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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JOIN (Jurnal Online Informatika)
Anugrah Tri Ramadhan
Department of Informatics Engineering, Universitas Dian Nuswantoro, Indonesia
Computer Science & IT Control & Systems Engineering

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Fishku Apps: Fishes Freshness Detection Using CNN With MobilenetV2 Muthia Farah Hanifa; Anugrah Tri Ramadhan; Ni’Matul Husna; Nabila Apriliana Widiyono; Rhamdan Syahrul Mubarak; Adisti Anjani Putri; Sigit Priyanta
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 17, No 1 (2023): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.80049

Abstract

Marine fish are one of the most promising economic commodities for the Indonesian economy. Marine fish will decrease in protein content along with the decreasing level of freshness of the fish that will be consumed. There are still many people who do not know about the classification of fresh and unfresh fish, so we need a system that can classify which fish are fresh and which are not. Previous studies have succeeded in classifying tuna using a convolutional neural network (CNN) algorithm with an accuracy of 90%. In the preprocessing stage of this research, segmentation is carried out, which aims to separate the object to be studied and the background image, then feature extraction is carried out using a color moment, which aims to get the value of the object to be studied. This research was conducted to increase the accuracy value in the freshness classification of tuna and also to add some fish for freshness detection, such as mackerel and milkfish, using the MobilenetV2. The results were able to produce accuracy of 97%, 94%, and 93% for each fish. The freshness detection method in this study has been implemented in the Fishku mobile-based application.
Catbreedsnet: An Android Application for Cat Breed Classification Using Convolutional Neural Networks Anugrah Tri Ramadhan; Abas Setiawan
JOIN (Jurnal Online Informatika) Vol 8 No 1 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i1.1007

Abstract

There are so many cat races in the world. Ignorance in recognizing cat breeds will be dangerous if the cat being kept is affected by a disease, which allows mishandling of the cat being kept. In addition, many cat breeds have different foods from one race to another. The problem is that a cat caretaker cannot easily recognize the cat breed. Therefore, technology needs to help a cat caretaker to treat cats appropriately. In this study, we proposed a Machine Learning approach to recognize cat breeds. This study aims to identify the cat breed from the cat images then deployed on an Android smartphone. It was tested with data from cat images of 13 races. The classification method applied in this study uses the Convolutional Neural Network (CNN) algorithm using transfer learning. The base models tested are MobilenetV2, VGG16, and InceptionV3. The results tested using several models and through several experimental scenarios produced the best classification model with an accuracy of 82% with MobilenetV2. The model with the best accuracy is then embedded in an application with the Android operating system. Then the application is named Catbreednet.
Co-Authors Abas Setiawan Adisti Anjani Putri Muthia Farah Hanifa Nabila Apriliana Widiyono Ni’Matul Husna Rhamdan Syahrul Mubarak Sigit Priyanta