Implementasi Data Mining untuk Prediksi Status Proses Persalinan pada Ibu Hamil Menggunakan Algoritma Naive Bayes

Mohammad Yazdi Pusadan; Ari Ghifari; Yusuf Anshori

doi:10.33050/tmj.v8i1.1980

Authors

Mohammad Yazdi Pusadan Tadulako University
Ari Ghifari Tadulako University
Yusuf Anshori Tadulako University

DOI:

https://doi.org/10.33050/tmj.v8i1.1980

Abstract

Childbirth is the process of taking out the fetus after 20 weeks of gestation or more to be able to live outside the uterus through the birth canal or another way, with or without assistance. Maternal Mortality Rate in Indonesia is still quite high based on the White Book of National Health System Reform in March 2022, at 305 for every 100.000 births. Causes of the high Maternal Mortality Rate is the risky of childbirth process for the mother and the baby. Clinical prediction is growing by adopting computer sience and information technology in data processing, accompanied by data mining methods for processing. The problem of pregnant mother can be anticipated by using the system for predicting the status of the childbirth process with the implementation of data mining and Naïve Bayes algorithm, with the purpose for helping to reduce Maternal Mortality Rate, especially caused by risky childbirth process. This study using 600 training data, then tested using the Confusion Matrix method on 100 testing data. Obtained Precision value was 82.4%, Recall value was 94%, F-Measure value was 88.7 and Accuracy value was 92%.

References

A. Ardiyansyah, P. A. Rahayuningsih, and R. Maulana, “Analisis Perbandingan Algoritma Klasifikasi Data Mining Untuk Dataset Blogger Dengan Rapid Miner,” Jurnal Khatulistiwa Informatika, vol. 6, no. 1, 2018.

F. F. Nugraha and C. Juliane, “Penerapan Data Mining Dengan Metode Klasifikasi Menggunakan Algoritma C4. 5,” JATISI (Jurnal Teknik Informatika dan Sistem Informasi), vol. 9, no. 4, pp. 2862–2869, 2022.

M. J. Zaki, W. Meira Jr, and W. Meira, Data mining and analysis: fundamental concepts and algorithms. Cambridge University Press, 2014.

I. B. A. Peling, I. N. Arnawan, I. P. A. Arthawan, and I. G. N. Janardana, “Implementation of Data Mining To Predict Period of Students Study Using Naive Bayes Algorithm,” Int. J. Eng. Emerg. Technol, vol. 2, no. 1, p. 53, 2017.

L. M. N. Bernita, Klasifikasi Persalinan Normal Atau Caesar Menggunakan Algoritma C4.5, vol. 13, no. 3. 2017.

P. L. Kumalasari, Sistem Pengambilan Keputusan Untuk Menentukan Proses Persalinan Dengan Metode Naïve Bayes Dan Forward Chaining. 2020.

R. R. Andarista and A. Jananto, “Penerapan Data Mining Algoritma C4. 5 Untuk Klasifikasi Hasil Pengujian Kendaraan Bermotor,” Jurnal Tekno Kompak, vol. 16, no. 2, pp. 29–43, 2022.

M. P. S. D. Ningsih and B. Noranita, “Status Proses Persalinan Menggunakan Algoritma C4.5,” Jurnal Masyarakat Informatika, vol. 9, no. 1, pp. 1–13, 2018.

H. A. Damar Rani and S. Zuhri, “Sistem Prediksi Kondisi Kelahiran Bayi menggunakan Klasifikasi Naïve Bayes,” Joined Journal (Journal of Informatics Education), vol. 3, no. 2, p. 48, 2020, doi: 10.31331/joined.v3i2.1432.

U. Rahardja, T. Hariguna, and W. M. Baihaqi, “Opinion mining on e-commerce data using sentiment analysis and k-medoid clustering,” Proceedings - 2019 12th International Conference on Ubi-Media Computing, Ubi-Media 2019, pp. 168–170, 2019, doi: 10.1109/Ubi-Media.2019.00040.

H. Amalia, A. B. Pohan, and S. Masripah, “Penerapan Feature Weighting Optimized Pada Naïve Bayes Untuk Prediksi Proses Persalinan,” Jurnal Pilar Nusa Mandiri, vol. 15, no. 1, pp. 15–20, 2019, doi: 10.33480/pilar.v15i1.3.

S. Watini, “Development of Java Hands Startup Business Idea Model by Lean Startup Approach,” Startupreneur Bisnis Digital (SABDA Journal), vol. 2, no. 1, pp. 43–50, 2023.

Samsu, Metode Penelitian : Teori dan Aplikasi Penelitian Kualitatif, Kuantittif, dan Mix Method serta Research and Development, no. June. 2017.

