Published: 2024-08-10
Data Mining Modeling Using the K-Means Algorithm to Analyze the Impact of New Media on Early Childhood Psychology at Bimba Rainbow Kids Sukmajaya
DOI: 10.35870/ijsecs.v4i2.2874
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Abstract
New media, particularly the internet, has become an integral aspect of contemporary life, fundamentally altering the ways in which individuals interact, learn, play, and access information. The continuous evolution of new media, driven by technological advancements, exerts a profound influence on its users, with implications that span various dimensions of human experience. This study aims to analyze and classify the psychological impact of new media on early childhood, specifically within the context of Bimba Rainbow Kids Sukmajaya, utilizing the K-Means data mining method. This research employs a qualitative approach to uncover the underlying factors that shape the psychological effects observed in young children. The anticipated outcomes of this study are expected to contribute significantly to the academic discourse on the influence of new media on early childhood psychology. Moreover, the findings hold potential relevance for educators, parents, teachers, policymakers, and the general public who are invested in comprehending the broader implications of new media on the psychological development of early childhood
Keywords
Early Childhood ; Psychological Impact ; Data Mining ; K-Means ; New Media
Article Information
This article has been peer-reviewed and published in the International Journal Software Engineering and Computer Science (IJSECS). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
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Issue: Vol. 4 No. 2 (2024)
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Section: Articles
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Published: %750 %e, %2024
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License: CC BY 4.0
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Copyright: © 2024 Authors
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DOI: 10.35870/ijsecs.v4i2.2874
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Sugiyono
Informatics Engineering Study Program, Faculty of Computer Technology, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, East Jakarta City, Special Capital Region of Jakarta, Indonesia
Haryati
Informatics Engineering Study Program, Faculty of Computer Technology, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, East Jakarta City, Special Capital Region of Jakarta, Indonesia
Frencis Matheos Sarimole
Informatics Engineering Study Program, Faculty of Computer Technology, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, East Jakarta City, Special Capital Region of Jakarta, Indonesia
Tundo
Informatics Engineering Study Program, Faculty of Computer Technology, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, East Jakarta City, Special Capital Region of Jakarta, Indonesia
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Rizal, S., & Khotimah, R. Q. (2022). Penerapan data mining untuk clustering data penduduk yang terdampak Covid-19 menggunakan algoritma K-Means. Jurnal Pendidikan dan Konseling, 4(4), 2781–2792.
-
Sudibyo, N. A., Iswardani, A., Sari, K., & Suprihatiningsih, S. (2020). Penerapan data mining pada jumlah penduduk miskin di Indonesia. Jurnal Lebesgue: Jurnal Ilmiah Pendidikan Matematika dan Matematika, 1(3), 199–207. https://doi.org/10.46306/lb.v1i3.42
-
Gustientiedina, G., Adiya, M. H., & Desnelita, Y. (2019). Penerapan algoritma K-Means untuk clustering data obat-obatan. Jurnal Nasional Teknologi dan Sistem Informasi, 5(1), 17–24. https://doi.org/10.25077/teknosi.v5i1.2019.17-24
-
Gustian, D., & Al-Farits, M. S. (2023). Data mining untuk melihat minat belajar siswa menerapkan metode K-Means. Jurnal Information System Research, 4(3), 775–784. https://doi.org/10.47065/josh.v4i3.3218
-
Ikhwan, A., & Aslami, N. (2020). Implementasi data mining untuk manajemen bantuan sosial menggunakan algoritma K-Means. Jurnal Teknologi Informasi, 4(2), 208–217. https://doi.org/10.36294/jurti.v4i2.2103
-
Rahmawati, R., & Bahtiar, A. (2023). Pengelompokan remaja berdasarkan segmentasi usia menggunakan metode K-Means clustering (Studi kasus: Desa Sindangsari). Jurnal Riset Ilmu Akuntansi, 2(2), 35–51. https://doi.org/10.37600/tekinkom.v2i2.115
-
Azhari, R., Hartama, D., Lubis, M. R., Nasution, D. F., & Windarto, A. P. (2023). Analisis penerapan data mining terhadap kasus positif Covid-19 menggunakan metode K-Means clustering. Jurnal Informatics, Electronics, and Electrical Engineering, 3(2), 221–235. https://doi.org/10.47065/jieee.v3i2.1760
-
Saputra, E. A., & Nataliani, Y. (2021). Analisis pengelompokan data nilai siswa untuk menentukan siswa berprestasi menggunakan metode clustering K-Means. Jurnal Information System Informatics, 3(3), 424–439. https://doi.org/10.51519/journalisi.v3i3.164
-
Simarmata, R., & Samuel, Y. T. (2021). Analisa pengaruh penggunaan gadget terhadap nilai akhir siswa SMA secara umum menggunakan metode data mining (Decision Tree). TeIKa, 11(1), 15–28. https://doi.org/10.36342/teika.v11i1.2475
-
Apriyani, P., Dikananda, A. R., & Ali, I. (2023). Penerapan algoritma K-Means dalam klasterisasi kasus stunting balita desa Tegalwangi. Hello World Journal of Computer Science, 2(1), 20–33. https://doi.org/10.56211/helloworld.v2i1.230
-
Aristika, W., & Hartono, W. J. (2020). Penerapan clustering K-Means untuk menentukan pengaruh media sosial Facebook terhadap usaha mikro, kecil dan menengah (UMKM) di Kecamatan Pekanbaru Kota. Jurnal Ilmu Komputer dan Bisnis, 11(1), 2389–2395.
-
Tambunan, M. P. (2021). Penerapan data mining dalam analisa data pemakaian obat dengan menerapkan algoritma K-Means. Jurnal Informasi dan Teknologi Ilmiah, 8(3), 109–113.
-
Nugroho, B. I., Ma’arif, Z., & Arif, Z. (2022). Tinjauan pustaka sistematis: Penerapan data mining metode klasifikasi untuk menganalisa penyalahgunaan sosial media. Jurnal Sistem Informasi dan Teknologi Informasi, 3(2), 46–51. http://journal.peradaban.ac.id/index.php/jsitp/article/download/1265/860
-
Al Halik, M. F., & Septiana, L. (2022). Analisa data untuk prediksi daerah rawan bencana alam di Jawa Barat menggunakan algoritma K-Means clustering. Jurnal Information Systems Applied, Management, and Accounting Research, 6(4), 856–870. https://doi.org/10.52362/jisamar.v6i4.939
-
Indraputra, R. A., & Fitriana, R. (2020). K-Means clustering data COVID-19. Jurnal Teknik Industri, 10(3), 275–282. https://doi.org/10.25105/jti.v10i3.8428
-
Afrilia, M. N., Rahaningsih, N., Dana, R. D., & Nuris, N. D. (2024). Optimasi analisis clustering untuk aktivitas dan respon pengguna media sosial dengan K-Means. JATI (Jurnal Mahasiswa Teknik Informatika), 8(1), 148–155. https://doi.org/10.36040/jati.v8i1.8334
-
Ramadhani, D. I., Damayanti, O., Thaushiyah, O., & Kadafi, A. R. (2022). Penerapan metode K-Means untuk clustering desa rawan bencana berdasarkan data kejadian terjadinya bencana alam. JURIKOM (Jurnal Riset Komputer), 9(3), 749. https://doi.org/10.30865/jurikom.v9i3.4326.
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