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:
https://doi.org/10.35870/ijsecs.v4i2.2874Keywords:
Early Childhood, Psychological Impact, Data Mining, K-Means, New MediaAbstract
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
Downloads
References
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.
Downloads
Published
License
Copyright (c) 2024 Sugiyono, Haryati, Frencis Matheos Sarimole, Tundo

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Copyright Retention and Open Access License
Authors retain copyright of their work and grant the journal non-exclusive right of first publication under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license allows unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
2. Rights Granted Under CC BY 4.0
Under this license, readers are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, including commercial use
- No additional restrictions — the licensor cannot revoke these freedoms as long as license terms are followed
3. Attribution Requirements
All uses must include:
- Proper citation of the original work
- Link to the Creative Commons license
- Indication if changes were made to the original work
- No suggestion that the licensor endorses the user or their use
4. Additional Distribution Rights
Authors may:
- Deposit the published version in institutional repositories
- Share through academic social networks
- Include in books, monographs, or other publications
- Post on personal or institutional websites
Requirement: All additional distributions must maintain the CC BY 4.0 license and proper attribution.
5. Self-Archiving and Pre-Print Sharing
Authors are encouraged to:
- Share pre-prints and post-prints online
- Deposit in subject-specific repositories (e.g., arXiv, bioRxiv)
- Engage in scholarly communication throughout the publication process
6. Open Access Commitment
This journal provides immediate open access to all content, supporting the global exchange of knowledge without financial, legal, or technical barriers.
