Kehamilan tidak diinginkan di Indonesia belum menunjukkan perubahan yang konsisten dari 2002 hingga 2019 (BKKBN, 2019). Dominasi kehamilan tidak diinginkan terjadi pada kelompok usia berisiko tinggi (56% kasus) (BKKBN, 2012, 2017) dan cenderung lebih banyak ditemukan di perkotaan Indonesia. Salah satu faktor yang mempengaruhi terjadinya kehamilan tidak diinginkan yaitu penggunaan kontrasepsi modern. Penelitian ini dilakukan untuk melihat besar hubungan yang terjadi antara penggunaan kontrasepsi modern dengan kejadian kehamilan tidak diinginkan pada wanita kelompok usia berisiko tinggi di wilayah perkotaan dan pedesaan Indonesia. Desain studi pada penelitian ini merupakan cross sectional dengan analisis menggunakan chi square dan regresi logistik. Data yang digunakan merupakan data SDKI 2017. Hasil analisis menunjukkan bahwa wanita usia risiko tinggi di wilayah perkotaan Indonesia yang tidak menggunakan kontrasepsi memiliki risiko yang lebih rendah untuk mengalami kehamilan tidak diinginkan (OR: 0.76; 95% CI: 0.588-0.977). Sedangkan wanita usia risiko tinggi di wilayah pedesaan Indonesia yang tidak menggunakan kontrasepsi memiliki risiko yang lebih tinggi untuk mengalami kehamilan tidak diinginkan (OR: 1.66 95% CI: 1.035-2.648).

Unintended pregnancies in Indonesia have not shown consistent changes from 2002 to 2019 (BKKBN, 2019). In addition, unintended pregnancies mostly occur in the high-risk age group (56% of cases) (BKKBN, 2012, 2017). One of the factor that can influence incident of unintended pregnancy is the use of modern contraception. In Indonesia unintended pregnancies tend to be more common in urban areas. This research was conducted to see the relationship between modern contraception use and the incidence of unintended pregnancies in women in high-risk age groups in urban and rural areas of Indonesia. The study design in this research is cross sectional and data will be conducted with chi square and logistic regression. The data used in this research is the 2017 IDHS. The results show that women of high risk age in urban areas of Indonesia who do not use contraception have a lower risk of experiencing unwanted pregnancy (OR: 0.76; 95% CI: 0.588-0.977). Meanwhile, women of high risk age in rural areas of Indonesia who do not use contraception have a higher risk of experiencing unwanted pregnancy (OR: 1.66 95% CI: 1.035-2.648).

The tuberculosis treatment success rate in Indonesia in 2023 did not reach the 90% target. Treatment success impacts the reduction of infection spread and drug resistance cases, making early prediction of treatment success crucial. This study aims to develop a machine-learning model to predict treatment success. Data from Indonesia's Tuberculosis Information System (SITB) cohort was used. The study included productive-age patients (15-64 years) diagnosed with drug-sensitive tuberculosis who received treatment from January 1, 2020, to December 31, 2023. Data was randomly split into training (80%) and testing (20%) sets for model validation, with cross-validation performed. The algorithms used include decision tree, random forest, multilayer perception, extreme gradient boosting, and logistic regression. A consensus was reached for decision-making variables required in performing machine learning-based modeling of SITB data to predict treatment success using modeling of SITB data to predict treatment success using the Delphi method. The results of the study show that the random forest machine learning algorithm had the best performance and highest accuracy in predicting treatment success. This machine learning–based prediction tool can provide early predictions with SHAP (SHapley Additive ExPlanations) interpretation, helping healthcare workers make informed decisions more easily.