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All Journal Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Nursing Genius Journal
Aldin , Reimon
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Computer Science & IT Decision Sciences, Operations Research & Management Health Professions Nursing Public Health

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The Impact of Social Support and Health Literacy on CERDIK Practices Among Type 2 Diabetics in Rural Indonesia Narmawan, Narmawan; Seri Yuliana, Hartalia; Aldin , Reimon; Narmi, Narmi
Nursing Genius Journal Vol. 2 No. 3 (2025): Nursing Genius Journal Vol. 2 No. 3 July 2025
Publisher : PT. Nursing Genius Care

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Background: The CERDIK program (Check, Eat, Regular exercise, Drugs, Insulin injection, and Control blood glucose) is a key self‑management strategy for Type 2 Diabetes Mellitus (T2DM). However, patient adherence remains suboptimal in many primary health care settings.Objective: To identify family support, diabetes knowledge, and motivation as factors associated with CERDIK behavior among T2DM patients at Puskesmas Moramo.Methods: A cross‑sectional study was conducted in May 2024 among 42 T2DM patients attending Puskesmas Moramo. Participants were recruited by consecutive sampling. Data were collected using validated questionnaires for family support, diabetes knowledge, motivation, and CERDIK behavior (Cronbach’s α = 0.82–0.90). Spearman’s rank correlation was performed in SPSS v26 with α = 0.05.Results: The mean age was 57.3 ± 8.5 years; 58.3% were female. Good CERDIK behavior was observed in 24 (57.1%) patients. Family support was moderately correlated with CERDIK behavior (ρ = 0.483; p = 0.001). Diabetes knowledge (ρ = 0.395; p = 0.008) and motivation (ρ = 0.317; p = 0.035) also showed significant positive correlations with CERDIK behavior. Conclusion: Family support, diabetes knowledge, and motivation are significant factors influencing CERDIK behavior in T2DM patients at Puskesmas Moramo. Interventions should integrate family involvement, targeted education, and motivational enhancement to improve self‑management and glycemic outcomes.
Implementasi Convolutional Neural Network Pada Deteksi Penyakit Retina Menggunakan Citra Fundus Mata Aldin, Reimon
JURNAL FASILKOM Vol. 15 No. 2 (2025): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

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This study aims to develop an efficient system for detecting retinal diseases from fundus eye images by applying Deep Learning with Convolutional Neural Networks (CNN). The proposed model is designed to assist ophthalmologists in making accurate and timely diagnoses, enabling appropriate treatment and improving patient outcomes. The research emphasizes the role of CNN-based Deep Learning as a reliable method for classifying retinal disorders. A quantitative approach was employed, utilizing numerical and descriptive data such as images, observations, and secondary sources. The research procedure covered several stages: image preprocessing, CNN model design, training, validation, evaluation, and system testing. The experimental results demonstrated that the developed system achieved an accuracy of 97%. Evaluation metrics confirmed high performance with classification results as follows: Myopia (precision 1.00, recall 1.00, f1-score 1.00), Cataract (precision 0.88, recall 1.00, f1-score 0.93), Diabetic Retinopathy (precision 1.00, recall 1.00, f1-score 1.00), and Glaucoma (precision 1.00, recall 0.95, f1-score 0.97). These findings show that the CNN architecture with VGG16 demonstrates excellent capability in detecting and classifying retinal diseases using fundus images. Therefore, the model can be recommended as a practical tool for early detection of retinal disorders, particularly within the context of healthcare services in Kendari City, Southeast Sulawesi.
Co-Authors Narmawan, Narmawan Narmi, Narmi Seri Yuliana, Hartalia