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All Journal Jurnal Abdi Insani Bulletin of Computer Science Research
Mu’awanah, Cahyatul
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Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics

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SOSIALISASI PENGOLAHAN JAMUR TIRAM MENJADI NUGGET SEBAGAI UPAYA PENINGKATAN EKONOMI MASYARAKAT DESA TAMANSARI WONOREJO PASURUAN Soedarmadji, Wisma; Maulida, Ade Khofifa; Linsia, Nada Afra; Rochmah, Habibatur; Khasanah, Imarotul; Nurroin, Kavita; Nisa, Khoirun; Hidayah, Reissa Arifa Nur; Saskiyah, Bela Alimatus; Mu’awanah, Cahyatul
Jurnal Abdi Insani Vol 11 No 3 (2024): Jurnal Abdi Insani
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/abdiinsani.v11i3.1798

Abstract

White oyster mushroom (Pleurotus ostreatus) is a type of fungus that often grows on agricultural waste in the form of wood or its derivatives. Fungi obtain food from places where they grow and can survive on plant remains around other organisms. Oyster mushrooms are a food commodity that is in great demand among the public, so by cultivating mushrooms it is hoped that they will be able to be involved further, it is hoped that creativity and innovation will emerge and be able to provide added value to society. Oyster mushrooms are only produced and sold fresh without any diversification process. In this KKN activity there will be training on oyster mushrooms to increase family income. As a result of this training, PKK women from Tamansari Wonorejo Village were able to provide understanding and skills to activity participants. PKK women in Tamansari Wonorejo Village have knowledge and skills regarding the food potential around them as an effort to increase family income.
Lightweight Convolutional Neural Network Based on Modified LeNet for Retinal Pathology Classification in High-Resolution Fundus Imaging Mu’awanah, Cahyatul; Hakim, Lukman
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.796

Abstract

Eye disease are visual impairments that can lead to blindness if not detected early. Fundus imaging is one of the most effective methods for identifying abnormalities in the eye. With the advancement of deep neural network technologies, particularly Convolutional Neural Network (CNN), the classification of fundus image can now be performed efficiently. LeNet is a well-known CNN architecture commonly used in image classification tasks, however it has limitation when processing images with complex visual features with high resolution, such as fundus images. This study proposes a modification to the LeNet architecture to enhance it’s a ability to extract important features from images with high resolution. The modification involves adding convolutional layers and adjusting image resolution to optimize the models performance in detecting eye disease in fundus images. The dataset used consists of 4,217 fundus images, classified into four categories: normal, cataract, glaucoma, and diabetic retinopathy. Experimental result show that the original LeNet-5 achieved an accuracy 0f 76%, while the modified LeNet architecture improved the accuracy to 86%. The main contibution of this research lies in the development of a modified and lighweight LeNet architecture, which is capable of handling high-resolution fundus images while maintainig computational efficiency and producing better classification performance compared to the original LeNet.
Co-Authors Hidayah, Reissa Arifa Nur Khasanah, Imarotul Khoirun Nisa Linsia, Nada Afra Lukman Hakim Maulida, Ade Khofifa Nurroin, Kavita Rochmah, Habibatur Saskiyah, Bela Alimatus Wisma Soedarmadji