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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)
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 Social Sciences Other

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Comparison of DenseNet-121 and MobileNet for Coral Reef Classification Hadi, Heru Pramono; Rachmawanto, Eko Hari; Ali, Rabei Raad
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.
KLASIFIKASI TERUMBU KARANG MENGGUNAKAN CNN MOBILENET Hadi, Heru Pramono; Rachmawanto, Eko Hari; Sari, Christy Atika
Semnas Ristek (Seminar Nasional Riset dan Inovasi Teknologi) Vol 8, No 01 (2024): SEMNAS RISTEK 2024
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/semnasristek.v8i01.7177

Abstract

Terumbu karang merupakan bagian dari ekosistem laut yang indah, namun dibalik keindahan tersebut, terumbu karang juga rentan akan kerusakan ekosistem yang terjadi, yang dimana dapat disebabkan oleh terumbu karang rentan mengalami pemutihan oleh aktivitas yang terjadi di sekitar ekosistem terumbu karang tersebut. Oleh karena itu, diperlukan proses klasifikasi atau pemilahan antara terumbu karang yang terkena pemutihan, sehat ataupun mati sehingga dapat diambil suatu tindakan konservatif yang tidak merusak ekosistem terumbu karang tersebut. Pada penelitian ini, akan dilakukan proses klasifikasi terumbu karang dengan menggunakan metode transfer learning Convolutional Neural Network yaitu dengan arsitektur MobileNet. Dalam proses penelitian ini, akan menggunakan dataset yang berjumlah total 1582 data citra terumbu karang yang memiliki 3 kelas utama dengan sebaran data yaitu 720 data bleached, 150 data dead dan 712 data healthy. Hasil yang didapatkan setelah dilakukannya proses pengujian pada penelitian ini yaitu arsitektur MobileNet mendapatkan akurasi pengujian yaitu sebesar 88%.
Manajemen Sampah Dalam Meningkatkan Circular Economy Di Desa Kebuman, Kecamatan Banyubiru, Semarang Hadi, Heru Pramono; Gamayanto, Indra; Faisal, Edi; -, Suhariyanto; Fahmi, Amiq
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 7, No 1 (2024): JANUARI 2024
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v7i1.1743

Abstract

Abstrak Permasalahan sampah sudah menjadi permasalahan dunia, terutama sampah anorganik dan B3 yang tidak dapat diurai secara alami, sementara jumlah produksi sampah terus bertambah seiringdengan pertumbuhan penduduk. Dari data statistik Kabupaten Semarang jumlah sampah yang terangkut mulai tahun 2019 sebanyak : 220 487 M3, tahun 2020 : 247 095 M3 dan tahun 2021 : 280 859 M3, hal ini menunjukkan peningkatan jumlah sampah naik secara liner. Desa KebumenKecamatan Banyubiru Kabupaten Semarang menghadapi permasalahan yang serupa dengan meningkatnya volume sampah rumah tangga berdampak pada lingkungan yang kurang sehat. Meskipun sudah ada bank sampah pada wilayah tersebut namun ada beberapa kendala yangdihadapi yaitu manajemen sampah, reduce, reuse dan recycle atau 3 R belum optimal. Program PKM (Program Kemitraan Masyarakan) Universitas Dian Nuswantor dengan penerapan manajemen sampah yang efektif dan efisien dengan metode FDG (Focus Group Discussion) dan Edukasi dan Pelatihan diharapkan sampah yang terdapat diwilyah tersebut diolah baik sehingga dapat meniminalkan dampak negatif sampah terhadap lingkungan hidup desa Kebumen dan dapatmenciptakan circular ekonomi, sehingga dapat meningkatkan taraf ekonomi masyarakat setempat Kata kunci: Pengelolaan, Sampah, Manajemen, Taraf Hidup, Ekonomi
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.
Co-Authors -, Suhariyanto Al zami, Farrikh Ali, Rabei Raad Alzami, Farrikh Ashari, Ayu Aufa Nisrina Aulia 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 Heru Lestiawan Hindarto, Aris Nur Indra Gamayanto Jannah, Safira Baiti Kusumawati, Yupie Maurensa, Giacinta Megantara, Rama Aria Moh. Yusuf, Moh. Muhammad Naufal, Muhammad MY. Teguh Sulistyono Nazella, Desvita Dian 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 Winaryanti, Hida Sekar Zaenal Arifin