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All Journal JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JITER-PM (Jurnal Inovasi Terapan - Pengabdian Masyarakat) Komposit : Jurnal Pengabdian Dan Pemberdayaan Masyarakat
Rina Anugrahwaty
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Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Engineering Health Professions Social Sciences Other

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Journal : JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING

 1 
Applications For Detecting The Rate Of Fruit In Mangrove Plants Sharfina Faza; Meyatul Husna; Ajulio Padly Sembiring; Rina Anugrahwaty; Silmi; Romi Fadillah Rahmat; Rhama Permadi Ahmad
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 6 No. 2 (2023): Issues January 2023
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v6i2.8379

Abstract

Mangrove plants are one of the plants that really help aquatic ecosystems between the sea, coast, and land. Mangrove plants provide many ecological, social, and economic benefits. In Indonesia, mangrove plants have 202 species with the same anatomy as other plants in general, consisting of roots, fruits, stems and leaves. Nowadays, the location of mangrove plants in Indonesia has experienced the fastest damage in the world due to conversion to ponds, settlements, industry and plantations. One of the efforts to restore aesthetic value and restore the ecological function of mangrove forest areas is rehabilitation using mangrove fruit. In the rehabilitation process, farmers generally use the manual method with the naked eye to determine fruit ripeness on mangrove plants, so the resulting level of accuracy is not optimal. To overcome this problem, an application is needed that can facilitate farmers in determining fruit maturity in mangrove plants so that it can help determine the maturity level of mangrove fruit. The development of this application utilizes the Deep Learning method as well as the utilization of digital image processing techniques with Grayscaling, Adaptive Threshold, Sharpening and Smoothing techniques. The results of this study are an application that can detect the level of fruit maturity in mangrove plants with an accuracy of 99.11%. With this application, determining the maturity level of fruit on mangrove plants can be easily done.
Multi-Detection System Using Faster R-CNN for Fish Species Classification and Quality Assessment on Android Faza, Sharfina; Lubis, Arif Ridho; Meryatul Husna; Rina Anugrahwaty; Muhammad Rafif Rasyidi; Romi Fadillah Rahmat
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 2 (2026): Issues January 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i2.16374

Abstract

Species identification and quality assessment of fish in trade still rely on manual visual observation, which is subjective and requires specialized expertise. This method's limitations make it difficult for consumers to distinguish species with similar morphology and accurately assess fish quality, which can lead to inappropriate purchasing decisions. This research develops a multi-detection system based on Faster R-CNN with VGG16 backbone for fish species classification and quality assessment simultaneously on Android platform. The system uses convolutional layers to extract visual features from input images, Region Proposal Network for fish object detection and localization, and fully connected layers for simultaneous classification of species and quality levels. The research dataset consists of 3,000 images of five fish species (gourami, tilapia, nile tilapia, snapper, and pomfret) with four quality levels, divided into 2,400 training images and 600 testing images. The trained model is converted to TensorFlow Lite format for implementation on Android devices. Test results show the multi-detection system achieves 92% accuracy in fish species classification and quality assessment, demonstrating the effectiveness of the Faster R-CNN approach for multi-detection applications in the Android-based fisheries sector.
Co-Authors Ajulio Padly Sembiring Andam Lukcyhasnita Faza, Sharfina Gunawan Gunawan Husna, Meryatul Julham Julham Lubis, Arif Ridho Lukcy T Simanjutak Marliana Sari Meyatul Husna Muhammad Rafif Rasyidi Nur Adilah Nurhafni Carol Panangian Mahadi Sihombing Rhama Permadi Ahmad Romi Fadillah Rahmat Sari, Indah Vusvita Sembiring, Ajulio Padly Silmi Silmi Silmi Wiwin Sry Adinda Banjarnahor