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All Journal Journal La Medihealtico Journal La Multiapp
St Surya Indah Nurdin
Midwifery Study Program, Muhammadiyah University Of Gorontalo, Indonesia
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Engineering Health Professions Medicine & Pharmacology Nursing Public Health Veterinary

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Midwife and Shaman Partnership Relationship with MCH Service Coverage in the Work Area of the West Limboto Health Center Fifi Ishak; St Surya Indah Nurdin; Zulaika F Asikin
Journal La Medihealtico Vol. 3 No. 1 (2022): Journal La Medihealtico
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamedihealtico.v3i1.613

Abstract

The cooperation between midwives and traditional birth attendants is referred to as a partnership between midwives and traditional birth attendants. In order to promote the health of mothers and newborns, it must be mutually advantageous for all parties and built on the principles of openness, equality, and mutual confidence. The purpose of this research is to evaluate the collaboration interaction between midwives and traditional delivery attendants in the context of the enhancement of maternal and infant health services at West Limboto Health Center in Uganda, Africa. In this study, a cross-sectional research strategy using the Chi Square test was employed. The sampling approach used in this research was incidental sampling, which is to say, the technique of determining samples was used. The findings indicated that the Chi Square test was 0.005 or the p value was less than 0.005, which led to the conclusion that there was a link between midwives and traditional delivery attendants in order to promote the health of mothers and newborns.
Stunting Classification in Children's Measurement Data Using Machine Learning Models Syahrial Syahrial; Rosmin Ilham; Zulaika F Asikin; St. Surya Indah Nurdin
Journal La Multiapp Vol. 3 No. 2 (2022): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v3i2.614

Abstract

The study conducted a stunting classification of measurement data for children under 5 years old. The dataset has attributes such as: gender, age, weight (BB), height (TB), weight / height (BBTB), weight / age (BBU), and height / age (TBU). The research uses the CRISP-DM methodology in processing the data. The data were tested on several classification models, namely: logistic regression (LR), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbor (KNN), classification and regression trees (CART), nave bayes (NB), support vector machine - linear kernel (SVM-Linear), support vector machine - rbf kernel (SVM-RBF), random forest classifier (RPC), adaboost (ADA), and neural network (MLPC). These models were tested on the dataset to find out the best model in accuracy. The test results show that SVM-RBF produces an accuracy of 78%. SVM-RBF has consistently been at the highest accuracy in several tests. Testing through k-fold cross validation with k=10.
Co-Authors Fifi Ishak Rosmin Ilham Syahrial Syahrial Zulaika F Asikin Zulaika F Asikin