S. Purnama, Q. Aini, U. Rahardja, N. P. L. Santoso, and S. Millah, “Design of Educational Learning Management Cloud Process with Blockchain 4.0 based E-Portfolio,” Journal of Education Technology, vol. 5, no. 4, pp. 628–635, 2021.

D. Pascalina, R. Widhiastono, and C. Juliane, “Pengukuran Kesiapan Transformasi Digital Smart City Menggunakan Aplikasi Rapid Miner,” Technomedia Journal, vol. 7, no. 3, pp. 293–302, 2023.

A. Pratama and A. Wijaya, “Implementasi Sistem Good Corporate Governance Pada Perangkat Lunak Berbasis Website PT. Pusaka Bumi Transportasi,” Technomedia Journal, vol. 7, no. 3, pp. 340–353, 2023.

L. Meria, A. N. Bintoro, and D. Prastyani, “Peran Ethical Leadership Dalam Mengurangi Stres Kerja Dan Meningkatkan Kualitas Kinerja,” Technomedia Journal, vol. 7, no. 3, pp. 208–292, 2023.

E. Arif, E. Julianti, and I. P. Soko, “Penerapan Konsep Internet of Things pada Pengembangan Aplikasi Portal Alumni di Universitas Terbuka,” Technomedia Journal, vol. 7, no. 3, pp. 303–313, 2023.

N. Lutfiani, P. A. Sunarya, S. Millah, and S. A. Anjani, “Penerapan Gamifikasi Blockchain dalam Pendidikan iLearning,” Technomedia Journal, vol. 7, no. 3, pp. 399–407, 2023.

M. Sheshikala, D. R. Rao, and M. A. Kadampur, “An Improved Approximation Algorithm for Co-location Mining in Uncertain Data Sets using Probabilistic Approach,” Aptikom Journal on Computer Science and Information Technologies, vol. 2, no. 1, pp. 1–7, 2017.

L. Meria, Q. Aini, N. P. Lestari Santoso, U. Raharja, and S. Millah, “Management of Access Control for Decentralized Online Educations using Blockchain Technology,” in 2021 Sixth International Conference on Informatics and Computing (ICIC), IEEE, Nov. 2021, pp. 1–6. doi: 10.1109/ICIC54025.2021.9632999.

V. Febrian, M. R. Ramadhan, M. Faisal, and A. Saifudin, “Pengujian pada Aplikasi Penggajian Pegawai dengan menggunakan Metode Blackbox,” Jurnal Informatika Universitas Pamulang, vol. 5, no. 1, p. 61, 2020, doi: 10.32493/informatika.v5i1.4340.

Y. Ramesh and K. K. Reddi, “RK algorithm: stochastic parallel methodology for symmetric key cryptography,” Aptikom Journal on Computer Science and Information Technologies, vol. 2, no. 3, pp. 137–144, 2017.

M. Faid, M. Jasri, and T. Rahmawati, “Perbandingan Kinerja Tool Data Mining Weka dan Rapidminer Dalam Algoritma Klasifikasi. Teknika, 8 (1), 11–16.” 2019.

M. A. Rahman, N. Hidayat, and A. A. Supianto, “Komparasi Metode Data Mining K-Nearest Neighbor Dengan Naïve Bayes Untuk Klasifikasi Kualitas Air Bersih (Studi Kasus PDAM Tirta Kencana Kabupaten Jombang),” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer e-ISSN, vol. 2548, p. 964X, 2018.

U. Rahardja, “Penerapan Teknologi Blockchain Dalam Pendidikan Kooperatif Berbasis E-Portfolio,” Technomedia Journal, vol. 7, no. 3, pp. 354–363, 2023.

D. Visa Sofia, “Confusion Matrix-based Feature Selection Sofia Visa,” ConfusionMatrix-based Feature Selection Sofia, vol. 710, no. January, p. 8, 2011.

S. Masripah and L. Ramayanti, “Penerapan Pengujian Alpha Dan Beta Pada Aplikasi Penerimaan Siswa Baru,” Swabumi, vol. 8, no. 1, pp. 100–105, 2020, doi: 10.31294/swabumi.v8i1.7448.

C.-M. Chen and M.-C. Chen, “Mobile formative assessment tool based on data mining techniques for supporting web-based learning,” Comput Educ, vol. 52, no. 1, pp. 256–273, Jan. 2009, doi: 10.1016/j.compedu.2008.08.005.

D. Susilawati and D. Riana, “Optimization the Naive Bayes Classifier Method to diagnose diabetes Mellitus,” IAIC Transactions on Sustainable Digital Innovation, vol. 1, no. 1, pp. 78–86, 2019.