Sistem Klasifikasi Kesegaran Daging Ikan Gurami berdasarkan Warna dan Gas Amonia menggunakan K-Nearest Neighbor (KNN) berbasis Arduino - M. Fiqhi Hidayatulah; Hurriyatul Fitriyah; Fitri Utaminingrum (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2022)
Gouramy in some areas is one of the mainstay of agriculture. Gouramy meat is one of the sources of food that contains lots of good nutrition for humans. The protein content in gouramy is 19%, more than other fish that are often consumed by humans, such as catfish which has (18.2%) protein content, tilapia which has (16%) protein content, and carp which has ( 16%) protein content. The protein found in fish has more benefits than other animal meat. The characteristics of fresh fish are that it has a smooth texture but is still quite chewy and dense. All parts of the meat seemed to stick tightly to the bone. Gouramy has a weakness after the fish dies, that is, it quickly declines in quality, the speed of decline in fish quality can be influenced by internal factors that depend on the type of fish itself and externally related to human handling. gouramy meat that is not fresh or rotten with fresh gourami meat. So it will be very dangerous if the meat is not fresh or in a rotten state to be consumed by consumers. In this study, a classification system was created that can detect the level of freshness of gouramy meat with the characteristics of quality deterioration found in gouramy meat. With the MQ137 sensor to calculate the NH3 odor parameter and the TCS3200 sensor to calculate the color value of gouramy meat with an output of 3 classes, namely Rotten, Less and Fresh which is displayed on lCD16x2. With the classification results obtained on 180 training data and 30 test data, it produces an accuracy of up to 80% with an average computation time of 8.0667 ms..