Implementasi Algoritma K-Nearest Neighbor Untuk Klasifikasi Penerimaan Beasiswa Program Indonesia Pintar

Chusi Yanasari; Toni Arifin

doi:10.59581/jusiik-widyakarya.v1i4.1862

Authors

Chusi Yanasari Universitas Adhirajasa Reswara Sanjaya
Toni Arifin Universitas Adhirajasa Reswara Sanjaya

DOI:

https://doi.org/10.59581/jusiik-widyakarya.v1i4.1862

Keywords:

Implementation, Classification, K-Nearest Neighbor, Scholarship Recipients Smart Indonesia Pintar (PIP)

Abstract

Scholarships are a form of assistance in the form of educational expenses provided by the government or foundations to students or students who are categorized as from underprivileged families. However, in datermining scholarship recipients, there are still many scholarship recipients who come from wealthy families, while those from less fortunate families do not receive this assistance. This may be due to calculations and data processing that still use manual methods, causing scholarship recipients to not be on target. The purpose of this research is to simplify and minimize calculation errors in determining scholarship recipients for the Smart Indonesia Program (PIP) at SMK Karya Medika. Therefore, for calculating and processing PIP scholarship recipients data, data mining techniques can use the calssification method using the K-NN algprithm. K-Nearest Neighbor is a data classification method that will be used for data objects based on learning data that is closer to the object. In this study using the Confusion Matrix test so as to obtain an accuracy value of 80.00%.

References

Andini, A. D., Arifin, T., Sanjaya, A. R., Sanjaya, A. R., Coefficient, S., & Pasien, P. (2020). Implementasi Algoritma K-Medoids Untuk Klasterisasi Data Penyakit Pasien. 2(2), 128–138.

Arifin, T., & Ariesta, D. (2019). Prediksi Penyakit Ginjal Kronis Menggunakan Algoritma Naive Bayes Classifier Berbasis Particle Swarm Optimization. Jurnal Tekno Insentif, 13(1), 26–30. Https://Doi.Org/10.36787/Jti.V13i1.97

Arifin, T., & Herliana, A. (2020). Optimizing Decision Tree Using Particle Swarm Optimization To Identify Eye Diseases Based On Texture Analysis. Jurnal Teknologi Dan Sistem Komputer, 8(1), 59–63. Https://Doi.Org/10.14710/Jtsiskom.8.1.2020.59-63

Homepage, J., Cholil, S. R., Handayani, T., Prathivi, R., & Ardianita, T. (2021). Ijcit (Indonesian Journal On Computer And Information Technology) Implementasi Algoritma Klasifikasi K-Nearest Neighbor (Knn) Untuk Klasifikasi Seleksi Penerima Beasiswa. Ijcit (Indonesian Journal On Computer And Information Technology), 6(2), 118–127.

Iriane, R., & Nurfaizah, N. (2023). Klik: Kajian Ilmiah Informatika Dan Komputer Penerapan Data Mining Untuk Prediksi Penjualan Produk Pangan Hewan Menggunakan Metode K-Nearest Neighbor. Media Online, 3(5), 509–515. Https://Djournals.Com/Klik

Karepesina, F., & Zahrotun, L. (2023). Penerapan Data Mining Untuk Penentuan Penerima Beasiswa Dengan Metode K-Nearest Neighbor (K-Nn). Techno (Jurnal Fakultas Teknik, Universitas Muhammadiyah Purwokerto), 24(1), 1. Https://Doi.Org/10.30595/Techno.V24i1.9084

Koda, A., Rahayu, P., Pratama, A., Rafly, A., & Kaslani. (2020). Penentuan Bonus Karyawan Dengan Menggunakan Algoritma K-Nearest Neighbor. Kopertip : Jurnal Ilmiah Manajemen Informatika Dan Komputer, 4(1), 14–20. Https://Doi.Org/10.32485/Kopertip.V4i1.115

Mulyani, N. K. (2020). Penerapan Algoritma K-Nearest Naighbor Dalam Menentukan Penerima Beasiswa Peningkatan Prestasi Akademik Di Unisnu Jepara. Https://Doi.Org/Https://Eprints.Unisnu.Ac.Id/Id/Eprint/658/

Nata, A., & Suparmadi, S. (2022). Analisis Sistem Pendukung Keputusan Dengan Model Klasifikasi Berbasis Machine Learning Dalam Penentuan Penerima Program Indonesia Pintar. Journal Of Science And Social Research, 5(3), 697. Https://Doi.Org/10.54314/Jssr.V5i3.1041

Noviana, D., Susanti, Y., & Susanto, I. (2019). Analisis Rekomendasi Penerima Beasiswa Menggunakan Algoritma K-Nearest Neighbor (K-Nn) Dan Algoritma C4.5. Seminar Nasional Penelitian Pendidikan Matematika (Snp2m) 2019 Umt, 79–87.

Pauji, A., Aisyah, S., Surip, A., Saputra, R., & Ali, I. (2020). Implementasi Algoritma K-Nearest Neighbor Dalam Menentukan Penerima Bantuan Langsung Tunai. Kopertip : Jurnal Ilmiah Manajemen Informatika Dan Komputer, 4(1), 21–27. Https://Doi.Org/10.32485/Kopertip.V4i1.114

Putry, N. M., & Sari, B. N. (2022). Komparasi Algoritma Knn Dan Naïve Bayes Untuk Klasifikasi Diagnosis Penyakit Diabetes Mellitus. Evolusi : Jurnal Sains Dan Manajemen, 10(1). Https://Doi.Org/10.31294/Evolusi.V10i1.12514

Resmiati, R., & Arifin, T. (2021). Klasifikasi Pasien Kanker Payudara Menggunakan Metode Support Vector Machine Dengan Backward Elimination. Sistemasi, 10(2), 381. Https://Doi.Org/10.32520/Stmsi.V10i2.1238

Soleha, R. A., Wijaya, H. O. L., & Daulay, N. K. (2023). Klasifikasi Kemampuan Ekonomi Calon Siswa Baru Dengan Metode K-Nearest Neighbor Pada Sma Negeri 1 Musi Rawas. Jurasik (Jurnal Riset Sistem Informasi Dan Teknik Informatika), 8(1), 62–69. Http://Tunasbangsa.Ac.Id/Ejurnal/Index.Php/Jurasik/Article/View/542

Zaman, B., Rifai, A., & Hanif, M. B. (2021). Komparasi Metode Klasifikasi Batik Menggunakan Neural Network Dan K-Nearest Neighbor Berbasis Ekstraksi Fitur Tekstur. Journal Of Information Systems And Informatics, 3(4), 582–595. Https://Doi.Org/10.51519/Journalisi.V3i4.213

Zulfallah, F. H. (2022). Implementasi Algoritma Knn Dalam Mengukur Ketepatan Kelulusan Mahasiswa Uin Syarif Hidayatullah Jakarta.