Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer)
Muhammad Fajar B
State University of Makassar
Computer Science & IT

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

Found 2 Documents
Search

 1 
CLASSIFICATION OF PAPAYA NUTRITION BASED ON MATURITY WITH DIGITAL IMAGE AND ARTIFICIAL NEURAL NETWORK Andi Ahmad Taufiq; Hanum Zalsabilah Idham; Muh Fuad Zahran Firman; Andi Baso Kaswar; Dyah Darma Andayani; Muhammad Fajar B; Abdul Muis Mappalotteng; Andi Tenriola
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.7070

Abstract

Papaya is a tropical fruit with high nutritional content and significant health benefits. Nutritional components such as sugars, vitamin C, and fibre are strongly influenced by ripeness level. Identifying these nutrients usually requires laboratory tests that are time-consuming and rely on sophisticated equipment. Previous studies have focused on classifying ripeness levels, yet none have specifically addressed the classification of nutritional content. This study proposes a classification system for papaya nutrition based on ripeness using digital image processing and artificial neural networks (ANN). The method consists of six stages: image acquisition, preprocessing, segmentation, morphology, feature extraction, and classification with a trained ANN model. Experiments were conducted to evaluate feature combinations, including colour and texture features. The combination of LAB colour features and texture features-contrast, correlation, energy, and homogeneity-produced the best results. Testing on 75 images achieved an average precision of 97.22%, recall of 96.67%, F1-Score of 96.80%, and accuracy of 97.33%, with an average computation time of 0.02 seconds per image. These findings indicate that the proposed method provides fast and highly accurate classification of papaya’s nutritional content, offering a practical alternative to laboratory testing. Nevertheless, the study is limited by the relatively small dataset and controlled acquisition environment. Future research should extend the dataset, incorporate deep learning approaches, and validate performance under real-world conditions to enhance robustness and generalization
COMPARATIVE BENCHMARKING OF YOLO11 FOR UAV-BASED HUMAN DETECTION IN SIMULATED DISASTER SCENARIOS A. Ahmad Fadil; Jumadi Mabe Parenreng; Muhammad Fardan; Muhammad Fajar B; Ana Sulistiana Alwi
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 12 No. 1 (2026): JITK Issue August 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v12i1.8002

Abstract

Search and Rescue (SAR) operations require rapid and reliable identification of survivors in disaster-affected areas. This study presents an empirical benchmark of YOLO11m for UAV-oriented human detection in simulated disaster scenarios. The model was trained and evaluated using the synthetically generated C2A: Human Detection in Disaster Scenarios dataset and compared with YOLOv8m, YOLOv9m, and YOLOv10m under consistent training configurations. The experiments were conducted at a resolution of 640 × 640 pixels using mixed-precision training on dual NVIDIA T4 GPUs. YOLO11m achieved an mAP@50 of 0.850, an mAP@50–95 of 0.612, a Precision of 0.880, a Recall of 0.799, and a peak F1-score of 0.830 at a confidence threshold of 0.376. The model also recorded an average inference latency of 5.5 ms per image in the test environment. Compared with the evaluated medium-scale YOLO variants, YOLO11m achieved the highest mAP@50–95 while maintaining moderate parameter and computational requirements. These results indicate that YOLO11m provides a promising accuracy–efficiency baseline for UAV-based SAR research. However, because the evaluation relies on synthetic data and does not include onboard inference or physical UAV field trials, further validation using real-world aerial disaster imagery is required before operational deployment.
Co-Authors A. Ahmad Fadil Abdul Muis Mappalotteng Ana Sulistiana Alwi Andi Ahmad Taufiq Andi Baso Kaswar Andi Tenriola Dyah Darma Andayani Hanum Zalsabilah Idham Jumadi Mabe Parenreng Muh Fuad Zahran Firman Muhammad Fardan