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Abstract

National Education Standards serves as the basis of education development strategy based on the result of evaluation the implementation of education. The evaluation is implemented through accreditation and national exam. The objective of this study is to analyze the score of computer-based national exam based on accreditation scores per items of instrument by applying multiclass random forest classification modeling. The research used Computer-Based National Exam data in 2018 and accreditation data from the year of 2017 and 2018. This study employed random forest for multiclass classification. The results showed that, based on the evaluation model, classification accuration value in multiclass random forest was 83.49%. In addition, this model produces important variables in classifying the average value of computer-based national examination, i.e., items laboratory conditions (x71, x68, x69, x67), electrical installation (x62), infrastructure (x64), canteen (x83), laboratory (x55), special service officers (x56), certified teachers (x39), library staff (x54), head of administration (x51), literacy activities for students (x33), use of textbooks (x14), and community/partner collaboration in education management (x96). Based on the indicators of important variables, National Education Standards that have important role are facility and infrastructure standards, educator and educational staff standards, and graduate competence standards. Therefore, improving the quality of education can be done by improving school facilities, the competency of teacher and education staff, and graduate competency.


Abstak 


Standar Nasional Pendidikan (SNP) berfungsi sebagai dasar strategi pengembangan pendidikan berdasarkan hasil evaluasi pelaksanaan pendidikan. Evaluasi pelaksanaan pendidikan dilaksanakan melalui akreditasi dan ujian nasional (UN). Tujuan penelitian ini untuk menganalisis nilai ujian nasional berbasis komputer (UNBK) berdasarkan skor akreditasi per butir instrumen dengan menerapkan pemodelan klasifikasi random forest multikelas. Penelitian ini menggunakan data UNBK tahun 2018 dan data hasil akreditasi tahun 2017 dan 2018. Metode penelitian yang digunakan adalah pemodelan klasifikasi random forest multikelas. Hasil penelitian menunjukkan bahwa, pertama, berdasarkan evaluasi model, nilai akurasi klasifikasi dalam pemodelan klasifikasi random forest multikelas sebesar 83.49%. Kedua, model ini menghasilkan tingkat kepentingan variabel prediktor (butir-butir instrumen akreditasi) dalam mengklasifikasikan nilai rataan UNBK yakni kondisi laboratorium (x71, x68, x69, x67), instalansi listrik (x62), prasarana (x64), kantin (x83), kondisi laboran (x55), petugas layanan khusus (x56), guru tersertifikat (x39), tenaga perpustakaan (x54), kepala administrasi (x51), kegiatan literasi S/M bagi peserta didik (x33), penggunaan buku teks (x14), dan kerja sama masyarakat/mitra dalam pengelolaan pendidikan (x96). Berdasarkan indikator variabel penting tersebut, SNP yang memiliki peran penting adalah Standar Sarana dan Prasarana, Standar Pendidik dan Tenaga Kependidikan, dan Standar Kompetensi Lulusan. Oleh karena itu, peningkatan mutu pendidikan dapat dilakukan dengan meningkatkan sarana dan prasarana, kompetensi pendidik dan tenaga kependidikan, serta kompetensi lulusan. 

Article Details

How to Cite
Ramadhan, A., Susetyo, B., & Indahwati. (2019). PENERAPAN METODE KLASIFIKASI RANDOM FOREST DALAM MENGIDENTIFIKASI FAKTOR PENTING PENILAIAN MUTU PENDIDIKAN. Jurnal Pendidikan Dan Kebudayaan, 4(2), 169-182. https://doi.org/10.24832/jpnk.v4i2.1327

