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All Journal Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Virginia Khoirunnisa
Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika
Computer Science & IT Languange, Linguistic, Communication & Media Library & Information Science

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IMPLEMENTASI KLASIFIKASI KEHAMILAN BERESIKO DENGAN METODE NAIVE BAYES PADA PUSKESMAS KELURAHAN MALAKA JAYA Virginia Khoirunnisa; Sri Lestari
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 4 No. 3 (2023): Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) AMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v4i3.396

Abstract

Pregnancy risk is classified into three categories: Low Risk Pregnancy (KRR), High Risk Pregnancy (KRT), and Very High Risk Pregnancy (KRST). Examination data recorded in the Maternal and Child Health (MCH) book at Puskesmas is used to categorize pregnancy risk. Although the maternal mortality rate has decreased, recent surveys show a significant increase. The causes include lack of counseling on obstetric and gynecological health, lack of emergency obstetric and neonatal care, and the use of ineffective traditional medicine. To overcome this problem, it is necessary to improve quality maternal health services. Efforts to accelerate the reduction of maternal mortality rate (MMR) include access to appropriate health services, postpartum care, and family planning services. Data processing uses data mining techniques with the Naïve Bayes classification method, which has high accuracy and short execution time. This study aims to develop a Naïve Bayes classification model in early detection of pregnancy risk, so that it can help diagnose pregnant women with low or high risk.
IMPLEMENTASI KLASIFIKASI KEHAMILAN BERESIKO DENGAN METODE NAIVE BAYES PADA PUSKESMAS KELURAHAN MALAKA JAYA Virginia Khoirunnisa; Sri Lestari
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 4 No. 3 (2023): Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v4i3.396

Abstract

Pregnancy risk is classified into three categories: Low Risk Pregnancy (KRR), High Risk Pregnancy (KRT), and Very High Risk Pregnancy (KRST). Examination data recorded in the Maternal and Child Health (MCH) book at Puskesmas is used to categorize pregnancy risk. Although the maternal mortality rate has decreased, recent surveys show a significant increase. The causes include lack of counseling on obstetric and gynecological health, lack of emergency obstetric and neonatal care, and the use of ineffective traditional medicine. To overcome this problem, it is necessary to improve quality maternal health services. Efforts to accelerate the reduction of maternal mortality rate (MMR) include access to appropriate health services, postpartum care, and family planning services. Data processing uses data mining techniques with the Naïve Bayes classification method, which has high accuracy and short execution time. This study aims to develop a Naïve Bayes classification model in early detection of pregnancy risk, so that it can help diagnose pregnant women with low or high risk.
Co-Authors Sri Lestari