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All Journal Jurnal Kolaboratif Sains Journal of Advanced Health Informatics Research
Sony Kartika Wibisono
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Private Blockchain in the Field of Health Services Purwono, Purwono; Nisa, Khoirun; Sony Kartika Wibisono; Bala Putra Dewa
Journal of Advanced Health Informatics Research Vol. 1 No. 1 (2023)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v1i1.14

Abstract

Blockchain is a technology that is quite popular and has been adopted in various fields in recent years. This technology has caught the attention of researchers in the health sector because of its innovation which is considered capable of providing the necessary guarantees for the safe processing, sharing, and management of sensitive patient data. There are many problems with falsifying reports and withholding important information from patients, which is considered medical fraud. Hyperledger, a type of private Blockchain, is very suitable for healthcare applications. A private blockchain is a restricted type of blockchain network created by an entity. This type of network is limited to those with access permissions. In addition, private blockchains usually use a centralized verification system and are controlled by the network's creators. Hyperledger Fabric is one example of a permissioned blockchain that can play a role in implementing patient-centric, interoperable healthcare systems
Analisis Deep Learning Metode Convolutional Neural Network Dalam Klasifikasi Varietas Gandum: Analysis of Convolutional Neural Network Deep Learning Method in Durum Wheat Variety Classification Rian Ardianto; Sony Kartika Wibisono
Jurnal Kolaboratif Sains Vol. 6 No. 12: DESEMBER 2023
Publisher : Universitas Muhammadiyah Palu

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Abstract

Selama ini, Indonesia memenuhi kebutuhan gandum dengan mengimpor dari beberapa negara, seperti Australia, Ukraina, Kanada, Argentina, Amerika Serikat, Bulgaria, Moldova, Rusia, India, dan lain-lain. Tanaman ini umumnya tumbuh subur di wilayah subtropis dengan suhu berkisar 10–25°C dan curah hujan antara 350–1.250 mm. Penelitian ini bertujuan untuk menjelaskan metode transfer learning pada arsitektur Convolutional Neural Network (CNN) guna mendukung identifikasi otomatis. Keunggulan CNN terletak pada kemampuannya yang tidak memerlukan ekstraksi fitur karena fitur ekstraksi sudah terintegrasi secara otomatis dalam CNN. Studi ini melakukan perbandingan antara dua arsitektur CNN pada tiga jenis gandum yang berbeda. Hasil analisis menggunakan 150 citra data latih dan 45 citra data uji menunjukkan bahwa arsitektur MobileNet mampu memodelkan dataset dengan tingkat akurasi mencapai 98%, sementara tingkat kesalahan mencapai 0,02%.
Co-Authors Bala Putra Dewa Khoirun Nisa Purwono, Purwono Rian Ardianto