References

  1. Adnan, M.N. & Islam, M.Z. (2015). One-vs-all binarization technique in the context of random forest. Journal of Computational Intelligence Machine Learning, 5(1), 22–24.
  2. Badan Standar Nasional Pendidikan. (2018). Kebijakan pelaksanaan ujian nasional tahun 2018. Jakarta: Badan Penelitian dan Pengembangan, Kementerian Pendidikan dan Kebudayaan.
  3. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
  4. Bunkhumpornpat, C., Sinapiromsaran, K., & Lursinsap, C. (2012). DBSMOTE: density-based synthetic minority over-sampling technique. International Journal of Research on Intelligent Systems for Real Life Complex Problems (Applied Intelligence), 36(3), 664– 684. https://doi.org/10.1007/s10489-011-0287-y.
  5. Ferezagia, D. V., Susetyo, B., Indriyanto, B., & Pajajaran, J. (2015). Generalized Structured Component Analysis (GSCA) for National Education Standards (NES) of secondary school in Indonesia. Global Journal of Pure and Applied Mathematics, 11(4), 2441–2449.
  6. Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. Prosiding of the 14th International Joint Conference on Artrificial Intelligence 2, 1137–1143.
  7. Handayani, M. (2016). Pencapaian standar nasional pendidikan berdasarkan hasil akreditasi SMA di Provinsi DKI Jakarta. Jurnal Pendidikan dan Kebudayaan, 1(2), 179-201.
  8. Hendarman. (2013). Pemanfaatan hasil akreditasi dan kredibilitas asesor sekolah/madrasah. Jurnal Pendidikan dan Kebudayaan, 19(4), 532-542.
  9. Hijrah, M., Susetyo, B., & Sartono, B. (2018). Structural equation modeling of national standard education of vocational high school using partial least square path modeling. International Journal of Scientific Research in Science, Engineering and Technology, 4(4), 1418–1422.
  10. Hüllermeier, E. & Vanderlooy, S. (2010). Combining predictions in pairwise classification/ : an optimal adaptive voting strategy and its relation to weighted voting. Pattern Recognit, 43(1), 128–142.
  11. Pusat Penilaian Pendidikan. (2018). Hasil Ujian Nasional Tahun 2018. Jakarta: Badan Penelitian dan Pengembangan, Kementerian Pendidikan dan Kebudayaan.
  12. Raharjo, S. B. (2014). Kontribusi delapan standar nasional pendidikan terhadap pencapaian prestasi belajar. Jurnal Pendidikan dan Kebudayaan, 20(4), 470-482.
  13. Raharjo, S. B. (2018). Capaian standar nasional pendidikan sebagai prediktor mutu sekolah. Jurnal Pendidikan dan Kebudayaan, 3(2), 129-140.
  14. Republik Indonesia. (2005). Peraturan Pemerintah Nomor 19 tahun 2005 tentang Standar Nasional Pendidikan. Jakarta: Kementerian Pendidikan dan Kebudayaan.
  15. Kementerian Pendidikan dan Kebudayaan. (2017). Peraturan Menteri Pendidikan dan Kebudyaan Nomor 3 Tahun 2017 tentang Penilaian Hasil Belajar oleh Pemerintah dan Penilaian Hasil
  16. Belajar oleh Satuan Pendidikan.
  17. Sartono, B.& Syafitri, U. D. (2010). Metode pohon gabungan: Solusi pilihan untuk mengatasi kelemahan pohon regresi dan klasifikasi tunggal. Forum Statistika dan Komputasi, 15(1), 1–7.
  18. Sandri, M. & Zuccolotto, P. (2006). Variable selection using random forest. Dalam S. Zani, A. Cerioli, M. Riani, & M. Vichi (Eds),The Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society: Data Analysis, Classification and the Forward Search. New York (US): Springer Berlin Heidelberg.
  19. Setiawan, I.A., Susetyo, B., & Fitrianto, A. (2018). Application of generalized structural component analysis to identify relation between accreditation and national assessment. International Journal of Scientific Research in Science, Engineering and Technology, 4(10), 93–97.
  20. Suresha, M., Danti, A., & Narasimhamurthy, S.K. (2014). Decision trees to multiclass prediction for analysis of arecanut data. Journal of Computer Systems Science and Engineering, 29(1), 105-114.
  21. Toomey, D. (2014). R for data science. Brimingham: Packt Publishing Ltd.
  22. Zhou, L., Wang, Q. & Fujita, H. (2016). Predicting the listing status of chinese listed companies with multi-class classification models. International Journal of Informatics and Computer Science Intelligent Systems Applications, 328, 222–236.
  23. Zhou, L., Wang, Q., & Fujita, H. (2017). One versus one multi-class classification fusion using optimizing decision directed acyclic graph for predicting listing status of companies. International Journal on Multi-Sensor, Multi-Source Information Fusion, 36(1), 80–89.