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All Journal bit-Tech Jurnal Komputer, Informasi dan Teknologi
Rudi Heriansyah
Indo Global Mandiri University
Computer Science & IT Education Languange, Linguistic, Communication & Media

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Identification of Ginger Varieties Using Manhattan Distance on Image Pixel Vectors and Histograms Rauditha Putri Cahyani; Rudi Heriansyah; Gasim Gasim
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3019

Abstract

The integration of digital image processing and pattern recognition has opened new opportunities for improving agricultural product classification. This study focuses on the identification of three economically important ginger varieties red ginger, elephant ginger, and Emprit ginger through an image-based classification system. Unlike conventional manual inspection, which is prone to subjectivity and error, the proposed method applies a distance-based similarity measure to enhance consistency and reliability. Central to this approach is the use of the Manhattan Distance metric, chosen for its computational efficiency and robustness in high-dimensional data spaces. Two types of image features were explored: global intensity histograms and pixel vector representations. Comparative evaluation demonstrates that histogram-based classification achieves an accuracy of 86.6%, substantially outperforming the pixel vector approach at 76.6%. Novelty this research lies in demonstrating that lightweight, interpretable techniques can deliver competitive accuracy while avoiding the data and computational demands of more complex machine learning or deep learning models. This makes the system particularly suitable for smallholder farmers, local cooperatives, and resource-limited agricultural environments. Moreover, the study highlights the potential of histogram-based representation as a practical solution to variability in lighting and texture, offering improved robustness over traditional visual inspection or pixel-level methods. By contributing a simple yet effective framework, this research advances the field of agricultural informatics and supports the development of low-cost, automated tools for crop identification. Beyond academic significance, the findings have practical implications for supply chain management, post-harvest quality control, and precision agriculture, fostering transparency and value optimization in ginger production and distribution
Implementasi Algoritma K-nearest Neighbor dan Principal Component Analysis untuk Klasifikasi Tingkat Kematangan Ceri Kopi Robusta Berdasarkan Warna Ayu Wulandari; Rudi Heriansyah; Lastri Widya Astuti
Jurnal Komputer, Informasi dan Teknologi Vol. 5 No. 2 (2025): Desember
Publisher : Penerbit Jurnal Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53697/jkomitek.v5i2.3048

Abstract

This study addresses the need for objective and efficient classification of Robusta coffee cherry ripeness, which is crucial for ensuring optimal coffee quality. The research aims to develop an automatic classification system using K-Nearest Neighbor (KNN) and Principal Component Analysis (PCA) based on digital image color features. Employing a quantitative experimental approach, the study utilized 150 digital images of Robusta coffee cherries, categorized into three ripeness levels: unripe, semi-ripe, and ripe. Data were collected using a smartphone camera under controlled lighting, and processed with MATLAB for feature extraction and analysis. The RGB color features were reduced using PCA, and classification was performed with KNN. Evaluation metrics included confusion matrix, accuracy, precision, and recall. The results showed that the system achieved an accuracy of 93.33%, successfully classifying 28 out of 30 test images correctly. These findings indicate that the combination of KNN and PCA provides a reliable and practical solution for automated coffee cherry ripeness classification. The study concludes that this approach can enhance post-harvest processes and support consistent quality in Robusta coffee production.
Co-Authors Ayu Wulandari Gasim Gasim Lastri Widya Astuti Rauditha Putri Cahyani