JURNAL RISET INFORMATIKA Vol. 5, No. 1. December 2022 P-ISSN: 2656-1743 |E-ISSN: 2656-1735 DOI: https://doi.org/10.34288/jri.v5i1.487 Accredited rank 3 (SINTA 3), excerpts from the decision of the Minister of RISTEK-BRIN No. 200/M/KPT/2020 93 PREGNANCY RISK LEVEL CLASSIFICATION USING THE CRISP-DM METHOD Reka Dwi Syaputra -1*) , Achmad Solichin -2 Master of Computer Science, Universitas Budi Luhur Jakarta, Indonesia https://www.budiluhur.ac.id/ 1*) 2011600075@student.budiluhur.ac.id, 2 achmad.solichin@budiluhur.ac.id (*) Corresponding Author Abstract Independent midwife practices are tasked with reminding and maintaining the quality of standardized reproductive health services for pregnant women. Independent midwife practices have had patient visits since the covid-19 pandemic from 2020 to 2021, especially at the yetti puranama midwife, which consists of 320 pregnancy examinations, 130 delivery care, and 50 referrals. The covid-19 pandemic has impacted maternal mortality rates because there are still many restrictions on all services. Maternal health services include pregnant women who are routinely unable to go to the puskesmas or other healthcare facilities due to fear of contracting covid-19, which delays the examination of pregnancy gravida, abortion, temperature, pregnancy distance, haemoglobin, blood pressure, ideal weight, and decisions. So that the problem that occurs is an increase in the risk of pregnancy, resulting in death and increased maternal mortality. In solving this problem, the research takes a machine-learning approach. The research aims to build a classification of pregnancy risk levels that can predict early treatment in this study using the random forest method with cross-validation 2. This study obtained the results of an accuracy value of 98%, precision of 94%, and recalled 100% in the random forest method. Keywords: pregnancy risk level, classification, decision tree, random forest. Abstrak Praktik bidan mandiri memiliki tugas mengingatkan dan mempertahankan kualitas pelayanan kesehatan reproduksi terstandar pada ibu hamil. Praktik bidan mandiri memiliki kunjungan pasien sejak pandemi covid- 19 dari tahun 2020 sampai 2021 khususnya di bidan yetti puranama yang terdiri dari pemeriksaan kehamilan 320 orang, asuhan persalinan 130 orang dan rujukan 50 orang. Masa pandemi covid-19 memberikan dampak angka kematian ibu meningkat disebabkan masih banyak pembatasan ke semua layanan. pelayanan kesehatan ibu seperti ibu hamil yang menjadi rutinitas tidak dapat ke puskesmas atau fasilitas pelayanan kesehatan lainnya disebabkan takut tertular covid-19. Sehingga menundakan pemeriksaan kehamilan gravida, abortus, suhu, jarak kehamilan, hemoglobin, tekanan darah, berat badan ideal dan keputusan. Sehingga permasalahan yang terjadinya terdapat peningkatan tingkat risiko kehamilan berdampak kematian dan meningkatnya angka kematian ibu. Dalam memecahkan masalah ini, penelitian melakukan cara pendekatan dengan machine learning. Tujuan penelitian untuk membangun klasifikasi tingkat risiko kehamilan yang dapat memprediksi penanganan secara dini. Pada penelitian ini menggunakan metode random forest dengan cross-validation 2. Penelitian ini mendapatkan hasil nilai akurasi 98%, precision 94% dan recall 100% pada metode random forest. Kata kunci: tingkat risiko kehamilan, klasifikasi, pohon keputusan, random forest. INTRODUCTION From 2007-2020, the coverage of K4 pregnant women's health services tended to increase. However, there was a decrease in 2020, from 88.55% to 84.60%. This decline is assumed to occur due to program implementation in areas affected by the Covid-19 pandemic (Kementerian Kesehatan RI, 2021). Health services for pregnant women (K4) in 2020 in bengkulu province by 87% (Badan Pusat Statistik Provinsi Bengkulu, 2020). Compared to 2021, the coverage of K4 pregnant women's health services tends to fluctuate. In 2021 the K4 rate was 88.8%, which is an increase