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Segmentasi Kematangan Pisang Raja Berbasis Fitur Warna HSV Menggunakan Metode KNN Aliansa, Warham; Ifayatin, Hadijah Nisa; Saputra, Rizal Adi
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 2 (2023): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i2.669

Abstract

Taxonomically, the banana tree belongs to the Family Musaceae and the Genus Musa. The most widely cultivated species or type of banana worldwide is the wild banana. The ripeness classification of Raja bananas can be obtained through two methods: destructive and non-destructive. Destructive classification is performed through chemical analysis, but it can only be done by destroying the banana. On the other hand, non-destructive classification for Raja bananas can be done by observing the texture and color of the banana peel, which is the outermost part of the fruit, without the need to taste the flesh or peel it, thus keeping the fruit intact. In the classification of king bananas into three ripeness stages: unripe, ripe, and overripe, 150 test data and 15 training data are used. The HSV color feature is employed using the K-Nearest Neighbors (KNN) classification method with the assistance of MATLAB R2021a software, achieving 100% accuracy
Segmentasi Kematangan Pisang Raja Berbasis Fitur Warna HSV Menggunakan Metode KNN Aliansa, Warham; Ifayatin, Hadijah Nisa; Saputra, Rizal Adi
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 2 (2023): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i2.669

Abstract

Taxonomically, the banana tree belongs to the Family Musaceae and the Genus Musa. The most widely cultivated species or type of banana worldwide is the wild banana. The ripeness classification of Raja bananas can be obtained through two methods: destructive and non-destructive. Destructive classification is performed through chemical analysis, but it can only be done by destroying the banana. On the other hand, non-destructive classification for Raja bananas can be done by observing the texture and color of the banana peel, which is the outermost part of the fruit, without the need to taste the flesh or peel it, thus keeping the fruit intact. In the classification of king bananas into three ripeness stages: unripe, ripe, and overripe, 150 test data and 15 training data are used. The HSV color feature is employed using the K-Nearest Neighbors (KNN) classification method with the assistance of MATLAB R2021a software, achieving 100% accuracy