Prediksi Jumlah Bayi Penerima Imunisasi DPT 1 dan DPT 2 Menggunakan Support Vector Regression
 (*)Nova Idriani R  (Universitas Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)
Inggih Permana (Universitas Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)
Febi Nur Salisah (Universitas Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)
Megawati Megawati (Universitas Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)
Medyantiwi Rahmawita M (Universitas Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)
(*) Corresponding Author Submitted: May 7, 2024; Published: July 26, 2024 |
Abstract
Vaccination against diphtheria, pertussis (whooping cough), and tetanus is known as DPT immunization, which protects a person from three serious diseases. This vaccine is given in the form of an injection where there are 5 antigens in one injection of the vaccine. DPT immunization is a complete routine immunization that will be continued in grades 1 to 6 elementary school. DPT immunization is feared by mothers because of the side effects that occur in babies after the vaccine injection, namely that the baby will have a fever and be fussy. This has resulted in delays in collecting data on babies who have received this immunization, which has an impact on estimates of babies who will receive DPT immunization in the following month. Of course, this will disrupt the stock of vaccines provided, causing the potential for them to be out of stock. To overcome this problem, it is necessary to collect data on babies who have received DPT in the previous month. This data will be used to predict babies who will receive DPT immunization in the following month using the Support Vector Regression (SVR) method. So that the community health center can provide information regarding the prediction of the number of babies who will receive DPT immunization. This method uses three kernels and a Sliding Window to divide the data into smaller segments, moving alternately across the time series data, making it suitable for predicting babies who will receive DPT immunization in the next time interval. From the three kernels used on the two data that have been separated into DPT 1 and DPT 2, windowing size 3 linear kernels were obtained which were selected as an accurate evaluation of model work on DPT 1 with MAPE values of 3.35, RMSE 0.193, and R2 0.1. And windowing size 3 RBF kernels are more optimal in DPT 2 with MAPE values of 7.86, RMSE 0.163, and R2 0.288.
Keywords
Full Text:
PDFArticle Metrics
Abstract view : 116 timesPDF - 96 times
References
N. Ayu and V. Irawati, “Imunisasi Dasar dalam Masa Pandemi COVID-19,” 2020.
D. Tri Sundari, I. Rusmiati, R. Oktavia, P. D. Studi III Kebidanan, and S. Mitra Adiguna Palembang, “PENYULUHAN IMUNISASI DASAR DAN ASI EKSKLUSIF,” Communnity Development Journal, vol. 3, no. 3, pp. 1770–1773, 2022.
Dini Nurbaeti Zen, Tita Rohita, and Siti Sopiah, “HUBUNGAN SIKAP IBU YANG MEMPUNYAI BAYI DENGAN PELAKSANAAN IMUNISASI DPT DI PUSKESMAS KAWALI KABUPATEN CIAMIS TAHUN 2018,” Keperawatan Galuh, vol. 1, no. 1, 2019, Accessed: May 06, 2024. [Online]. Available: https://jurnal.unigal.ac.id/JKG/article/view/1789
M. Permata Sari, A. Zatil Izzah, and A. Perdana Harmen, “Gambaran Kejadian Ikutan Pasca Imunisasi pada Anak yang Mendapatkan Imunisasi Difteri Pertusis dan Tetanus di Puskesmas Seberang Padang Kota Padang,” 2018. [Online]. Available: http://jurnal.fk.unand.ac.id
“Kementrian Kesehatan 2020”, Accessed: May 06, 2024. [Online]. Available: https://www.google.com/url?sa=t&source=web&rct=j&opi=89978449&url=https://www.kemkes.go.id/app_asset/file_content_download/Profil-Kesehatan-Indonesia-2019.pdf&ved=2ahUKEwiP7P3N2fiFAxWq1TgGHf3XA3oQFnoECBIQAQ&usg=AOvVaw2aQxHB8O9s5UzexKlnI1Y1
L. Lestari, H. Heryani, F. Nopianti, F. Nur, and I. Lestari, “Upaya Peningkatan Pemahaman Pentingnya Imunisasi DPT pada Kader,” Jurnal Pengabdian Masyarakat, 2022, doi: 10.56359/kolaborasi.
A. Budi Raharjo, Z. Zizki Dinanto, D. Sunaryono, and D. Purwitasari, “Prediksi Akumulasi Kasus Terkonfirmasi Covid-19 di Indonesia Menggunakan Support Vector Regression Prediction of Accumulation of COVID-19 Confirmed Cases in Indonesia using Support Vector Regression,” 2021.
L. Malem Ginting, M. MTSigiro, E. Delima Manurung, and J. Jasa Putra Sinurat, “Perbandingan Metode Algoritma Support Vector Regression dan Multiple Linear Regression Untuk Memprediksi Stok Obat,” 2021. [Online]. Available: http://journal-jati.del.ac.id/
N. Putu et al., “Penerapan Support Vector Regression (Svr) dalam Memprediksi Jumlah Kunjungan Wisatawan Domestik ke Bali,” 2019.
