ANALISIS KECANDUAN SMARTPHONE PADA MAHASISWA MENGGUNAKAN METODE K-NEARST NEIGHBORS (K-NN)
DOI:
https://doi.org/10.69714/623bk437Keywords:
content, formatting, articleAbstract
Smartphone are a tecnology that is widely used among teenagers. Smartphone have a negative impact on teenagers, one of which is that amsrtphone addiction can interfere with various activities in teenagers’ real lives.this writing aims to understand and describe various aspects including health aspects, psychological aspects, academic aspects, social aspects and financial aspects. Classification is carried out to support decision making regarding smartphone addiction problems. K-Nearest Neighbors (KNN) is a machine learning classification method used in this research. The research results show that the best method for classifying smartphone addiction is KNN with attribute selection using Linear Regression based on weight correlation.
References
N. K. C. B. Dewi, N. K. A. Wirdiani, and D. M. S. Arsa, “Klasifikasi Kecanduan Smartphone pada Pelajar Sekolah Menengah Atas menggunakan Metode Machine Learning Berbasis Feature Weighting,” J. Edukasi dan Penelit. Inform., vol. 8, no. 1, p. 95, 2022, doi: 10.26418/jp.v8i1.51914.
M. P. Al Faridzi, S. Niman, F. Widiantoro, and T. Shinta, “Tingkat Kecanduan Smartphone pada Mahasiswa Selama Pandemi Covid 19,” Elisabeth Heal. J., vol. 7, no. 1, pp. 81–88, 2022, doi: 10.52317/ehj.v7i1.417.
R. P. Bakri, “Pengaruh Stres Akademik dan Kecanduan Smartphone Terhadap Prokrastinasi Akademik,” Psikoborneo J. Ilm. Psikol., vol. 9, no. 3, p. 578, 2021, doi: 10.30872/psikoborneo.v9i3.6501.
D. K. Hidayanto, R. Rosid, A. H. Nur Ajijah, and Y. Khoerunnisa, “Pengaruh Kecanduan Telpon Pintar (Smartphone) pada Remaja (Literature Review),” J. Publisitas, vol. 8, no. 1, pp. 73–79, 2021, doi: 10.37858/publisitas.v8i1.67.
Z. Cindya Dwynne, D. Nur Aini, T. Ayunita Pertiwi, and D. Pramana, “Cluster Tingkat Kecanduan Game Online Pada Mahasiswa Fakultas Sains dan Teknologi dan Korelasinya Terhadap Minat Belajar,” Pros. Sentimas, vol. 1, no. 1, pp. 126–132, 2023, [Online]. Available: https://journal.irpi.or.id/index.php/sentimas
P. Aisha, M. Fathurahman, and S. Prangga, “Implementasi Metode Neighbor Weighted K-Nearest Neighbor Pada Pengklasifikasian Status Gizi Balita Di Wilayah Kerja Puskesmas Wonorejo Kota Samarinda,” Var. J. Stat. Its Appl., vol. 6, no. 1, pp. 11–20, 2024, doi: 10.30598/variancevol6iss1page11-20.
+S.+K.+M.+K.+D.+A.+N.+A.+P.+S.+K.+M.+K.+(n.d.).+DATA+MINING:+Pengolahan+Data+Menjadi++Informasi+dengan+RapidMiner.+CV+Kekata+Group.+https://books.google.co.id/books%3Fid%3DrTlmDwAAQBAJ&ots=ujN91oPSae&sig=Ryd3exJtwVnQ58u7lIziSNdVOn4&redir_esc=y#v=onepage&q https://books.google.co.id/books?hl=id&lr=&id=rTlmDwAAQBAJ&oi=fnd&pg=PR7&dq=Amril+Mutoi+Siregar, data mining.
Moch. Rizky Yuliansyah, M. B, and A. Franz, “Perbandingan Metode K-Nearest Neighbors dan Naïve Bayes Classifier Pada Klasifikasi Status Gizi Balita di Puskesmas Muara Jawa Kota Samarinda,” Adopsi Teknol. dan Sist. Inf., vol. 1, no. 1, pp. 08–20, 2022, doi: 10.30872/atasi.v1i1.25.
F. Ahluna et al., “Metode K-Nearest Neighbor Untuk Analisis Sentimen Tentang Penghapusan Ujian Nasional,” J. Ikraith-Informatika, vol. 7, no. 2, pp. 1–6, 2023.
A. N. Yuliarina and H. Hendry, “Comparison of Prediction Analysis of Gofood Service Users Using the Knn & Naive Bayes Algorithm With Rapidminer Software,” J. Tek. Inform., vol. 3, no. 4, pp. 847–856, 2022, doi: 10.20884/1.jutif.2022.3.4.294.











