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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Faizal Andy Susilo
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Deteksi Kesegaran Ikan Tongkol (Euthynnus Affinis) secara Otomatis Berdasarkan Citra Mata Menggunakan Binary Similarity Hurriyatul Fitriyah; Dahnial Syauqy; Faizal Andy Susilo
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 5: Oktober 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020753839

Abstract

Ikan tongkol (Euthynnus Affinis) adalah salah satu ikan yang paling banyak diminati di Indonesia karena kandungan proteinnya hampir setara ikan tuna, namun dengan harga relatif lebih murah. Ikan termasuk komoditi pangan yang mudah rusak tanpa adanya penanganan khusus ketika ikan ditangkap. Padahal, mutu dan nilai jual ikan sangat tergantung dari parameter kesegaran ikan itu sendiri. Penelitian ini mengembangkan metode deteksi kesegaran ikan tongkol menggunakan fitur berupa citra mata ikan. Mata ikan dapat digunakan untuk mengetahui tingkat kesegarannya. Ikan segar memiliki pupil bulat berwarna hitam yang utuh dan jernih di tengahnya. Hal tersebut kemudian dijadikan knowledge-based dari proses deteksi kesegaran ikan. Sebelum dilakukan proses deteksi, dilakukan proses pre-processing untuk mendapatkan gambar kepala ikan secara otomatis. Selanjutnya dilakukan perhitungan similarity antara citra biner kepala ikan dengan 2 buah template, yakni Template-Mata untuk mendeteksi mata dan Template-Tengah untuk mendeteksi bulat hitam di tengah mata. Sebanyak 30 citra mata ikan dengan kriteria segar dan tidak segar digunakan sebagai data pengujian. Dari pengujian, kedua template tersebut mampu membedakan ciri morfologis dari mata ikan yang segar dengan tepat.AbstractTongkol fish (Euthynnus Affinis) is one of the most popular fish in Indonesia because it has more protein than tuna, but with a relatively cheaper price. Fish is a perishable food commodities if it is caught without any special handling. In fact, the quality and value of fish selling depends on the parameters of the freshness of the fish itself. This study developed a method for detecting freshness of tongkol fish using features that is extracted from the image of a fish's eye. Fish eye can be used to determine the level of freshness. Fresh fish have whole round and clear black pupils in the middle. This is then made into knowledge-base on the process of detecting the freshness. First, this fully automatic detection performed a pre-processing process to obtain automatic fish head images. It was then compared with two templates, which are eye-template and middle-template. If the fish head image has similarity below certain threshold then it is classified as fresh fish, or else it is non-fresh fish. A total of 30 images of fish with fresh and non-fresh criteria were used as test data. From the test, the two templates can classify the morphological characteristics of fresh fish eyes precisely.
Sistem Klasifikasi Kualitas Ikan Tongkol Beku Berdasarkan Fitur Nilai Warna HSV Menggunakan Metode Naive Bayes Faizal Andy Susilo; Hurriyatul Fitriyah; Gembong Edhi Setyawan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 1 (2019): Januari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (798.211 KB)

Abstract

The factor of fish quality can be affected by storage procedure and processing treatment is still done manually. Of course, it can make the fish quality will decrease and the sorting process will be wrong. From this problem, it is needed some research and system that can reduce errors to classify fish quality. On this research, we are using image processing and Bayesian method to classify fish quality. Fish will be placed on a styrofoam box that has been equipped with a webcam camera and lamp as lighting. Image processing is used to convert an image from RGB space to HSV space, and we crop the image to get the head section. And after that, we use the hue histogram colour information for the parameter to classify. so the value of bin1, bin 2, and bin 3 and also the standard deviation from histogram value are using as input for classification using Naive Bayes and will process in Raspberry Pi 3 and finally, we can get the fish quality. We are doing some testing. From testing how to implement image processing for this system we get some conclusion that the image which uses the lighting from 5 Watt lamp with white fabric clothes has a good image result, and the result for hue value information from images has to be added. And from testing Naive Bayes methods accuracy was 72.727% and the computation time was 468.864 ms.
Co-Authors Dahnial Syauqy Fitriyah, Hurriyatul Gembong Edhi Setyawan