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All Journal JOIV : International Journal on Informatics Visualization Ensiklopedia of Journal
Hanif, Ilham
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Arts Humanities Computer Science & IT Law, Crime, Criminology & Criminal Justice Social Sciences

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PENGGUNAAN KAPAL PENANGKAP ASING UNTUK KEGIATAN PENANGKAPAN IKAN DI WILAYAH REPUBLIK INDONESIA DIKAITKAN DENGAN ASAS CABOTAGE Hanif, Ilham; Ratnawati, Elfrida
Ensiklopedia of Journal Vol 6, No 4 (2024): Vol. 6 No. 4 Edisi 2 Juli 2024
Publisher : Lembaga Penelitian dan Penerbitan Hasil Penelitian Ensiklopedia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33559/eoj.v6i4.2194

Abstract

Regulating the use of foreign fishing vessels in fishing activities in Indonesian waters. This regulation aims to ensure the availability of fish resources, protect traditional fishermen, and optimise the utilisation of national fisheries potential. This regulation sets strict requirements for foreign vessels, such as the obligation to use a base port, employ Indonesian crew members, and pay fisheries levies. In addition, there are restrictions on operating areas, types of fishing gear, and types of fish that can be caught. Strict supervision is carried out through tracking with a vessel monitoring system, vessel inspections, and catch verification. Violations are subject to administrative sanctions up to revocation of operating licences. Thus, KP Regulation 28/2023 seeks to ensure the sustainability of fish resources, protect the interests of local fishermen, and increase state revenue from the capture fisheries sector.Keywords: kelautan, pertahanan, sumber daya alam.
A Hybrid Model for Dry Waste Classification using Transfer Learning and Dimensionality Reduction Santoso, Hadi; Hanif, Ilham; Magdalena, Hilyah; Afiyati, Afiyati
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.1943

Abstract

The categorization of waste plays a crucial part in efficient waste management, facilitating the recognition and segregation of various waste types to ensure appropriate disposal, recycling, or repurposing. With the growing concern for environmental sustainability, accurate waste classification systems are in high demand. Traditional waste classification methods often rely on manual sorting, which is time-consuming, labor-intensive, and prone to errors. Hence, there is a need for automated and efficient waste classification systems that can accurately categorize waste materials. In this research, we introduce an innovative waste classification system that merges feature extraction from a pretrained EfficientNet model with Principal Component Analysis (PCA) to reduce dimensionality. The methodology involves two main stages: (1) transfer learning using the EfficientNet-CNN architecture for feature extraction, and (2) dimensionality reduction using PCA to reduce the feature vector dimensionality. The features extracted from both the average pooling and convolutional layers are combined by concatenation, and subsequently, classification is performed using a fully connected layer. Extensive experiments were conducted on a waste dataset, and the proposed system achieved a remarkable accuracy of 99.07%. This outperformed the state-of-the-art waste classification systems, demonstrating the effectiveness of the combined approach. Further research can explore the application of the proposed waste classification system on larger and diverse datasets, optimize the dimensionality reduction technique, consider real-time implementation, investigate advanced techniques like ensemble learning and deep learning, and assess its effectiveness in industrial waste management systems.
Co-Authors Afiyati, Afiyati Elfrida Ratnawati Hadi Santoso Hilyah Magdalena