K-Nearest Neighbors (K-NN) Algorithm Model in Predicting the Graduation Rate of Teacher Professional Education Students in Indonesia
DOI:
https://doi.org/10.47134/ijsl.v4i3.277Keywords:
Input Quality, K-Nearest Neighbor, Teacher Professional Education, Online LearningAbstract
Predicting the graduation rate of the PPG program has an important significance in analyzing the factors that affect students' success in completing the PPG program. This study uses the K-Nearest Neighbor model in online learning to predict the pass rate of students in the Teacher Professional Education Program (PPG) at UIN Walisongo Semarang. The study analyzed data from 423 students, focusing on input quality variables, such as pedagogical competence and teaching innovation. Results showed the Wave 1 pass rate in 2023 was 86.7%, with 13.3% failure, a 1.7% decrease from Wave 3 in 2022. The confusion matrix showed significant improvement in True Positives (TP) and True Negatives (TN), with an accuracy of 0.916, precision of 0.3, and recall of 0.9725 students' academic achievement.
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References
Abel, R. (2005). Implementing Best Practices in Online Learning. Educause Quarterly, 28(3), 75–77. https://www.learntechlib.org/p/103719/.
Adnan, M., & Anwar, K. (2020). Online Learning amid the COVID-19 Pandemic: Students’ Perspectives. Online Submission, 2(1), 45–51. https://doi.org/10.33902/JPSP.2020261309.
Arifa, F. N., & Prayitno, U. S. (2019). Peningkatan Kualitas Pendidikan: Program Pendidikan Profesi Guru Prajabatan dalam Pemenuhan Kebutuhan Guru Profesional di Indonesia. Aspirasi: Jurnal Masalah-Masalah Sosial, 10(1), 1–17. https://doi.org/10.22212/aspirasi.v7i1.1084.
Avalos, B. (2011). Teacher professional development in Teaching and Teacher Education over ten years. Teaching and Teacher Education, 27(1), 10–20. https://doi.org/10.1016/j.tate.2010.08.007.
Bakia, M., Shear, L., Toyama, Y., & Lasseter, A. (2012). Understanding the Implications of Online Learning for Educational Productivity. Office of Educational Technology, US Department of Education. http://www2.ed.gov/about/offices/list/os/technology/index.html.
Bansal, M., Goyal, A., & Choudhary, A. (2022). A comparative analysis of K-nearest neighbor, genetic, support vector machine, decision tree, and long short term memory algorithms in machine learning. Decision Analytics Journal, 3, 100071. https://doi.org/10.1016/j.dajour.2022.100071.
Beauchamp, C., & Thomas, L. (2009). Understanding teacher identity: An overview of issues in the literature and implications for teacher education. Cambridge Journal of Education, 39(2), 175–189. https://doi.org/10.1080/03057640902902252.
Boateng, E. Y., Otoo, J., & Abaye, D. A. (2020). Basic tenets of classification algorithms K-nearest-neighbor, support vector machine, random forest and neural network: A review. Journal of Data Analysis and Information Processing, 8(4), 341-357. DOI: 10.4236/jdaip.2020.84020.
Cunningham, P., & Delany, S. J. (2021). K-nearest neighbour classifiers-a tutorial. ACM computing surveys (CSUR), 54(6), 1-25. https://doi.org/10.1145/3459665.
Dann, E., Henderson, N. C., Teichmann, S. A., Morgan, M. D., & Marioni, J. C. (2022). Differential abundance testing on single-cell data using k-nearest neighbor graphs. Nature Biotechnology, 40(2), 245-253. DOI: 10.1038/s41587-021-01033-z.
Faisal, M. R., & Nugrahadi, D. T. (2017). Belajar Data Science: Klasifikasi dengan Bahasa Pemrograman R (Vol. 1). M Reza Faisal. From https://www.researchgate.net/publication/312160783_Belajar_Data_Science_Klasifikasi_dengan_Bahasa_Pemrograman_R.
Irwan, I. (2016). Kualitas Input Mahasiswa Baru UIN Alauddin Makassar Tahun 2014. Teknosains: Media Informasi Sains dan Teknologi, 10(1), Article 1. https://doi.org/10.24252/teknosains.v10i1.1876.
Isnain, A. R., Supriyanto, J., & Kharisma, M. P. (2021). Implementation of K-Nearest Neighbor (K-NN) algorithm for public sentiment analysis of online learning. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 15(2), 121-130. https://doi.org/10.22146/ijccs.65176.
Jiang, L., Cai, Z., Wang, D., & Jiang, S. (2007). Survey of Improving K-Nearest-Neighbor for Classification. Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007), 1, 679–683. https://doi.org/10.1109/FSKD.2007.552.
Junaedi, M., Nasikhin, N., & Hasanah, S. (2022). Issues in the Implementing of Online Learning in Islamic Higher Education During the Covid-19 Pandemic. Ta’dib, 25(1), 33–46. https://doi.org/10.31958/jt.v25i1.5365.
Kang, S. (2021). K-nearest neighbor learning with graph neural networks. Mathematics, 9(8), 830. https://doi.org/10.3390/math9080830.
