Performance Improvement of K-Nearest Neighbor Algorithm in KIP Scholarship Recipient Selection
DOI:
https://doi.org/10.34288/jri.v5i4.242Keywords:
k-NN, Parameter of k, KIPAbstract
Abstract
Law 12 of 2012 mandates that the government increase access to higher education for high achievers and underprivileged people. One of the efforts to realize this is by providing KIP Lectures. To ensure that beneficiaries are indeed eligible for KIP scholarships, it is necessary to classify scholarship recipients with data mining classification techniques correctly. The classification technique chosen is k-Nearest Neighbor (K-NN). K-NN is a classification method that relies heavily on the k parameter in carrying out classification. K-NN was applied to the KIP Scholarship applicant dataset at UIN Malang in 2022. The test scenario in this research is to compare the k-odd and k-even parameters to find the most optimal k value in K-NN. The highest accuracy value obtained by k-odd is 0.71 or 71% when k=9, and the highest for k-even is 0.67 or 67% when k=10. Using optimal k parameters is proven to improve k-NN performance. The K-NN algorithm with k-odd parameters, namely k=9, is the best method for classifying KIP scholarship recipients in this research. The results of this research can be considered in determining KIP scholarship recipients worthy of using K-NN.
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References
Agama, M. (2020). KMA No. 361 Tentang Pedoman KIP Kuliah. Kementerian Agama.
Akbar, A. S., & Kusumodestoni, R. H. (2020). Optimasi nilai k dan parameter lag algoritme k-nearest neighbor pada prediksi tingkat hunian hotel Optimization. Jurnal Teknologi Dan Sistem Komputer, 8(3), 246–254. https://www.jtsiskom.undip.ac.id/index.php/jtsiskom/article/view/13648
Cahyanti, D., Rahmayani, A., & Husniar, S. A. (2020). Analisis performa metode Knn pada Dataset pasien pengidap Kanker Payudara. Indonesian Journal of Data and Science, 1(2), 39–43. https://doi.org/10.33096/ijodas.v1i2.13
Endang Etriyanti. (2021). Perbandingan Tingkat Akurasi Metode Knn Dan Decision Tree Dalam Memprediksi Lama Studi Mahasiswa. Jurnal Ilmiah Binary STMIK Bina Nusantara Jaya Lubuklinggau, 3(1), 6–14. https://doi.org/10.52303/jb.v3i1.40
Fuadah, Y. N., Magdalena, R., Palondongan, S., & Kumalasari, N. (2019). Optimasi K-Nearest Neighbor Untuk Sistem Klasifikasi Kondisi Katarak. TEKTRIKA - Jurnal Penelitian Dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, Dan Elektronika, 4(1), 16–25. https://doi.org/10.25124/tektrika.v4i1.1832
Id, I. D. (2021). Machine Learning: Teori, Studi Kasus dan Implementasi Menggunakan Python. In UNRI Press. UNRI Press.
Maskuri, M. N., Harliana, H., Sukerti, K., & Bhakti, R. M. H. (2022). Penerapan Algoritma K-Nearest Neighbor (KNN) untuk Prediksi Penyakit Stroke. Jurnal Ilmiah Intech: Information Technology Journal of UMUS, 4(01), 130–140. http://jurnal.umus.ac.id/index.php/intech/article/view/751
Naufal, M. F., Subrata, -, Susanto, A. F., Kansil, C. N., & Huda, S. (2023). Penerapan Machine Learning untuk Prediksi Potensi Hilangnya Nasabah Bank. Techno.Com, 22(1), 1–11. https://doi.org/10.33633/tc.v22i1.7302
Nikmatun, I. A., & Waspada, I. (2019). Implementasi Data Mining untuk Klasifikasi Masa Studi Mahasiswa Menggunakan Algoritma K-Nearest Neighbor. Jurnal SIMETRIS, 10(2), 421–432. https://jurnal.umk.ac.id/index.php/simet/article/view/2882
Normawati, D., & Prayogi, S. A. (2021). Implementasi Naïve Bayes Classifier Dan Confusion Matrix Pada Analisis Sentimen Berbasis Teks Pada Twitter. Jurnal Sains Komputer & Informatika (J-SAKTI, 5(2), 697–711. http://ejurnal.tunasbangsa.ac.id/index.php/jsakti/article/view/369
Pradnyana, G. A. (2020). Data Mining Tanpa Skill Coding Menggunakan Orange. Universitas Pendidikan Ganesha. https://www.youtube.com/watch?v=WOyfHJvq8_I&ab_channel=UniversitasPendidikanGanesha
Prasetio, R. T. (2020). Seleksi Fitur Dan Optimasi Parameter K-Nn Berbasis Algoritma Genetika Pada Dataset Medis. Jurnal Responsif : Riset Sains Dan Informatika, 2(2), 213–221. https://doi.org/10.51977/jti.v2i2.319
Pratiwi, B. P., Handayani, A. S., & Sarjana, S. (2021). Pengukuran Kinerja Sistem Kualitas Udara Dengan Teknologi Wsn Menggunakan Confusion Matrix. Jurnal Informatika Upgris, 6(2), 66–75. https://doi.org/10.26877/jiu.v6i2.6552
Salsabila, A., Yunita, R., & Rozikin, C. (2021). Identifikasi Citra Jenis Bunga menggunakan Algoritma KNN dengan Ekstrasi Warna HSV dan Tekstur GLCM. Technomedia Journal, 6(1), 124–137. https://doi.org/10.33050/tmj.v6i1.1667
Syukri Mustafa, M., & Wayan Simpen, I. (2019). Implementasi Algoritma K-Nearest Neighbor (KNN) Untuk Memprediksi Pasien Terkena Penyakit Diabetes Pada Puskesmas Manyampa Kabupaten Bulukumba. SISITI: Seminar Ilmiah Sistem Informasi Dan Teknologi Informasi, 8(1), 1–10. https://ejurnal.dipanegara.ac.id/index.php/sisiti/article/view/1 -10
Ulya, S., Soeleman, M. A., & Budiman, F. (2021). Optimasi Parameter K Pada Algoritma K-NN Untuk Klasifikasi Prioritas Bantuan Pembangunan Desa. Techno.Com, 20(1), 83–96. https://doi.org/10.33633/tc.v20i1.4215
Wibowo, C., Sukarno, S., Nursanti, Y. B., & Dewadi, F. M. (2022). Kebutuhan Perguruan Tinggi di Wonogiri sebagai Bagian dari Pengembangan Sumber Daya Manusia. Visioner, 4(1 JUNI), 20–27. http://ojs.mputantular.ac.id/index.php/vis/article/view/660%0Ahttp://ojs.mputantular.ac.id/index.php/vis/article/download/660/501
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