Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal Indonesian Journal of Data and Science Kebijakan: Jurnal Ilmu Administrasi
Putu Surya Wedra Lesmana
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Social Sciences

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

Found 2 Documents
Search

 1 
TANTANGAN PENCEGAHAN PERKAWINAN ANAK MELALUI IMPLEMENTASI UNDANG-UNDANG NOMOR 16 TAHUN 2019 DI INDONESIA Dewi Mahayogi, Ni Putu Tirta; Rahayu, Luh Riniti; Sulandari, Sri; Putu Surya Wedra Lesmana
Kebijakan : Jurnal Ilmu Administrasi Vol. 16 No. 01 (2025): Volume 16 No. 1 Januari 2025
Publisher : Program Magister Ilmu Administrasi dan Kebijakan Publik, Pascasarjana, Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/kebijakan.v16i01.21866

Abstract

Penelitian ini bertujuan untuk menelaah apa saja faktor-faktor penyebab dari perkawinan anak dan tantangan Tantangan Implementasi Undang-Undang Nomor 16 Tahun 2019, dengan fokus pada kesenjangan antara kebijakan dan praktik di lapangan. Menurut data UNICEF 2023, lebih dari 25,53 juta anak perempuan yang dinikahkan di bawah umur, Indonesia menempati peringkat ke-4 di dunia. Perkawinan anak merupakan masalah serius di Indonesia yang melanggar hak-hak dasar anak dan berdampak negatif pada kesehatan, pendidikan, dan kesejahteraan mereka. Penelitian ini menggunakan metode pendekatan kualitatif karena data yang dihasilkan berupa kata atau deskripsi. Melalui studi literatur, penelitian ini mengidentifikasi faktor-faktor penyebab pernikahan anak, seperti siklus kemiskinan, dampak sosial, dampak kesehatan, dampak psikologis, serta faktor sosial, ekonomi, dan religius. Tantangan Implementasi Undang-Undang Nomor 16 Tahun 2019 seperti adanya dispensasi Nikah. Terdapat kesenjangan antara kebijakan pusat dan kondisi di daerah, seperti perbedaan budaya, adat istiadat, dan tingkat pemahaman masyarakat tentang hukum. Banyak anak dan remaja yang tidak mengetahui undang-undang tentang perkawinan, dan peran sekolah dalam sosialisasi tentang undang-undang ini masih lemah.
Comparison of ResNet-50 and DenseNet-121 Architectures in Classifying Diabetic Retinopathy Yoga Pramana Putra, I Putu Gede; Ni Wayan Jeri Kusuma Dewi; Putu Surya Wedra Lesmana; I Gede Totok Suryawan; Putu Satria Udyana Putra
Indonesian Journal of Data and Science Vol. 6 No. 1 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i1.232

Abstract

Introduction: Diabetic Retinopathy (DR) is a vision-threatening complication of diabetes that requires early and accurate diagnosis. Deep learning offers promising solutions for automating DR classification from retinal images. This study compares the performance of two convolutional neural network (CNN) architectures—ResNet-50 and DenseNet-121—for classifying DR severity levels. Methods: A dataset of 2,000 pre-processed and augmented retinal images was used, categorized into four classes: normal, mild, moderate, and severe. Both models were trained using two approaches: standard train-test split and Stratified K-Fold Cross Validation (k=5). Data augmentation techniques such as flipping, rotation, zooming, and translation were applied to enhance model generalization. The models were trained using the Adam optimizer with a learning rate of 0.001, dropout of 0.2, and learning rate adjustment via ReduceLROnPlateau. Performance was evaluated using accuracy, precision, recall, and F1-score. Results: ResNet-50 outperformed DenseNet-121 across all evaluation metrics. Without K-Fold, ResNet-50 achieved 84% accuracy compared to DenseNet-121’s 80%; with K-Fold, ResNet-50 scored 83% and DenseNet-121 81%. ResNet-50 also demonstrated better balance in class-wise classification, with higher recall and F1-score, especially for moderate and severe DR classes. Confusion matrices confirmed fewer misclassifications with ResNet-50. Conclusions: ResNet-50 provides superior accuracy and robustness in classifying DR severity levels compared to DenseNet-121. While K-Fold Cross Validation enhances model stability, it slightly reduces overall accuracy. These findings support the use of ResNet-50 in developing reliable deep learning-based screening tools for early DR detection in clinical practice
Co-Authors Dewi Mahayogi, Ni Putu Tirta I Gede Totok Suryawan Luh Riniti Rahayu Ni Wayan Jeri Kusuma Dewi Putra, Putu Satria Udyana SRI SULANDARI Yoga Pramana Putra, I Putu Gede