Kearns, L. R. (2012). Student Assessment in Online Learning: Challenges and Effective Practices. 8(3), 198. https://jolt.merlot.org/vol8no3/kearns_0912.pdf.
Keller, J. M., Gray, M. R., & Givens, J. A. (1985). A fuzzy K-nearest neighbor algorithm. IEEE Transactions on Systems, Man, and Cybernetics, SMC-15(4), 580–585. IEEE Transactions on Systems, Man, and Cybernetics. https://doi.org/10.1109/TSMC.1985.6313426.
Larose, D. T., & Larose, C. D. (2014). k‐Nearest Neighbor Algorithm. In Discovering Knowledge in Data: An Introduction to Data Mining (pp. 149–164). Discovering Knowledge in Data: An Introduction to Data Mining. Wiley. https://doi.org/10.1002/9781118874059.ch7.
Lu, J., Qian, W., Li, S., & Cui, R. (2021). Enhanced K-nearest neighbor for intelligent fault diagnosis of rotating machinery. Applied Sciences, 11(3), 919. https://www.researchgate.net/publication/348636817_Enhanced_K-Nearest_Neighbor_for_Intelligent_Fault_Diagnosis_of_Rotating_Machinery.
Mushtaha, E., Abu Dabous, S., Alsyouf, I., Ahmed, A., & Raafat Abdraboh, N. (2022). The challenges and opportunities of online learning and teaching at engineering and theoretical colleges during the pandemic. Ain Shams Engineering Journal, 13(6), 101770. https://doi.org/10.1016/j.asej.2022.101770.
Muslim, S. B. (2010). Supervisi Pendidikan Meningkatkan Kualitas Profesionalisme Guru. Alfabeta.
Nasikhin, Nasikhin, & Shodiq. (2021). Different Perspective of Religious Education in Islamic Theology and West Theology. Al-Fatih: Jurnal Pendidikan Dan Keislaman, 4(2), 328–342. https://jurnal.stit-al-ittihadiyahlabura.ac.id/index.php/alfatih/article/view/157/132.
Nguyen, T. (2015). The Effectiveness of Online Learning: Beyond No Significant Difference and Future Horizons. MERLOT The Journal of Online Teaching and Learning, 11(2), 309–319. http://jolt.merlot.org/Vol11no2/Nguyen_0615.pdf.
Ningrum, E. (2016). Membangun Sinergi Pendidikan Akademik (S1) Dan Pendidikan Profesi Guru (PPG). Jurnal Geografi Gea, 12(2). https://doi.org/10.17509/gea.v12i2.1783.
Palloff, R. M., & Pratt, K. (2007). Building Online Learning Communities: Effective Strategies for the Virtual Classroom. John Wiley & Sons. San Francisco, CA: Jossey-Bass.
Pangestika, R. R., & Alfarisa, F. (2015). Pendidikan profesi guru (PPG): Strategi pengembangan profesionalitas guru dan peningkatan mutu pendidikan Indonesia. Prosiding Seminar Nasional, 19(1), 671–683. https://core.ac.uk/download/pdf/33518888.pdf.
Ratnasari, Y. T. (2019). Profesionalisme Guru Dalam Peningkatan Mutu Pendidikan. Revitalisasi Manajemen Pendidikan Anak Usia Dini (PAUD) Di Era Revolusi Industri 4.0, 0, Article 0. http://conference.um.ac.id/index.php/apfip2/article/view/404.
Simamora, R. M. (2020). The Challenges of Online Learning during the COVID-19 Pandemic: An Essay Analysis of Performing Arts Education Students. Studies in Learning and Teaching, 1(2), 86–103. https://doi.org/10.46627/silet.v1i2.38.
Stürmer, K., Könings, K. D., & Seidel, T. (2015). Factors Within University-Based Teacher Education Relating to Preservice Teachers’ Professional Vision. Vocations and Learning, 8(1), 35–54. https://doi.org/10.1007/s12186-014-9122-z.
Sukardi, Giatman, M., Haq, S., Sarwandi, & Pratama, Y. F. (2019). Effectivity of Online Learning Teaching Materials Model on Innovation Course of Vocational and Technology Education. Journal of Physics: Conference Series, 1387(1), 012131. https://doi.org/10.1088/1742-6596/1387/1/012131.
Sunaryo, H., Zuriah, N., & Handayani, T. (2020). Kesiapan Mahasiswa Pendidikan Profesi Guru (PPG) Dalam-Jabatan untuk Menempuh Program Praktik Pengalaman Lapangan. Jurnal Pendidikan Profesi Guru, 1(1), 29–38. https://doi.org/10.22219/jppg.v1i1.12430.
Toosi, J. F., Jamil, A. I., & Zulkifli, M. Y. (2019). Moral Autonomy and Habituation Method: A Study Based on Islamic Teachings. Kemanusiaan the Asian Journal of Humanities, 26(1), 47–61. https://doi.org/10.21315/kajh2019.26.s1.3.
Zulfitri, H., Setiawati, N. P., & Ismaini, I. (2019). Pendidikan Profesi Guru (PPG) sebagai Upaya Meningkatkan Profesionalisme Guru. LINGUA: Jurnal Bahasa Dan Sastra, 19(2), 1–130.
