Penentuan Jenis Persalinan Berdasarkan Faktor-Faktor Klinis Menggunakan K-NN dan Naive Bayes

  • Kristianto Oggie Ibrahim STIKOM Yos Sudarso
  • Diwahana Mutiara Candrasari SEKOLAH TINGGI ILMU KOMPUTER YOS SUDARSO
  • Rosa Ratri Kusuma Hariningsih STIKOM Yos Sudarso

Abstract

The purpose of this research is to classify the type of normal or caesarean delivery using the K-NN and Naïve Bayes algorithms. The data used are data on maternal age, hemoglobin, gestational age, pregnancy problems. The results of this study show that the Naïve Bayes algorithm is able to classify the type of normal or cesarean delivery. With the validation method K-Fold Cross Validation and Confusion Matrix for the calculation of misclassification which will make us know how bad our model is in making predictions, to calculate the accuracy level of the classification method we use. The results show that in the calculation of Cross Validation validation, the accuracy presentation value is 63.57% for training data and 95.48% for testing data and in Confusion Matrix, the accuracy presentation value is 66.2% for training data and 96.18% for testing data.

References

Annur, H. (2018). Klasifikasi Masyarakat Miskin Menggunakan Metode Naive Bayes. ILKOM Jurnal Ilmiah, 10(2), 160–165.
Bode, A. (2017). K-Nearest Neighbor Dengan Feature Selection Menggunakan Backward Elimination Untuk Prediksi Harga Komoditi Kopi Arabika. ILKOM Jurnal Ilmiah, 9(2), 188–195.
Etriyanti, E., Syamsuar, D., & Kunang, N. (2020). Implementasi Data Mining Menggunakan Algoritme Naive Bayes Classifier dan C4.5 untuk Memprediksi Kelulusan Mahasiswa. Telematika, 13(1), 56–67.
Feblian, D., & Daihani, D. U. (2017). Implementasi Model Crisp-Dm Untuk Menentukan Sales Pipeline Pada Pt X. Jurnal Teknik Industri, 6(1), 1–12.
Ilyas, M. M. (2017). Pelayanan Pendaftaran Pasien Rawat Jalan Di Rumah Sakit. Prosiding Seminar Nasional Darmajaya, 1(1), 477–486.
Kusrini & Luthfi, E. T. (2009). Alogritma Data Mining. Penerbit Andi. Yogkarta.
Nugraha, F. F., Sunandar, I., & Juliane, C. (2022). Penerapan Data Mining dengan Metode Klasifikasi Menggunakan Algoritma C4.5. Jurnal Teknik Informatika dan Sistem Informasi, 9(4), 2862 – 2869.
Nurfalinda, Nola Ritha, M. (n.d.). 1 , 2 , 3. 1–8.
M. J. Zaki, W. Meira Jr & W. Meira (2014). Data Mining and Analysis: Fundamental Concepts and Algorithms. Cambridge University Press.
Puteri, Q. A., Sagirani, T., & Lemantara, J. (2023). Perbandingan Algoritma Naïve Bayes dan K-Nearest Neighbor (KNN) untuk Mengetahui Keakuratan Diagnoas Penyakit Diabetes. Jurnal Nasional Teknologi dan Sistem Informasi, 9(03), 248 – 254.
Rani, H. A. D., Zuhri, S., & Fuji, S. (2020). Sistem Prediksi Kondisi Kelahiran Bayi menggunakan Klasifikasi Naïve Bayes. Joined Journal (Journal of Informatics Education), 3(2), 48.
Setyawati, E., Wijoyo, H, & Soeharmoko, N. (2020). Relational Database Management System (RDBMS). Building and Maintaining a Data Warehouse, 43–51.
Shedriko., Firdaus M. (2022). Penentuan Klasifikasi dengan CRISP-DM dalam Memprediksi Kelulusan Mahasiswa pada Suatu Mata Kuliah. Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK). 826 – 831.
Telaumbanua, F. D., Hulu, P., Nadeak, T. Z., Lumbantong, R. R., & Dharma, A. (2019). Penggunaan Machine Learning. 3, 57–64.
Triyadi, T. (2020). Aplikasi Monitoring Server dan Analisis Kualitas Menggunakan Model ISO 9126. STRING (Satuan Tulisan Riset Dan Inovasi Teknologi), 4(3), 304.
Published
2024-06-30
How to Cite
IBRAHIM, Kristianto Oggie; CANDRASARI, Diwahana Mutiara; HARININGSIH, Rosa Ratri Kusuma. Penentuan Jenis Persalinan Berdasarkan Faktor-Faktor Klinis Menggunakan K-NN dan Naive Bayes. Joined Journal (Journal of Informatics Education), [S.l.], v. 7, n. 1, p. 24-41, june 2024. ISSN 2620-8415. Available at: <https://e-journal.ivet.ac.id/index.php/jiptika/article/view/3206>. Date accessed: 03 dec. 2024. doi: https://doi.org/10.31331/joined.v7i1.3206.

Most read articles by the same author(s)

Obs.: This plugin requires at least one statistics/report plugin to be enabled. If your statistics plugins provide more than one metric then please also select a main metric on the admin's site settings page and/or on the journal manager's settings pages.