Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Indonesian Journal of Artificial Intelligence and Data Mining SMART ANKes
Nita, Yulia
Unknown Affiliation
Computer Science & IT Education

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
HUBUNGAN USIA REPRODUKSI, JUMLAH ANAK, DAN RIWAYAT KONTRASEPSI PENGGUNAAN KONTRASEPSI SUNTIK DI PUSKESMAS PEDAMARAN TIMUR KECAMATAN PEDAMARAN TIMUR KABUPATEN OKI Nita, Yulia
JURNAL SMART ANKES Vol 3 No 1 (2019): Jurnal Ilmiah Kesehatan Masyarakat STIKES Abdi Nusa Pangkalpinang
Publisher : Program Studi Ilmu Kesehatan STIKES  Abdi Nusa Pangkalpinang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Injectable contraceptives are effective and practical to use and are affordable and safe. The purpose of this study was to determine the relationship between reproductive age, number of children, and history of use of injection contraceptives at the Pedamaran Timur OKI Health Center, Pedamaran District, OKI Regency in 2019. This study used an analytical survey method with a cross sectional approach. The number of samples used was 306 samples. The data used in this research is secondary data. According to the results of the chi-square statistical test of reproductive age, the p-value was 0.001, the number of children was Pvalue 0.005, the history of contraceptive use was Pvalue 0.000, so it could be concluded that there was a significant relationship between childbearing age, the number of children and history of contraceptive use with the use of injection contraceptives at the Puskesmas Pedamaran Timur District of East Pedamaran District of OKI Regency in 2019. It is hoped that this research can be a means of providing counseling and counseling about contraception.
Early Detection of Hepatitis Disease Using Machine Learning Algorithms Sister, Maya Gian; Nita, Yulia; Solichin, Achmad
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 3 (2025): November 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i3.38084

Abstract

Hepatitis is an inflammation of the liver caused by viral infections, autoimmune disorders, or exposure to toxic substances. Hepatitis B and C are major public health concerns because they may progress to cirrhosis or liver cancer. In Indonesia, the transmission rate remains high, primarily through blood contact, unsterile needles, transfusions, and maternal delivery. Limited public awareness, coupled with the often asymptomatic nature of hepatitis, leads to delayed detection, which increases the risk of severe complications and mortality. Therefore, early detection is crucial to minimizing the disease burden.This study proposes a risk prediction model for hepatitis using non-laboratory clinical data and machine learning methods. Eight classification algorithms were compared, Naïve Bayes, K-Nearest Neighbor (K-NN), Random Forest, Support Vector Machine (SVM), Decision Tree, AdaBoost, XGBoost, CatBoost, and LightGBM. Model performance was evaluated through K-fold cross-validation using accuracy, precision, recall, F1-score, and AUC. The results show that the SVM with a linear kernel achieved the highest performance, with 87% accuracy and balanced F1-scores across all classes. The model successfully classified four categories, Acute Hepatitis, Chronic Hepatitis, Liver Abscess, and Parasitic/Viral Infections. These findings highlight the potential of machine learning to improve early detection of hepatitis effectively and efficiently.
Co-Authors Achmad Solichin Sister, Maya Gian