F. E. Penalun, A. Hermawan, and D. Avianto, “Perbandingan Random Forest Regression dan Support Vector Regression Pada Prediksi Laju Penguapan,” Jurnal FASILKOM, 2023.
B. H. Prakoso, “Implementasi Support Vector Regression pada Prediksi Inflasi Indeks Harga Konsumen,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 19, no. 1, pp. 155–162, Nov. 2019, doi: 10.30812/matrik.v19i1.511.
A. Rahmawati, W. Sulandari, S. Subanti, and Y. Yudhanto, “Penerapan Metode Reccurent Neural Network dengan Pendekatan Long Short-Term Memory (LSTM) untuk Meramalkan Harga Saham Hybe Corporation The Application of Recurrent Neural Network Method with the Long Short-Term Memory (LSTM) Approach to Forecast Hybe Corporation’s Stock Price,” Jurnal Bumigora Information Technology (BITe), vol. 5, no. 1, pp. 65–76, 2023, doi: 10.30812/bite/v5i1.2973.
D. Iskandar Mulyana, “Optimasi Prediksi Harga Udang Vaname dengan Metode RMSE dan MAE Dalam Algoritma Regresi Linier,” 2022.
I. Nabillah and I. Ranggadara, “Mean Absolute Percentage Error untuk Evaluasi Hasil Prediksi Komoditas Laut,” JOINS (Journal of Information System), vol. 5, no. 2, pp. 250–255, Nov. 2020, doi: 10.33633/joins.v5i2.3900.
Fatmawati and N. A. K. Rifai, “Klasifikasi Penyakit Diabetes Retinopati Menggunakan Support Vector Machine dengan Algoritma Grid Search Cross-validation,” Jurnal Riset Statistika, pp. 79–86, Jul. 2023, doi: 10.29313/jrs.v3i1.1945.
R. Rizqi Robbi Arisandi, B. Warsito, and A. Rachman Hakim, “APLIKASI NAÏVE BAYES CLASSIFIER (NBC) PADA KLASIFIKASI STATUS GIZI BALITA STUNTING DENGAN PENGUJIAN K-FOLD CROSS VALIDATION,” vol. 11, no. 1, pp. 130–139, 2022, [Online]. Available: https://ejournal3.undip.ac.id/index.php/gaussian/
N. Hendrastuty, A. Rahman Isnain, and A. Yanti Rahmadhani, “Analisis Sentimen Masyarakat Terhadap Program Kartu Prakerja Pada Twitter Dengan Metode Support Vector Machine,” vol. 6, no. 3, 2021, [Online]. Available: http://situs.com
F. R. Lumbanraja, R. Sulaiman Sani, D. Kurniawan, and A. Rose Irawati, “IMPLEMENTASI METODE SUPPORT VECTOR MACHINE DALAM PREDIKSI PERSEBARAN DEMAM BERDARAH DI KOTA BANDAR LAMPUNG,” 2019. [Online]. Available: https://lampung.bps.go.id/dynamictable/2016/11/23/71/jumlah-curah-
R. Andika, H. Agus Rahmad, P. M Aditya Yoga, B. Ida Bagus, I. Alkausar, and P. Shinta, “PENERAPAN MODEL EXPONENSIAL DAN LOGISTIK DALAM PREDIKSI POPULASI : STUDI KASUS KOTA PALEMBANG,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 12, no. 2, Apr. 2024, doi: 10.23960/jitet.v12i2.4005.
R. E. Cahyono and J. P. Sugiono, “Analisis Kinerja Metode Support Vector Regression (SVR) dalam Memprediksi Indeks Harga Konsumen (Performance Analysis of Support Vector Regression (SVR) Methods in Predicting the Consumer Price Index),” vol. 1, no. 2, pp. 106–116, 2019, [Online]. Available: www.siskaperbapo.com
D. I. Ridwan, C. Setianingsih, and M. A. Murti, “PREDIKSI PENGGUNAAN ENERGI LISTRIK DENGAN MENGGUNAKAN METODE SUPPORT VECTOR REGRESSION PREDICTION OF ELECTRICITY USING SUPPORT VECTOR REGRESSION METHOD,” 2021.
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 JURNAL MEDIA INFORMATIKA BUDIDARMA
This work is licensed under a Creative Commons Attribution 4.0 International License.
JURNAL MEDIA INFORMATIKA BUDIDARMA
STMIK Budi Darma
Secretariat: Sisingamangaraja No. 338 Telp 061-7875998
Email: mib.stmikbd@gmail.com
This work is licensed under a Creative Commons Attribution 4.0 International License.