ANALISIS DAMPAK NILAI K OPTIMAL TERHADAP AKURASI PADA DATA BALITA PUSKESMAS CIPAKU
Abstract
Kurang gizi jangka panjang, yang disebabkan oleh asupan makanan yang rendah dalam jumlah waktu yang cukup lama, dikenal sebagai stunting. Fokus utama penelitian ini adalah menentukan nilai optimal dari K dalam algoritma K-NN, kemudian melanjutkan dengan evaluasi mendalam terhadap pengukuran nilai akurasi, dan penentuan nilai K optimal yang berpengaruh terhadap nilai akurasi. Penelitian ini akan menggunakan metode Klasifikasi dalam mengklasifikasi data yang sudah ada. Untuk pengolahannya juga penelitian ini menggunakan algoritma K-NN. Pengolahan data dilakukan menggunakan tools yaitu RapidMiner Versi 10.0. Hasil penelitian menunjukan nilai K optimal adalah 2 dengan nilai akurasi sebesar 95,21%. Manfaat dari penelitian ini terhadap pihak terkait yaitu memberikan informasi yang hasil penelitian mengenai status gizi balita di Puskesmas Cipaku, sehingga dapat membantu para tenaga kesehatan dan pemerintah desa dalam mengaÂmbil tindakan pencegahan dan intervensi yang lebih efektif.References
Arisusanto, A., Suarna, N., & Dwilestari, G. (2023). Analisa Klasifikasi Data Harga Handphone Menggunakan Algoritma Random Forest Dengan Optimize Parameter Grid. Jurnal Teknologi Ilmu Komputer, 1(2), 43–47. https://doi.org/10.56854/jtik.v1i2.51
Batubara, D. N., & Windarto, A. P. (2019). Analisa Klasifikasi Data Mining Pada Tingkat Kepuasan Pengunjung Taman Hewan Pematang Siantar Dengan Algoritma. KOMIK (Konferensi)
Ikhwan, A., & Aslami, N. (2020). Implementasi Data Mining untuk Manajemen Bantuan Sosial Menggunakan Algoritma K-Means. (JurTI) Jurnal Teknologi Informasi.
Lonang, S., & Normawati, D. (2022). Klasifikasi Status Stunting Pada Balita Menggunakan K-Nearest Neighbor Dengan Feature Selection Backward Elimination. Jurnal Media.
Maulidah, W. B., Rohmawati, N., Sulistiyani, S., Gizi, B., Masyarakat, K., Masyarakat, F. K., & Jember, U. (2019). Faktor yang berhubungan dengan kejadian stunting pada balita di Desa Panduman Kecamatan Jelbuk Kabupaten Jember Risk factor of stunting among under five children in Panduman Village , Jelbuk Sub- District , Jember Regency Hasil survei Pemantauan Status Gi. 02(02), 89–100.
Murti, F. C., Suryati, S., & Oktavianto, E. (2020). Hubungan Berat Badan Lahir Rendah (Bblr)Dengan Kejadian Stunting Pada Balita Usia 2-5 Tahun Di Desa Umbulrejo Kecamatan Ponjong Kabupaten Gunung Kidul. In Jurnal Ilmiah Kesehatan Keperawatan (Vol. 16, Issue 2, p. 52). Universitas Muhammadiyah Gombong. https://doi.org/10.26753/jikk.v16i2.419
Muttaqin, M. R., & Defriani, M. (2020). Algoritma K-Means untuk Pengelompokan Topik Skripsi Mahasiswa. In ILKOM Jurnal Ilmiah. scholar.archive.org.
Nurofik, A., Rahajeng, E., Munti, N. Y. S., Sutisna, Firmansyah, H., Sani, A., Hendarsyah, D., Adrianto, S., Darma, W. A., Herdiansah, A., Ariestiandy, D., Nurnaningsih, D., Setiawan, I., Wiyono, A. S., & Zaharah. (2021). Pengantar Teknologi Informasi (I. Kusumawati & M. Sari, Eds.; Ed.1). Insania
Prasetiya, T., Ali, I., Rohmat, C. L., & Nurdiawan, O. (2020). Klasifikasi Status Stunting Balita Di Desa Slangit Menggunakan Metode K-Nearest Neighbor. In INFORMATICS FOR EDUCATORS AND PROFESSIONAL : Journal of Informatics (Vol. 5, Issue 1, p. 93). Universitas Bina Insani. https://doi.org/10.51211/itbi.v5i1.1431
Resmiati, R., & Arifin, T. (2021). Klasifikasi Pasien Kanker Payudara Menggunakan Metode Support Vector Machine dengan Backward Elimination. SISTEMASI: Jurnal Sistem Informasi.
Puspitasari, A., Rudianto, B., Nasution, R., & Prasetya, M. A. (2022). Game Edukasi Pengenalan Tumbuhan untuk Anak Sekolah Dasar Kelas 3 Berbasis Augmented Reality. JIKA (Jurnal Informatika), 6(1), 10–17. https://doi.org/10.31000/jika.v6i1.5155
Saleh, H., Faisal, M., & Musa, R. I. (2019). Klasifikasi Status Gizi Balita Menggunakan Metode K-Nearest Neighbor,. In Simtek : jurnal sistem informasi dan teknik komputer (Vol. 4, Issue 2, pp. 120–126). STMIK Catur Sakti Kendari. https://doi.org/10.51876/simtek.v4i2.60
Ulya, S., Soeleman, M. A., & Budiman, F. (2021). Optimasi Parameter K Pada Algoritma K-NN Untuk Klasifikasi Prioritas Bantuan Pembangunan Desa. Techno.Com, 20(1), 83–96. https://doi.org/10.33633/tc.v20i1.4215
Urban-village, T., Sub-district, K. J., & Jakarta, D. K. I. (2021). Gambaran Pola Asuh Ibu dengan Balita Stunting dan Tidak Stunting di Kelurahan Tengah , Kecamatan Kramat Jati , DKI Jakarta Overview of Mothers Parenting Patterns with Stunting and Non-Stunting Toddlers. 3(2), 71–78. https://doi.org/10.47034/ppk.v3i2.4158
Wahyudi, R., Orisa, M., & Vendyansyah, N. (2021). Penerapan Algoritma K-Nearest Neighbors Pada Klasifikasi Penentuan Gizi Balita (Studi Kasus Di Posyandu Desa Bluto). In JATI (Jurnal Mahasiswa Teknik Informatika) (Vol. 5, Issue 2, pp. 750–757). LPPM ITN Malang. https://doi.org/10.36040/jati.v5i2.3738
Yuliska, Y., & Syaliman, K. U. (2020). Peningkatan Akurasi K-Nearest Neighbor Pada Data Index Standar Pencemaran Udara Kota Pekanbaru. IT Journal Research and Development, 5(1), 11–18. https://doi.org/10.25299/itjrd.2020.vol5(1).4680
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