengan Algoritma Fp-Growth Dalam Seleksi Pembelian Tanah Liat (Studi Kasus Di Pt. Anveve Ismi Berjaya),†J. Teknol. Dan Sist. Inf. Bisnis, vol. 1, no. 1, pp. 47–57, 2019.

E. Etriyanti, D. Syamsuar, and N. Kunang, “Implementasi Data Mining Menggunakan Algoritme Naive Bayes Classifier dan C4.5 untuk Memprediksi Kelulusan Mahasiswa,†Telematika, vol. 13, no. 1, pp. 56–67, 2020, doi: 10.35671/telematika.v13i1.881.

H. Dhika, F. Destiawati, and A. Fitriansyah, “Implementasi Algoritma C4. 5 terhadap Kepuasan Pelanggan,†pp. 80–86, 2018, doi: 10.31227/osf.io/fgc7a.

A. Amrin, “Perbandingan Metode Neural Network Model Radial Basis Function Dan Multilayer Perceptron Untuk Analisa Risiko Kredit Mobil,†Paradig. Komput. dan Inform., vol. 20, no. 1, pp. 31–38, 2018.

E. Buulolo, Data Mining Untuk Perguruan Tinggi. Deepublish, 2020.

D. Srianto and E. Mulyanto, “Perbandingan K-Nearest Neighbor Dan Naive Bayes Untuk Klasifikasi Tanah Layak Tanam Pohon Jati,†Techno. Com, vol. 15, no. 3, pp. 241–245, 2016.

Septiani, “Komparasi Metode Klasifikasi Data Mining Algoritma C4. 5 Dan Naive Bayes Untuk Prediksi Penyakit Hepatitis,†J. Pilar Nusa Mandiri, vol. 13, no. 1, pp. 76–84, 2017.

L. Setiyani and M. W. D. A. S. P. Purwani, “Analisis Prediksi Kelulusan Mahasiswa Tepat Waktu Menggunakan Metode Data Mining Naïve Bayes : Systematic Review,†Fakt. Exacta, vol. 13, no. 1, pp. 38–47, 2020, doi: 10.30998/faktorexacta.v13i1.5548.

W. Supriyanti, K. Kusrini, and A. Amborowati, “Perbandingan Kinerja Algoritma C4. 5 dan Naïve Bayes Untuk Ketepatan Pemilihan Konsentrasi Mahasiswa,†J. Inf. J. Penelit. dan Pengabdi. Masy., vol. 1, no. 3, pp. 61–67, 2016.

W. Muslehatin and Ibnu, “Penerapan Naïve Bayes Classification untuk Klasifikasi Tingkat Kemungkinan Obesitas Mahasiswa Sistem Informasi UIN Suska Riau,†in Seminar Nasional Teknologi Informasi Komunikasi dan Industri, 2017, pp. 250–256.

I. et al Brilian Rahmat C.T.I, “Implemetasi k-means clustering pada rapidminer untuk analisis daerah rawan kecelakaan,†in Prosiding Seminar Nasional Riset Kuantitatif Terapan, 2017, pp. 58–62.

A. P. Fadillah, “Penerapan Metode CRISP-DM untuk Prediksi Kelulusan Studi Mahasiswa Menempuh Mata Kuliah (Studi Kasus Universitas XYZ),†J. Tek. Inform. dan Sist. Inf., vol. 1, no. 3, pp. 260–270, 2015, doi: 10.28932/jutisi.v1i3.406.

S. A. Prakoso and E. T. Tutik, “KOMPARASI ALGORITMA C4. 5 DENGAN NAÃVE BAYES UNTUK KLASIFIKASI KELULUSAN MAHASISWA TEPAT WAKTU DI PTS ‘KZX,’†KOMPUTAKI, vol. 3, no. 1, 2017.

C. Anam and Santoso, “Perbandingan Kinerja Algoritma C4. 5 dan Naive Bayes untuk Klasifikasi Penerima Beasiswa,†ENERGY, vol. 8, no. 1, pp. 13–19, 2018.

Deteksi et al, “Eksperimen Naïve Bayes Pada Deteksi Berita Hoax Berbahasa Indonesia Naïve Bayes’s Experiment On Hoax News Detection In Indonesian Language,†vol, vol. 23, pp. 1–15, 2019.

H. Amalia and E. Evicienna, “PENENTUAN PROSES PERSALINAN IBU MELAHIRKAN MENGGUNAKAN ALGORITMA C4. 5,†in PROSIDING SEMINAR NASIONAL CENDEKIAWAN, 2017, pp. 101–107.

B. Bahar and D. W. Pratama, “Penerapan Teorema Bayes Dalam Sistem Pakar Untuk Konsultasi Siswa Bermasalah,†Jutisi J. Ilm. Tek. Inform. dan Sist. Inf., vol. 6, no. 2, pp. 1529–1540, 2018.

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Published

2021-10-26

Issue

Vol. 5 No. 4 (2021): Oktober 2021

Section

Articles

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