PREDICTION OF PIP RECIPIENTS USING K-NEAREST NEIGHBOR AT MI NURUL QOLBI
DOI:
https://doi.org/10.34288/jri.v7i2.321Keywords:
Education, Prediction, PIP, K-nearest neighbor(KNN), k-Fold Cross ValidationAbstract
Education is a key foundation in the development of quality human resources. However, the rising cost of education makes some children unable to attend school due to their parents' financial limitations. To address this problem, the government launched the Indonesia Smart Program (PIP) which provides education funding assistance to eligible students. This research aims to develop an Information System that can predict the eligibility of students to receive PIP assistance using the K-Nearest Neighbors (KNN) algorithm. The data used comes from all students of Madrasah Ibtidaiyah (MI) Nurul Qolbi in the 2022-2023 school year. This research methodology involves testing with a value of k=13 and model evaluation is done using split ratio and cross-validation techniques. The results showed an accuracy of 98.98% from various split ratios (10:90, 20:80, 30:70, 40:60) and an accuracy of 99.24% using the 10-fold cross-validation technique. The accuracy results show excellent performance and provide important significance in the development of prediction systems to help the selection process of aid recipients more efficiently and reduce the administrative burden for schools. However, its application on a wider scale still requires further research, especially to test its consistency and effectiveness in different contexts and with more diverse datasets.
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Anwar Pauji, Aisyah, S., Surip, A., Saputra, R., & Ali, I. (2022). 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
Baharuddin, M. M., Azis, H., & Hasanuddin, T. (2019). Analisis Performa Metode K-Nearest Neighbor Untuk Identifikasi Jenis Kaca. ILKOM Jurnal Ilmiah, 11(3), 269–274. https://doi.org/10.33096/ilkom.v11i3.489.269-274
Cholil, S. R., Handayani, T., Prathivi, R., & Ardianita, T. (2021). Implementasi Algoritma Klasifikasi K-Nearest Neighbor (KNN) Untuk Klasifikasi Seleksi Penerima Beasiswa. IJCIT (Indonesian Journal on Computer and Information Technology), 6(2), 118–127.
Penulis. (2023). PIP, Peningkatan Mutu Pembelajaran dan Pengentasan Kemiskinan Siswa Madrasah. Retrieved April 12, 2024, from Kementerian Agama Republik Indonesia website: https://kemenag.go.id/kolom/pip-peningkatan-mutu-pembelajaran-dan-pengentasan-kemiskinan-siswa-madrasah-X25JQ
Ibnu Daqiqil. (2021). Machine Learning: Teori, Studi Kasus, dan Implementasi Menggunakan Python. UR PRESS. https://doi.org/10.5281/zenodo.5113507
Khoirunnisa, Susanti, L., Rokhmah, I. T., & Stianingsih, L. (2021). Prediksi Siswa SMK Al-Hidayah yang Masuk Perguruan Tinggi dengan Metode Klasifikasi. Jurnal Informatika, 8, 26–33.
Kushartanto, A. I., & Aldisa, R. T. (2023). Data Mining Perbandingan Algoritma K-Nearest Neighbor dan Naïve Bayes dalam Prediksi Penerimaan Beasiswa. Journal of Computer System and Informatics (JoSYC), 5(1), 196–207. https://doi.org/10.47065/josyc.v5i1.4566
Nata, A., & Royal, S. (2022). Analisis Sistem Pendukung Keputusan Dengan Model Klasifikasi Berbasis Machine Learning Dalam Penentuan Penerima Program Indonesia Pintar. Journal of Science and Social Research, (3), 697–702. Retrieved from http://jurnal.goretanpena.com/index.php/JSSR
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 Universitas Muhammadiyah Tangerang, 79–87. https://doi.org/http://dx.doi.org/10.31000/cpu.v0i0.1685
Pebdika, A., Herdiana, R., & Solihudin, D. (2023). Klasifikasi Menggunakan Metode Naive Bayes Untuk Menentukan Calon Penerima PIP. Jurnal Mahasiswa Teknik Informatika, 7(1), 452–458. https://doi.org/10.36040/jati.v7i1.6303
Priyambodo, D., Nugroho, A., & Zaman, B. (n.d.). Prediksi Ketepatan Waktu Studi Mahasiswa Bidik Misi Menggunakan K-Nearest Neighbour. Jurnal Pendidikan Teknologi Informasi (JUKANTI), (5).
Puslapdik. (2022). Pusat Layanan Pembiayaan Pendidikan kementerian Pendidikan, Kebudayaan, Riset Dan Teknologi. Retrieved April 12, 2024, from Program Indonesia Pintar website: https://pip.kemdikbud.go.id/
Puslapdik. (2023a). SIPINTAR Enterprise, Sistem Informasi Indonesia Pintar, Data Penyaluran Nasional. Retrieved April 12, 2024, from Pusat Layanan Pembiayaan Pendidikankementerian Pendidikan, Kebudayaan, Riset Dan Teknologi website: https://pip.kemdikbud.go.id/penyaluran/nasional
Puslapdik. (2023b). SIPINTAR Enterprise, Sistem Informasi Indonesia Pintar, Data Penyaluran Provinsi. Retrieved April 12, 2024, from Pusat Layanan Pembiayaan Pendidikankementerian Pendidikan, Kebudayaan, Riset Dan Teknologi website: https://pip.kemdikbud.go.id/penyaluran
Rahmadini, LorencisLubis, E. E., Priansyah, A., N, Y. R. W., & Meutia, T. (2023). PENERAPAN Data Mining Untuk Memprediksi Harga Bahan Pangan Di Indonesia Menggunakan Algoritma K-Nearest Neighbor. JURNAL MAHASISWA AKUNTANSI SAMUDRA (JMAS)2023, 4(4), 223–235. https://doi.org/10.33059/jmas.v4i4.7074
Ramdhani, A. M. (2023). Petunjuk Teknis Pelaksanaan Program Indonesia Pintar Untuk Siswa Madrasah Tahun Anggaran 2023. Retrieved from www.pendis.kemenag.go.id
Sumiah, A., & Mirantika, N. (2020). Perbandingan Metode K-Nearest Neighbor dan Naive Bayes untuk Rekomendasi Penentuan Mahasiswa Penerima Beasiswa pada Universitas Kuningan. Buffer Informatika, 6 (1), 1–10. Retrieved from https://journal.uniku.ac.id/index.php/buffer
Widaningsih, S., & Yusuf, S. (2022). Penerapan Data Mining Untuk Memprediksi Siswa Berprestasi Dengan Menggunakan Algoritma K Nearest Neighbor. Jurnal Teknik Informatika Dan Sistem Informasi, 9(3), 2598–2611. Retrieved from http://jurnal.mdp.ac.id
Winarno, J. (2023). Penerapan Algoritma K-Nearest Neighbour Untuk Prediksi Penerima Beasiswa. Teknologiterkini.Org, 3(3), 1–16.
Yandi Saputra, A., & Primadasa, Y. (2018). Penerapan Teknik Klasifikasi Untuk Prediksi Kelulusan Mahasiswa Menggunakan Algoritma K-Nearest Neighbour Implementation of Classification Method to Predict Student Graduation Using K-Nearest Neighbor Algorithm. Techno Com, 17(4), 395–403. https://doi.org/10.33633/tc.v17i4.1864
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