TRIDANIEL, ARDESMAN (2024) IMPLEMENTASI METODE K-NEAREST NEIGHBOR UNTUK KLASIFIKASI TINGKAT KEMATANGAN BUAH MANGGA HARUMANIS BERBASIS ANDROID. S1 thesis, Fakultas Teknik Universitas Sultan Ageng Tirtayasa.
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Abstract
The advancement of technology continues to drive increased efficiency and effectiveness across various sectors, including agriculture. In Indonesia, mango (Mangifera indica L.) production reached 3.308.895 tons in 2022. This research aims to develop an Android application using the k-Nearest Neighbor (k-NN) method to classify the ripeness levels of Harumanis mangoes. This is to address the issue of differing perceptions among fruit selectors, which often lead to discrepancies with consumer expectations. The application leverages digital image processing technology with Gray Level Co-Occurrence Matrix (GLCM) texture features to detect mango ripeness levels, unripe, ripe, and overripe. The results indicate that the optimal k value is 3, with an average accuracy of 58.6% for unripe, 57% for ripe, and 62.6% for overripe classes. This application is expected to assist fruit selectors and farmers in improving harvest quality according to market standards. The implementation of this technology can reduce the risk of ripeness mismatches, enhance consumer satisfaction, and provide a faster, more accurate, and efficient solution for determining mango ripeness, thereby contributing significantly to the application of digital technology in the agricultural sector.
| Item Type: | Thesis (S1) | ||||||
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| Additional Information: | Kemajuan teknologi terus mendorong peningkatan efisiensi dan efektivitas di berbagai sektor, termasuk di bidang pertanian. Di Indonesia, produksi mangga (Mangifera indica L.) mencapai 3.308.895 ton pada tahun 2022. Penelitian ini bertujuan untuk mengembangkan aplikasi Android menggunakan metode k-Nearest Neighbor (k-NN) untuk mengklasifikasikan tingkat kematangan buah mangga Harumanis. Hal ini dilakukan untuk mengatasi masalah perbedaan persepsi penyeleksi buah yang sering menyebabkan ketidaksesuaian dengan ekspektasi konsumen. Aplikasi ini memanfaatkan teknologi pengolahan citra digital dengan fitur tekstur Gray Level Co-Occurrence Matrix (GLCM) untuk mendeteksi tiga kelas tingkat kematangan, yaitu mentah, matang, dan sangat matang. Hasil penelitian menunjukkan bahwa nilai optimal k adalah 3, dengan akurasi rata-rata sebesar 58,6% untuk kelas mentah, 57% untuk kelas matang, dan 62,6% untuk kelas sangat matang. Diharapkan aplikasi ini dapat membantu penyeleksi buah dan petani dalam meningkatkan kualitas hasil panen sesuai dengan standar pasar. Implementasi teknologi ini dapat mengurangi risiko ketidaksesuaian kematangan buah, meningkatkan kepuasan konsumen, dan memberikan solusi yang lebih cepat, akurat, dan efisien dalam menentukan kematangan buah mangga, serta memberikan kontribusi signifikan dalam penerapan teknologi digital di sektor pertanian. | ||||||
| Uncontrolled Keywords: | Mangifera indica L., Mango Ripeness Levels, Harumanis, GLCM, k-NN, Android | ||||||
| Subjects: | S Agriculture > S Agriculture (General) T Technology > T Technology (General) |
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| Divisions: | 03-Fakultas Teknik 03-Fakultas Teknik > 20201-Jurusan Teknik Elektro |
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| Depositing User: | Ardesman Tridaniel | ||||||
| Date Deposited: | 26 Aug 2024 09:03 | ||||||
| Last Modified: | 26 Aug 2024 09:03 | ||||||
| URI: | http://eprints.untirta.ac.id/id/eprint/41288 |
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