Bayu Laksamana Jati
Sekolah Tinggi Ilmu Kesehatan Abdi Nusantara

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Penerimaan Ibu Hamil Terhadap Mitos Kehamilan Di Lingkungan Desa Pancawati Bayu Laksamana Jati; Budi Ermanto; Yusniar; Rahayu Khairiah
Journal of Innovative and Creativity Vol. 5 No. 3 (2025)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joecy.v5i3.3769

Abstract

Desa Pancawati kabupaten Karawang telah mengalami perubahan secara fisik, karena sudah mulai banyaknya developer perumahan yang membangun dan mengembangkan perumahan di sekitar wilayah desa tersebut, sehingga diperlukan kajian mengenai penerimaan oleh ibu hamil terhadap mitos yang berkembang di lingkungan tersebut, apakah dengan karakteristik ibu hamil yang berbeda-beda, dengan pergeseran nilai budaya di desa sebagai pengaruh dari perubahan fisik desa menjadi desa yang modern berdampak pada persepsi dan interpretasi ibu hamil mengenai mitos tersebut. Tujuan: untuk mengeksplorasi hubungan kepercayaan terhadap mitos kehamilan dan interpretasinya oleh ibu hamil di lingkungan desa pancawati Karawang , apa yang menjadi penyebab adanya hubungan tersebut dan faktor-faktor pendukung lainnya. Metode: Penelitian ini merupakan penelitian deskriptif kualitatif, melalui pendekatan indepth interview, dengan informan berjumlah 5 orang. Data hasil wawancara dianalisis dengan analisis kualitatif. Hasil; penelitian ini menunjukkan bahwa ibu hamil yang tinggal dilingkungan desa pancawati, masih mengikuti kebiasaan yang harus dilakukan ibu pada saat hamil dan juga pantangan/larangan yang harus dihidari oleh ibu hamil, dengan keyakinan jika pantangan itu dilanggar akan mengakibatkan hal buruk pada ibu dan bayi yang dikandugnya. Masyarakat desa pancawati juga masih mempertahankankan adat istiadat yang berkaitan dengan mitos kehamilan, sehingga lingkungan domisili tempat tinggal cukup berpengaruh untuk mempertahankan eksistensi mitos kehamilan tersebut. Selain lingkungan ada beberapa faktor lain yang cukup memiliki hubungan dalam penerimaan ibu hamil terhadap mitos, antara lain; karakteristik informan, pengetahuan seputar mitos kehamilan, peran pengaruh orangtua, minimnya pengetahuan tentang konsep kehamilan yang sehat, latar belakang budaya dan pengalaman.
Effectiveness of Artificial Intelligence-Based Personalized Antenatal Care on Early Detection of Pregnancy Complication Risks and Antenatal Care Compliance Bayu Laksamana Jati; Udur Diana Tumanggor; Bambang Sugiharto
Journal of Innovative and Creativity (Joecy) Vol. 6 No. 2 (2026)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joecy.v6i2.14046

Abstract

Antenatal care (ANC) is an essential component of maternal healthcare because timely identification of pregnancy complications and appropriate follow-up can reduce preventable maternal and perinatal morbidity. Conventional ANC generally applies standardized assessment and follow-up schedules, which may not fully accommodate the dynamic and heterogeneous risk profiles of pregnant women. Artificial intelligence (AI)-based personalized ANC may facilitate individualized risk stratification while simultaneously improving patient engagement and adherence to ANC.This study aimed to evaluate the effectiveness of an AI-based personalized ANC approach in improving early detection of pregnancy complication risks and ANC compliance compared with standard ANC.A quantitative quasi-experimental pretest-posttest control group design was used. The simulated dataset consisted of 120 pregnant women, with 60 participants assigned to an intervention group and 60 to a control group. The intervention consisted of standard ANC supplemented by AI-supported individualized risk screening, personalized health education, and reminder-based follow-up. The control group received standard ANC. The primary outcomes were early detection of pregnancy complication risks and ANC compliance. Categorical outcomes were analyzed using the Chi-square test and effect estimates were expressed as odds ratios (ORs).In the simulated dataset, early detection of pregnancy complication risks was achieved in 48 participants (80.0%) in the intervention group compared with 33 participants (55.0%) in the control group. The intervention was associated with higher odds of early detection (OR = 3.27). ANC compliance was observed in 51 participants (85.0%) in the intervention group and 36 participants (60.0%) in the control group, corresponding to an OR of 3.78. Both outcomes demonstrated statistically significant between-group differences in the simulated analysis. The simulated findings suggest that AI-based personalized ANC may improve early identification of pregnancy complication risks and ANC compliance. AI may therefore serve as a complementary decision-support and patient-engagement technology within maternal healthcare. However, these numerical findings are synthetic and must not be interpreted or submitted as empirical findings from actual participants.