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Stunting occurs due to malnutrition which inhibits growth in toddlers. Stunting can also be caused by problems during pregnancy. This study aims to identify the risk of stunting during pregnancy and determine pregnant women who are at risk of this condition. By identifying and prioritizing critical factors that contribute to stunting in children under five, this research is expected to assist policy makers in developing effective solutions to reduce stunting rates. Handling the problem of stunting is important for the Government because it relates to the future generation of Golden Indonesia 2045. This study evaluates appropriate actions or therapies to reduce the risk of having children born with the potential to experience stunting. In the process of selecting pregnant women who are at risk of giving birth to children with the risk of stunting, a selection procedure is carried out that considers several factors such as the mother's age, mother's nutritional intake, arm circumference, hemoglobin level, parity, birth spacing, height, and mother's body mass index (BMI). The analytic network process (ANP) approach is used to determine the outcome of the selection process. The ranking is determined based on the calculation of the weighting of the criteria and sub-criteria in the ANP method. Based on the results of calculations using the ANP approach, PM 1 pregnant women get the highest score and are ranked first. These pregnant women are considered to have the highest risk of giving birth to babies with stunting risk.

Childfree terjadi pada individu atau pasangan yang secara sukarela memilih untuk tidak memiliki anak. Fenomena Childfree semakin menarik perhatian masyarakat modern dan mempengaruhi keputusan hidup banyak individu. Data yang digunakan diperoleh dengan melakukan crawling data pada sosial media twitter. Dalam analisis sentimen kali ini menggunakan metode naïve bayes, data akan diflasifikasikan menjadi dua hasil, yaitu sentimen negatif dan positif. Setelah itu, data tersebut akan dinilai menggunakan pengujian dengan confusion matrix untuk mengukur akurasi, recall, dan precision. Hasil pengujian dengan Rapidminer menunjukan tingkat akurasi naive bayes yang tinggi sebesar 96%, untuk sentimen positif menghasilkan nilai precision 84,13%, dan nilai recall 100%. Sedangkan untuk sentimen negatif menunjukkan nilai precision 94,92%, dan recall 100%. Hasil klasifikasi tersebut menghasilkan 254 tweet negatif, dan 74 tweet positif. Analisis sentimen menggunakan metode Naïve Bayes dapat memberikan wawasan yang berharga tentang pandangan dan sentimen masyarakat terhadap Childfree. Hasil penelitian ini penting untuk memahami dan menghargai keragaman pandangan terkait topik ini. Implikasi penelitian ini dapat digunakan untuk pengambilan keputusan yang lebih baik dalam konteks sosial, budaya, dan kebijakan yang berkaitan dengan keputusan Childfree.