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Pengalihan hak asuh anak sebagai instrumen perlindungan konstitusional anak korban kekerasan dalam rumah tangga : Studi putusan PA LAHAT No 685/PDT.G/2022/PA.LT Willy, Willy; Thaher, Irmanjaya
Cessie : Jurnal Ilmiah Hukum Vol. 4 No. 3 (2025): Cessie: Jurnal Ilmiah Hukum
Publisher : ARKA INSTITUTE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55904/cessie.v4i3.1674

Abstract

Secara tradisional, Kompilasi Hukum Islam (KHI) memprioritaskan ibu sebagai pengasuh utama anak di bawah usia 12 tahun (mumayyiz), namun Putusan Nomor 685/Pdt.G/2022/PA.Lt mengalihkan hak asuh anak kepada ayah dengan pertimbangan adanya kekerasan psikis berupa pengabaian kebutuhan dasar anak dan pola pengasuhan yang tidak kondusif dari ibu. Penelitian ini menganalisis pertimbangan hukum hakim dalam pengalihan hak asuh anak pasca perceraian dan penguatan asas “kepentingan terbaik bagi anak” dalam perspektif hukum tata negara sebagai bentuk perlindungan konstitusional terhadap anak korban KDRT melalui metode studi kepustakaan dan analisis literatur. Hasil penelitian menunjukkan Pertimbangan hukum didasarkan pada prinsip kepentingan terbaik bagi anak (UU No. 23/2002 jo UU No. 35/2014), ketidakmampuan ibu dalam mengasuh, dan bukti-bukti KDRT secara psikis. 
Evaluasi Efektivitas Rain Barrel dalam Pengendalian Limpasan Permukaan di Kawasan Perumahan Modern di Kota Bogor Prasetya, Muhamad Demirel; Yudianto, Doddi; Willy, Willy
JURNAL SUMBER DAYA AIR Vol 21, No 2 (2025)
Publisher : Direktorat Bina Teknik Sumber Daya Air, Kementerian Pekerjaan Umum dan Perumahan Rakyat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32679/jsda.v21i2.981

Abstract

Bogor City, with a population of approximately 1,064,000 in 2022 and an annual growth rate of 2.01%, faces increasing pressure to meet housing demands. To accommodate this need, one modern residential development in the area has undergone extensive expansion. A previous assessment indicated that the development of a 10.75-hectare residential area in this neighborhood could increase peak discharge by approximately 24.74% for the 2-year return period and 16.67% for the 5-year return period.Based on these findings, this study aims to evaluate the effectiveness of Low Impact Development (LID) measures, specifically the use of rain barrels, in mitigating the hydrological impacts of land-use changes. Simulations were conducted using the Storm Water Management Model (SWMM) to: (1) analyze changes in peak discharge and runoff volume before and after development; (2) design the residential drainage system while testing different rain barrel capacities; and (3) evaluate the effectiveness of rain barrels in reducing peak discharge and runoff.Simulation results indicate that the installation of two rain barrels per household, each with a diameter of 1.41 m and a height of 1.19 m, can reduce peak discharge by 19.66%, approaching pre-development conditions. However, total runoff volume remains higher than baseline levels, suggesting that additional LID strategies are necessary for more comprehensive flood mitigation. These findings underscore the potential of rain barrels as an effective tool for urban runoff management and provide practical guidance for optimizing their implementation in similar residential developments.
Penerapan Transfer Learning VGG-16 untuk Mendeteksi Penyakit Mata Manusia Berbasis Citra Fundus Willy, Willy; Prabowo, Ary
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 1 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i1.9291

Abstract

Eye disorders represent a serious global health issue that can lead to a decline in quality of life and even permanent blindness. Early diagnostic for eye diseases such as glaucoma, diabetic retinopathy, age-related macular degeneration, cataract, myopia, and hypertension is crucial to prevent more severe complications. The objective of this study is to develop an image classification model for fundus images using a transfer learning approach with the VGG-16 architecture. The dataset used is ODIR-5K, which includes eight classes of eye diseases. The research stages involve image preprocessing, data augmentation, class balancing using SMOTE, and CNN for training the model. The model training process was conducted over 80 epochs with a combination of freezing layers, fine-tuning, and hyperparameter tuning. Model evaluation was carried out using metrics such as accuracy, precision, recall, F1-score, confusion matrix, and ROC AUC curve. The results show that the developed model achieved an accuracy of 89% compared to the previous study which only reached 45%, with a macro average F1-score of 0.89. The model demonstrated excellent performance in classes such as Hypertension, Glaucoma, and Myopia, although challenges remain in distinguishing the Diabetes and Normal classes. Therefore, the VGG-16-based approach has proven effective for multi-class classification of fundus images, and the results of this study may serve as a foundation for developing deep learning-based diagnostic support systems in the field of ophthalmology.