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Comparison of DenseNet-121 and MobileNet for Coral Reef Classification Heru Pramono Hadi; Eko Hari Rachmawanto; Rabei Raad Ali
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.3683

Abstract

Coral reefs are a type of marine organism that has beauty and benefits for other sea creatures’ ecosystems. However, despite its beauty and usefulness, coral reefs are vulnerable to damage such as coral bleaching, which can impact other coral reef ecosystems. This research aims to classify digital images of healthy, bleached, and dead coral reefs. This research method is DenseNet-121 and MobileNet is based on Convolutional Neural Networks. This research uses a dataset from 1582 coral reef image data with three main classes: 720 were bleached, 150 were dead, and 712 were healthy. The testing process is carried out using several forms of split datasets, namely 60:10:30, 50:10:40, and 70:10:20. The test results obtained with a data sharing percentage of 60:10:30 show that MobileNet architecture achieved 88.00% accuracy, and DenseNet-121 achieved 91.57% accuracy. Using a data split percentage of 50:10:40, MobileNet achieved 84.51% accuracy, and DenseNet- 121 achieved 90.52% accuracy. Meanwhile, with a data separation percentage of 70:10:20, MobileNet achieved 85.48% accuracy, and DenseNet-121 achieved 92.74% accuracy.
Determining Factors in Choosing a Caesarean Section: The Role of Health Financing and Availability of Health Facilities in Indonesia Alfiena Nisa Belladiena; Ayu Ashari; Nugraheni Kusumawati; Syifa Sofia Wibowo; Fitria Wulandari; Heru Pramono Hadi; Aries Setiawan
International Journal Of Health Science Vol. 5 No. 3 (2025): November : International Journal of Health
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/ijhs.v5i3.6081

Abstract

The rising global cesarean section (SC) rate, projected to reach 29% by 2030, is a concern in Indonesia, where SC prevalence increased to 25.9% in 2023 from 17.6% in 2018. While medical indications drive SC, non-clinical factors like financing and healthcare access may contribute to overuse. This study examines the role of Indonesia’s BPJS health insurance and hospital availability in determining SC utilization. A cross-sectional analysis was conducted using SKI 2023 data, including 70,916 women with deliveries between 2018 and 2023 (weighted n = 20,076,001). Bivariate associations were assessed using chi-square tests with complex sample design, applying survey weights for national representativeness. SC prevalence was 25.9%, with 34.9% of BPJS-covered deliveries being SC compared to 10.8% for out-of-pocket payments (p < 0.001). Hospital availability within the district was associated with a 27.0% SC rate versus 16.1% where no access existed (p < 0.001). Private insurance (50.3%) and employer-funded (37.4%) deliveries also showed higher SC rates. BPJS coverage and hospital availability significantly influence SC utilization in Indonesia, suggesting improved access but potential overuse. Rural disparities highlight the need for infrastructure investment to ensure equitable maternal care under Universal Health Coverage. Further research with causal methods is recommended.
Perbandingan Metode Peramalan ARIMA dan Single Exponential Smoothing pada Kasus Kejadian Demam Berdarah Dengue di Kota Semarang Amiq Fahmi; Giacinta Maurensa; Heru Pramono Hadi; Aris Nur Hindarto; Sasono Wibowo; Edi Sugiarto
JOINS (Journal of Information System) Vol 8 No 2 (2023): Edisi November 2023
Publisher : Fakultas Ilmu Komputer, Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/joins.v8i2.9335

Abstract

Demam berdarah dengue (DBD) merupakan masalah kesehatan yang signifikan di Indonesia, khususnya di Kota Semarang. Setiap tahunnya, terdapat tren peningkatan penderita demam berdarah. Jika pemangku kepentingan tidak melakukan tindakan dan kebijakan preventif, hal ini akan berdampak buruk pada kesehatan dan kesejahteraan masyarakat. Peramalan kasus di masa yang akan datang merupakan salah satu upaya pencegahan dan pengendalian penyakit DBD. Penelitian ini menggunakan teknik peramalan ARIMA dan Single Smoothing Exponential. Data time series yang digunakan adalah bulan Januari sampai dengan Desember 2022 berdasarkan kasus kejadian di tingkat kecamatan Kota Semarang. Hasil percobaan kedua metode tersebut kemudian dibandingkan untuk mencari hasil terbaik dalam memprediksi jumlah kasus DBD di Kota Semarang. Hasil penelitian menunjukkan bahwa metode ARIMA memberikan hasil terbaik, dengan nilai MSE dan MAE yang lebih kecil.