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All Journal Journal of Public Health Science (JoPHS) Smatika Jurnal : STIKI Informatika Jurnal
sulistiyah
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Computer Science & IT Health Professions Medicine & Pharmacology Nursing Public Health Veterinary

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THE INFLUENCE OF FAMILY SUPPORT ON THE PREVENTION OF EARLY MARRIAGE IN DONOMULYO DISTRICT, MALANG REGENCY Ayu Sherlita Sari; Sulistiyah
Journal of Public Health Science Vol. 3 No. 2 (2026): Juni
Publisher : Yayasan Nuraini Ibrahim Mandiri

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Abstract

Early marriage, which occurs during adolescence before reaching the recommended age for marriage, remains a significant social issue in Indonesia because of its adverse effects on adolescents' health, education, and social well-being. Family support is considered one of the key factors influencing efforts to prevent early marriage. This study aimed to examine the effect of family support on the prevention of early marriage among adolescents in Donomulyo District, Malang Regency. A quantitative correlational study with a cross-sectional design was conducted involving 35 adolescents selected through total sampling. Data were collected using validated family support and early marriage prevention questionnaires and analyzed using simple linear regression with IBM SPSS Statistics. The findings indicated that both family support and early marriage prevention were predominantly at a moderate level. Regression analysis demonstrated a very strong positive relationship between family support and early marriage prevention (R = 0.995), with family support explaining 98.9% of the variance in early marriage prevention (R² = 0.989). The regression model was statistically significant (F = 2998.831, p < 0.001), and family support had a significant positive effect on early marriage prevention (β = 0.995; B = 0.991; t = 54.762; p < 0.001). These findings indicate that stronger family support is associated with greater adolescent readiness to delay marriage through improved awareness of education, reproductive health, and psychosocial maturity. Therefore, family-centered interventions that strengthen parental communication, emotional support, and reproductive health education should be prioritized as practical strategies to reduce the risk of early marriage among adolescents.
THE EFFECT OF PREGNANCY YOGA ON THE SLEEP QUALITY OF PREGNANT WOMEN IN THE THIRD TRIMESTER AT THE MULYOASRI VILLAGE HEALTH CENTER Meti Winarsih; Sulistiyah
Journal of Public Health Science Vol. 3 No. 2 (2026): Juni
Publisher : Yayasan Nuraini Ibrahim Mandiri

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Abstract

During the third trimester of pregnancy, various physiological and psychological changes occur that can lead to poor sleep quality, such as back pain, increased nocturnal urination, and anxiety before childbirth. These conditions may negatively affect both maternal and fetal health if not properly managed. One of the recommended non-pharmacological interventions is prenatal yoga. This study aimed to determine the effect of prenatal yoga on sleep quality among third trimester pregnant women in the working area of Mulyoasri Village Auxiliary Health Center. This study used a quasi-experimental design with a Non-Equivalent Control Group Design. A total of 30 respondents were selected using purposive sampling and divided into two groups: 15 in the intervention group and 15 in the control group. Sleep quality was measured using the Pittsburgh Sleep Quality Index (PSQI). Data were analyzed using the Paired Sample t-test. The results showed that all respondents in the intervention group (100%) had poor sleep quality during the pretest. After receiving prenatal yoga intervention, 53.3% of respondents improved to good sleep quality. Statistical analysis showed a p-value of <0.001, indicating a significant effect. Meanwhile, the control group showed no significant change (p=0.670). In conclusion, prenatal yoga is an effective non-pharmacological intervention to improve sleep quality in third trimester pregnant women through both physiological and psychological relaxation mechanisms.
Comparative Analysis of Transfer Learning-Based Deep Learning Models for Jatropha Leaf Disease Classification Agustiani, Sarifah; Sulistiyah; Junaidi, Agus; Ika Agustyaningrum, Cucu; Tajul Arifin, Yoseph
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 16 No 02 (2026): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v16i02.2325

Abstract

Plant disease identification is essential for enhancing agricultural productivity and promoting sustainable crop management practices. Jatropha curcas has considerable potential as a biofuel-producing plant; however, its growth and productivity can be significantly affected by various leaf diseases. Conventional disease diagnosis often requires substantial time and relies heavily on expert knowledge, creating a need for automated solutions based on deep learning techniques. Although deep learning has been widely applied in plant disease recognition, comparative studies focusing on transfer learning models for Jatropha leaf disease classification remain limited, particularly for datasets characterized by distinctive visual features and relatively small sample sizes. This research conducts a comparative assessment of several deep learning architectures to determine the most effective model for classifying Jatropha leaf diseases. The evaluated architectures include MobileNetV2, EfficientNetB0, ResNet50, DenseNet121, and VGG16. All models utilized ImageNet pre-trained weights and were adapted through fine-tuning of the final classification layers to accommodate a dataset containing healthy and diseased Jatropha leaf images. Experimental findings reveal that ResNet50 achieved the highest classification accuracy of 93.81%, followed by VGG16 at 93.58% and EfficientNetB0 at 90.49%. In comparison, DenseNet121 and MobileNetV2 attained accuracies of 85.40% and 74.56%, respectively. Model effectiveness was assessed using accuracy, training duration, confusion matrix analysis, and ROC curve evaluation to examine classification capability across categories. The results demonstrate that ResNet50 offers the most balanced combination of predictive accuracy and performance stability. Overall, the study confirms that transfer learning-based deep learning models are highly effective for Jatropha leaf disease classification, with ResNet50 emerging as the most suitable architecture among those investigated. These findings may serve as a valuable reference for the development of reliable and efficient plant disease detection systems in agricultural environments.
Co-Authors Agus Junaidi Agustiani, Sarifah Ayu Sherlita Sari Cucu Ika Agustyaningrum Meti Winarsih Yoseph Tajul Arifin