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All Journal Jurnal Manajemen Pendidikan: Jurnal Ilmiah Administrasi, Manajemen dan Kepemimpinan Pendidikan Jurnal Ilmu Kesehatan Duta Gama Klaten English Language and Literature International Conference (ELLiC) Proceedings Aspirasi : Jurnal Masalah-masalah Sosial Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) Jurnal Manajemen Pendidikan dan Ilmu Sosial (JMPIS) Triwikrama: Jurnal Ilmu Sosial Proceedings Series of Educational Studies
Tyas Wulandari
Unknown Affiliation
Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences Other

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A COMPUTER VISION APPROACH FOR CLASSIFYING CALIFORNIA PAPAYA RIPENESS USING K-NEAREST NEIGHBOR Wulandari, Tyas; Prabowo, Iwan Ady; Utami, Yustina Retno Wahyu; Raharja, Bayu Dwi; Wijayanto, Hendro
Jurnal Teknologi Informasi dan Komunikasi (TIKomSiN) Vol 14, No 1 (2026): Jurnal Tikomsin, Vol 14, No.1, April 2026
Publisher : STMIK Sinar Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30646/tikomsin.v14i1.1091

Abstract

Determining the ripeness level of California papaya is important for harvest decisions, sorting, distribution, and spoilage control. In practice, ripeness identification is still commonly performed visually and is therefore subjective. This study aims to develop a digital image-based classification system for California papaya ripeness using the K-Nearest Neighbor (K-NN) algorithm with Hue and Saturation features in the Hue Saturation Value (HSV) color space. The dataset consists of 90 primary images, divided into 60 training images and 30 testing images, with four ripeness classes: unripe, half-ripe, ripe, and rotten. All images were captured using a Xiaomi Mi A2 Lite smartphone and cropped to 1436 × 1000 pixels. Classification was conducted using Euclidean distance. The value of k was selected empirically through trial and error in the original study, and k = 9 was retained because it produced the most stable result on the available data while reducing the potential for class ties. The evaluation produced 22 correct predictions out of 30 test images, resulting in an accuracy of 73.33%. This revised manuscript strengthens the methodological reporting by clarifying parameter selection, documenting the data distribution and providing a literature-based comparison with alternative methods, such as Support Vector Machine (SVM) and Convolutional Neural Network (CNN). The findings suggest that K-NN with HSV features remains a feasible, low-cost baseline, although its performance should be improved through larger datasets, per-class evaluation reporting, and head-to-head comparisons on the same dataset.
Co-Authors Aan Fardani Ubaidillah Abidah Rolian Adinda Aulia Agus Timan Ardianti, Vika Fatimah Nur Heri Dwi Santoso, Heri Dwi Ida Untari Iwan Ady Prabowo Lukman Nul Hakim Raden Bambang Sumarsono Raharja, Bayu Dwi RAHMA DEWI, ZAQIA Rimadani, Rida Tuanany, Nursyahar Jihan Vannesa Meta Victoria Laira Wijayanto, Hendro Yeni Rusyani Yesika Maya Ocktarani Yustina Retno Wahyu Utami Yusuf Sobri, Ahmad