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All Journal EJIP BIOL SPEKTRUM: Jurnal Pendidikan Luar Sekolah (PLS) Jurnal Kreatif Tadulako Online AGROTEKBIS JIMKesmas (Jurnal Ilmiah Mahasiswa Kesehatan Masyarakat) JITEK (Jurnal Ilmiah Teknosains) Faktor Exacta Jusikom : Jurnal Sistem Komputer Musirawas Jurnal Teknologi Informasi MURA Martabe : Jurnal Pengabdian Kepada Masyarakat NUSANTARA : Jurnal Ilmu Pengetahuan Sosial JURNAL PHOTON JFL : Jurnal Farmasi Lampung JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Review Pendidikan dan Pengajaran (JRPP) Building of Informatics, Technology and Science Journal of Computer System and Informatics (JoSYC) Jurnal Ilmiah Biologi UMA (JIBIOMA) Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Wal'afiat Hospital Journal Knowbase : International Journal of Knowledge in Database WAL'AFIAT HOSPITAL JOURNAL IJRE
Sasmita Sasmita
FKIP UNTAD
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Journal : Jusikom : Jurnal Sistem Komputer Musirawas

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PENERAPAN ALGORITMA K-NEAREST NEIGHBOR UNTUK KLASIFIKASI TINGKAT KEMATANGAN BUAH ALPUKAT BERDASARKAN WARNA susanti, tri; Sasmita, Sasmita
Jusikom : Jurnal Sistem Komputer Musirawas Vol 10 No 1 (2025): Jusikom : Jurnal Sistem Komputer Musirawas JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jusikom.v10i1.2603

Abstract

This research develops an avocado fruit ripeness classification system using the K-Nearest Neighbor algorithm based on color feature extraction. Data was collected from 150 Mentega variety avocado samples with three ripeness categories: unripe, semi-ripe, and ripe. The classification process involved image preprocessing, extraction of RGB and HSV color components, and implementation of the KNN algorithm. Results showed the highest accuracy of 95.56% at k=9 using Euclidean Distance metric, with Mean R and Mean H components having the strongest correlation to avocado ripeness levels. The system was successfully implemented with a user-friendly graphical interface, enabling automatic classification with a processing time of 1.2 seconds per image. Compared to other classification methods such as Random Forest and SVM, KNN showed the best performance in modeling avocado color features, offering an effective and efficient solution for the Pagar Alam City Agriculture Department in determining avocado fruit ripeness levels.
Co-Authors Abdul Muis ainurafiq, ainurafiq Ajiza, Pratisa Defiera Amin, Astuti Muh Amiruddin, Eky Endriana Andi Baso Kaswar Andi Baso Kaswar Apriana Apriana Arizah, Ainun Awalia, Nur Azis, Salsabila Chairati, Chairati Dedi Setiadi Dedi Setiadi Delovita Ginting, Delovita Dewi Fatmarani Surianto Dhiniati, Fameira Efan, Efan Eriyani, Nindy Sri Gusmaliza, Debi Ihsan, Afifi Raima Ika Yuni Astuti Inda Anggraini Jamaluddin, Bunga Mawar Jariah S.Intam, Rezki Nurul Junaid Junaid, Junaid Karlina, Siti Khalid, Ansyarif MHD Natsir Mila mila Muhammad Fajar B Muntari, Siti Musdar, Devi Miftahul Jannah Nirmalasari, Ridha Noor Hujjatusnaini NURLAELA NURLAELA Rahayu, Rika Rahim, Amirudin Rahmadayanti, Fitria Ramadhan Ramadhan Retno Wahyuningrum Rininta Andriani Rizki Padya, Inka Romi Fadli Syahputra Saharia Kassa Satria, Defni Siti Aminah Suhada, Desmi Aulia Suhardi Suhardi TRI SUSANTI Yeni solfiah, Yeni Yogi Isro Mukti, Yogi Isro