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All Journal TEKNIK INFORMATIKA Prosiding KOMMIT Inovisi Bulletin of Electrical Engineering and Informatics InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Applied Information System and Management Journal of Applied Data Sciences Jurnal Teknologi Sistem Informasi
Zulfiandri Zulfiandri
Universitas Islam Negeri Syarif Hidayatullah Jakarta
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Transportation

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Journal : Bulletin of Electrical Engineering and Informatics

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Applying convolutional neural network and Nadam optimization in flower classification Aini, Qurrotul; Zulfiandri, Zulfiandri; Firmansyah, Rezky; Arif, Yunifa Miftachul
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.6203

Abstract

Flowers have a variety of shapes, colors and structures, the images of which need to be classified using guided learning techniques. Several studies classify flowers using machine learning, but their accuracy performance is not good. The thing is, the flowers come in a variety of colors that can sometimes look similar to the background. Therefore, this study aims to classify flowers using a convolutional neural network (CNN) and measure its performance. The method used is mixed methods by collecting existing data from previous studies and connecting it with the realities in the field. The Kozłowski and Steinbrener models were used, while the image data was obtained from the Oxford17 and Oxford102 dataset with 17 and 102 flower types, respectively. The results show 60% and 84% accuracy of CNN using the scratch and transfer learning approach for the Oxford17 dataset. The Oxford102 dataset shows 42% and 64%, respectively, with CNN from baseline and transfer learning.
Conceptual design for traceability and transparency in halal self-declared with blockchain Munawar, Munawar; Zulfiandri, Zulfiandri; Mugiyono, Arif
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8634

Abstract

Halal self-declared is a halal certification procedure for small micro enterprises (SMEs). However, sufficient technology support is required to ensure the halal process’s transparency and traceability. By integrating blockchain-oriented software engineering (BOSE) technology with smart contracts and electronic product code information services (EPCIS), this study aims to deliver a conceptual design for traceability and transparency in halal self-declared. The effectiveness of the blockchain in storing data and disclosing private information to interested parties can be circumvented by utilizing both off-chain and on-chain technology. The effectiveness of blockchain data storage and the ability to disclose sensitive information to interested parties can be advantageous for both on-chain and off-chain applications.
Co-Authors Abdillah, Yusuf Abdul Mughni Ashirotu Syafi’i Aisy, Rohadatul Amany Burhanuddin Umar Lubis Buslim, Nurhayati Evy Nurmiaty Firmansyah, Rezky Fitroh Fitroh Fitroh Fitroh Fitroh Fitroh Fitroh Fitroh, Fitroh Gunawan Pria Utama Ikhlayel, Mohammed Isnaini Monita Safitri KyungOh, Lee Lubis, Amany Burhanuddin Umar Mochammad Anas Mohammed Ikhlayel Mugiyono, Arif Muhammad Chendy Setiadi Munawar Munawar Muntasha, Irlan Nur Cholis Nurhayati Nurhayati Nurhidayah, Jessika Nurjannah, Wilda nurmiaty, evy Nuryasin Nuryasin Nuryasin Nuryasin Puji Rahayu Puji Rahayu Qurrotul Aini Rohadatul Aisy Rony, Zahara Tussoleha Safitri, Isnaini Monita Sarip Hidayatuloh Syafi’i, Abdul Mughni Ashirotu Yunifa Miftachul Arif