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Lembaga Penelitian Pengabdian Masyarakat Universitas Nusa Mandiri
Ciptaan disebarluaskan di bawah Lisensi Creative Commons Atribusi-NonKomersial 4.0 Internasional.
PENERAPAN FEATURE WEIGHTING OPTIMIZED PADA NAÏVE BAYES UNTUK PREDIKSI PROSES PERSALINAN
Keywords:
FEATURE WEIGHTING OPTIMIZED, NAÏVE BAYES, PREDIKSI PROSES PERSALINAN
Abstract
Birth of a baby is something that is very desirable for every married couple. All parties expect safety for mothers and babies who have just been born. Medical personnel make various efforts to help the delivery process run smoothly and the mother and baby survive. But in the labor process not all the baby's birth process runs smoothly. Problems often occur during labor. There are several obstacles so that there is a risk of labor, namely maternal and infant mortality. Every mother wants to be able to give birth to a baby normally, but due to medical reasons the delivery process is done by cesarean. The act of choosing a type of delivery faster can affect the safety of the mother and baby. The selection of the cesarean method is carried out late so it will increase the risk of maternal and infant mortality. For this reason, it is necessary to conduct research by using labor delivery data so that they can choose the right type of labor. In this study the classification of maternity labor will be carried out with data mining methods, namely Naive Bayes, which are improved by using the Optimize Weight (PSO) method. Naive Bayes was able to produce a high accuracy value for processing labor data for mothers, namely 94%. The final results of this study obtained the value of naïve bayes performance that can be improved by the Optimize Weights (PSO) method to be better at 98%
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References
Amalia;Eviciena, H. (2017a). Aplikasi Sistem Penunjang Keputusan Untuk Prediksi Persalinan Sesar. Jurnal Ilmu Pengetahuan Dan Teknologi Komputer, 3(1), 121–126.
Amalia;Eviciena, H. (2017b). Komparasi Metode Data Mining Untuk Penentuan Proses Persalinan Ibu Melahirkan. Jurnal Sistem Informasi, 13(2), 103. https://doi.org/10.21609/jsi.v13i2.545
Amalia, H. (2015). Penerapan Metode Neural Network Berbasis Particle Swam. Paradigma, XVII(1), 1–8.
Amalia, H., Pohan, A., & Masripah, S. (2019). Penelitian 2019.
Amalia, Hi., & Evicienna. (2017). Penentuan Proses Persalinan Ibu Melahirkan Menggunakan Algoritma c4.5. In Seminar Nasional Cendekiawan (Vol. 3, pp. 101–107). Retrieved from http://ir.obihiro.ac.jp/dspace/handle/10322/3933
Deressa, T. D., & Kadam, K. (2018). Prediction of Fetal Health State during Pregnancy : A Survey, 6(1), 29–36.
Frieyadie, & Aryanti, H. (2013). SISTEM PAKAR DIAGNOSA GANGGUAN KEHAMILAN BERBASIS WEB DENGAN MENGGUNAKAN METODE FORWARD CHAINING PADA RSIA RP SOEROSO. Pilar Nusa Mandiri, 9(1), 62–68.
Kamat, A., Oswal, V., & Datar, M. (2015). Implementation of Classification Algorithms to Predict Mode of Delivery, 6(5), 4531–4534.
Kementerian Kesehatan Republik Indonesia. (2017). Profile Kesehatan Indonesia Tahun 2016. (R. Kurniawan, Yudianto, B. Hardhana, & T. A. Soenardi, Eds.). Jakarta: Kementerian Kesehatan Republik Indonesia. Retrieved from http://www.depkes.go.id/resources/download/pusdatin/profil-kesehatan-indonesia/Profil-Kesehatan-Indonesia-2016.pdf
Lakshmi, K., Priya, P. R., & Nivedhitha, T. (2017). Naive Bayesian Model for Predicting Mode of Delivery, 2(4), 562–565.
Mary, R. C. S., & Kumar, B. S. (2018). Comparison of Various Data Mining Algorithms in the Prediction of Risk for Gestational Diabetes. International Journal of Advanced Research in Computer Science and Software Engineering, 7(8), 74. https://doi.org/10.23956/ijarcsse.v7i8.26
Masripah, S. (2019). Laporan Akhir Penelitian Mandiri. Jakarta.
Pereira, S., Portela, F., Santos, M. F., Machado, J., & Abelha, A. (2015). Predicting Type of Delivery by Identification of Obstetric Risk Factors through Data Mining. Procedia Computer Science, 64, 601–609. https://doi.org/10.1016/j.procs.2015.08.573
Amalia;Eviciena, H. (2017b). Komparasi Metode Data Mining Untuk Penentuan Proses Persalinan Ibu Melahirkan. Jurnal Sistem Informasi, 13(2), 103. https://doi.org/10.21609/jsi.v13i2.545
Amalia, H. (2015). Penerapan Metode Neural Network Berbasis Particle Swam. Paradigma, XVII(1), 1–8.
Amalia, H., Pohan, A., & Masripah, S. (2019). Penelitian 2019.
Amalia, Hi., & Evicienna. (2017). Penentuan Proses Persalinan Ibu Melahirkan Menggunakan Algoritma c4.5. In Seminar Nasional Cendekiawan (Vol. 3, pp. 101–107). Retrieved from http://ir.obihiro.ac.jp/dspace/handle/10322/3933
Deressa, T. D., & Kadam, K. (2018). Prediction of Fetal Health State during Pregnancy : A Survey, 6(1), 29–36.
Frieyadie, & Aryanti, H. (2013). SISTEM PAKAR DIAGNOSA GANGGUAN KEHAMILAN BERBASIS WEB DENGAN MENGGUNAKAN METODE FORWARD CHAINING PADA RSIA RP SOEROSO. Pilar Nusa Mandiri, 9(1), 62–68.
Kamat, A., Oswal, V., & Datar, M. (2015). Implementation of Classification Algorithms to Predict Mode of Delivery, 6(5), 4531–4534.
Kementerian Kesehatan Republik Indonesia. (2017). Profile Kesehatan Indonesia Tahun 2016. (R. Kurniawan, Yudianto, B. Hardhana, & T. A. Soenardi, Eds.). Jakarta: Kementerian Kesehatan Republik Indonesia. Retrieved from http://www.depkes.go.id/resources/download/pusdatin/profil-kesehatan-indonesia/Profil-Kesehatan-Indonesia-2016.pdf
Lakshmi, K., Priya, P. R., & Nivedhitha, T. (2017). Naive Bayesian Model for Predicting Mode of Delivery, 2(4), 562–565.
Mary, R. C. S., & Kumar, B. S. (2018). Comparison of Various Data Mining Algorithms in the Prediction of Risk for Gestational Diabetes. International Journal of Advanced Research in Computer Science and Software Engineering, 7(8), 74. https://doi.org/10.23956/ijarcsse.v7i8.26
Masripah, S. (2019). Laporan Akhir Penelitian Mandiri. Jakarta.
Pereira, S., Portela, F., Santos, M. F., Machado, J., & Abelha, A. (2015). Predicting Type of Delivery by Identification of Obstetric Risk Factors through Data Mining. Procedia Computer Science, 64, 601–609. https://doi.org/10.1016/j.procs.2015.08.573
Published
2019-03-07
How to Cite
Amalia, H., Pohan, A., & Masripah, S. (2019). PENERAPAN FEATURE WEIGHTING OPTIMIZED PADA NAÏVE BAYES UNTUK PREDIKSI PROSES PERSALINAN. Jurnal Pilar Nusa Mandiri, 15(1), 15-20. https://doi.org/10.33480/pilar.v15i1.3
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