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All Journal Dinamik TELKOMNIKA (Telecommunication Computing Electronics and Control) JAIS (Journal of Applied Intelligent System) Proceeding SENDI_U Journal of Information System Sinkron : Jurnal dan Penelitian Teknik Informatika Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Building of Informatics, Technology and Science Abdimasku : Jurnal Pengabdian Masyarakat SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Informatika Polinema (JIP) Jurnal Informatika: Jurnal Pengembangan IT Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) International Journal of Health Science
Hadi, Heru Pramono
Universitas Dian Nuswantoro
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Public Health Social Sciences Other

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Data-Driven K-Means Clustering Analysis for Stunting Risk Profiling of Pregnant Women Nazella, Desvita Dian; Hadi, Heru Pramono; Al Zami, Farrikh; Ashari, Ayu; Kusumawati, Yupie; Suharnawi, Suharnawi; Megantara, Rama Aria; Naufal, Muhammad
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8415

Abstract

Stunting in children is influenced by maternal health conditions during pregnancy. This study aims to classify pregnant women to prevent stunting based on clinical, demographic, and environmental factors using the K-Means Clustering algorithm. A total of 229 data from the Primadona application (Disdalduk KB Kota Semarang) were analyzed using 14 normalized variables. The optimal number of clusters was determined using the Elbow Method and validated using the Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index. The Kruskal-Wallis test was performed to verify differences between clusters. This study resulted in seven clusters with different profiles, with a Silhouette Score of 0.134, Davies-Bouldin Index of 1.509, and Calinski-Harabasz Index of 29.54. These values ​​indicate that the cluster structure is formed and reflects the variation in risk for pregnant women, although there is overlap due to differences in characteristics between individuals. The clustering successfully differentiated pregnant women with low to high risk, influenced by health and environmental factors. This study proves the effectiveness of K-Means in identifying stunting risk patterns in pregnant women and supports more targeted interventions, such as nutritional counseling, disease risk monitoring, education on cigarette smoke exposure, and referrals. Limitations of this study include the unbalanced distribution of data between and the use of cross-sectional data. Future research is recommended to improve pre-processing and compare other clustering methods such as K-Medoids or DBSCAN for more precise stunting risk analysis.
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.
Co-Authors -, Suhariyanto Al zami, Farrikh Alfiena Nisa Belladiena Alzami, Farrikh Aries Setiawan Ashari, Ayu Aufa Nisrina Aulia Ayu Ashari Azzami, Salman Yuris Adila Cahaya Jatmoko Cahyani, Tiara Indah Candra Irawan Christy Atika Sari Edi Faisal Edi Sugiarto Edi Sugiarto Eko Hari Rachmawanto Fahmi Amiq Faisal, Edi Fitria Wulandari Heru Lestiawan Hindarto, Aris Nur Indra Gamayanto Jannah, Safira Baiti Kusumawati, Yupie Maurensa, Giacinta Megantara, Rama Aria Moh. Yusuf Muhammad Naufal MY. Teguh Sulistyono Nazella, Desvita Dian nugraheni kusumawati Nurhindarto, Aris Pristyanti Nawang Putri R. Septiansa Anggoro Rabei Raad Ali Rohman, M. Hilma Minanur Sasono Wibowo Sinaga, Daurat Suhariyanto Suhariyanto Suharnawi Suharnawi Sukamto, Titien Sukamto, Titien Suhartini Titien S. Sukamto Titien Sukamto Wellia Shinta Sari Wibowo, Syifa Sofia Winaryanti, Hida Sekar Zaenal Arifin