Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Journal of Applied Data Sciences Jurnal Pengabdian kepada Masyarakat
Chalifa Chazar
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Languange, Linguistic, Communication & Media Public Health Social Sciences Other

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

Found 2 Documents
Search

 1 
PENGEMBANGAN DAN PENERAPAN APLIKASI NUTRI-GRADE DI PUSKESMAS CIMARAGAS UNTUK MENDUKUNG EDUKASI DAN PEMANTAUAN GIZI MASYARAKAT Chalifa Chazar; Muchammad Fahrizal; Yoga Ramadhan; Miftah Nugraha; Tegar Akmal; Shavinko Izumi Henryka
J-ABDI: Jurnal Pengabdian kepada Masyarakat Vol. 4 No. 9: Februari 2025
Publisher : Bajang Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53625/jabdi.v4i9.9678

Abstract

This community service program addresses the issue of limited public awareness regarding nutritional balance in food consumption, as highlighted in the background of the activity. The program aims to develop NutriGrade, a mobile application leveraging AI technology to scan and simplify nutritional label information, making it more accessible and engaging for users. Using the Agile development methodology, the project proceeded through planning, design, feature implementation, testing, and maintenance phases. The application was tested among diverse user groups, demonstrating its effectiveness in increasing nutritional understanding, supporting healthier consumption habits, and aligning with smart city objectives by integrating advanced technology into daily life. This initiative successfully contributed to public health awareness by enabling informed dietary choices, thus reducing the risks associated with malnutrition, obesity, and related health issues.
Egg Weight Estimation Based on Image Processing using Mask R-CNN and XGBoost Jasman Pardede; Muhammad Fadlansyah Zikri Akhiruddin Rawosi; Anisa Putri Setyaningrum; Rizka Milandga Milenio; Chalifa Chazar
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.1004

Abstract

Manually measuring egg weight in the context of livestock and the food industry can pose various problems, including time and labor requirements, the risk of egg damage, consistency and accuracy, and limitations on production scale. To address these issues, an automated egg weight estimation system is essential. This study proposes integrating computer vision and machine learning into a unified workflow that combines segmentation, classification, and regression for practical weight estimation. The proposed pipeline employs Mask R-CNN for egg segmentation, Random Forest (RF) classifier for egg type classification based on color features, and XGBoost for regression using morphological, geometric, color features, and egg type as predictors. The dataset used is 720 images, consisting of 20 eggs (10 chicken and 10 duck), each photographed from 36 rotational angles, and was collected with Ground Truth (GT) weights obtained from a digital scale. Experimental findings show that the RF classifier achieved perfect accuracy (precision, recall, and F1-score = 1.00) in distinguishing chicken and duck eggs. The XGBoost regressor obtained a training performance of MAE = 1.07 g and R² = 0.68, and a validation performance of MAE = 0.23 g and R² = 0.80 under 10-fold grouped cross-validation. Although a Support Vector Regressor baseline reached higher training accuracy (MAE = 0.22 g, R² = 0.96), it failed to generalize on validation (R² 0), highlighting XGBoost’s robustness. The feature importance analysis revealed that there are 4 (four) important features for building an estimation model, namely: Hu moments, eccentricity, elongation, and diagonal length, while color statistics played a complementary role. The novelty of this work lies in combining deep segmentation, color-based classification, and feature-driven regression into a unified framework specifically for egg weight estimation, showing its feasibility as a proof of concept and laying the foundation for future large-scale, calibrated, and externally validated deployment.
Co-Authors Anisa Putri Setyaningrum Jasman Pardede Miftah Nugraha Muchammad Fahrizal Muhammad Fadlansyah Zikri Akhiruddin Rawosi Rizka Milandga Milenio Shavinko Izumi Henryka Tegar Akmal Yoga Ramadhan