Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Journal of Tropical AgriFood
Agus Dharmawan
Universitas Jember
Agriculture, Biological Sciences & Forestry Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Grading Coffee Beans using Extraction of Shape-Based Features Coupled with Support Vector Machine Agus Dharmawan; Rudiati Evi Masithoh; Siswoyo Soekarno; Hanim Zuhrotul Amanah
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol. 15 No. 3 (2026): June 2026
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtepl.v15i3.895-906

Abstract

Evaluating coffee beans through a computer vision system (CVs) requires a large number of visual attributes to be extracted, but may affect prediction accuracy. Therefore, it is essential to reduce the large features to gain better prediction accuracy by generating new data that represents the most informative dimensions of the original data. Previous studies are limited to comparing different methods of feature extraction. The objective of this research was to explore the comparison of six feature extraction methods (PCA, EFA, LDA, SVD, ICA, and PLS) combined with support vector machine (SVM) as a supervised approach to predict three groups of coffee beans, namely long-berry, normal, and peaberry, for grading issues. SVM with three kernel functions (linear, RBF, and sigmoid) was used to construct a superior classification model. Data were acquired from coffee images processed to generate shape-based features. The results show that LDA provides a better visualization in separating sample classes according to the score plot with 2 variables obtained. The combination of SVM and LDA has a better recognition of coffee beans for grading, which is higher than that of other combinations. A combination of SVM-sigmoid with EFA gave mostly the worst recognition. Our findings proved that the investigation of feature extraction methods and SVM successfully achieve accurate results on grading coffee beans.
Development of Android application for coffee roast level classification using CNN mobilenetev3 based on digital image analysis Isran Mohamad Pakaya; Radi Radi; Agus Dharmawan; Melda Nurmaisari
Journal of Tropical AgriFood Volume 8 Nomor 3 Tahun 2026
Publisher : Department of Agricultural Products Technology, Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35941/jtaf.8.3.2026.26928.202-212

Abstract

Coffee is one of Indonesia's key commodities with high economic value in both domestic and international markets. Roasting is a crucial stage in coffee processing, as it directly influences the final taste and aroma. However, until now, the roasting level has been determined visually and subjectively, making it prone to human error. This study aimed to develop an automatic classification system for coffee roasting levels using a Convolutional Neural Network (CNN) approach integrated into an Android application. The CNN model was built using the MobileNetV3 architecture and applied with a transfer learning method on Google Colab. The dataset consisted of 1,600 coffee images with three roasting levels: light, medium, and dark. The trained model was then converted into TensorFlow Lite (TFLite) format for integration into an Android application called RoastScan. This app features automatic classification through the camera or gallery and provides real-time predictions with confidence levels. The evaluation results show that the model has a classification accuracy of 97% on the test data, with high precision, recall, and F1-score across all classes. Further testing via the application showed that the best accuracy reached 93.55%. These findings suggest that integrating CNN with mobile applications has the potential to be a practical, efficient, and accurate solution for determining coffee roasting degrees, as well as supporting the standardization of coffee product quality at both industrial and MSME levels.
Co-Authors Hanim Zuhrotul Amanah Isran Mohamad Pakaya Melda Nurmaisari Radi Radi Rudiati Evi Masithoh Siswoyo Soekarno