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All Journal Journal of Information Systems and Informatics Buletin Ilmiah Sarjana Teknik Elektro
Bayu Krisna Murti
Universitas Ahmad Dahlan
Computer Science & IT Electrical & Electronics Engineering

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Multi-Seed Robustness Benchmark of Lightweight YOLO Models for Young Crescent Moon Detection under Limited-Data Conditions Bayu Krisna Murti; Kartika Firdausy; Murinto
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1653

Abstract

Visual observation of the young crescent moon is challenging due to its thin and low-contrast appearance. Although YOLO-based object detectors are promising for image-based crescent localization, newer architectures do not automatically generalize well to small grayscale datasets, and prior studies rarely report robustness across repeated training runs. This study benchmarks YOLOv8n, YOLO11n, and YOLO26n for young crescent moon detection under limited-data conditions. A grayscale dataset of 697 images was resized to 640 × 640 pixels, annotated with the single class crescent_moon, and split into training, validation, and test subsets at a fixed 70:20:10 ratio. The three models were trained using the same configuration across five random seeds. Validation results were used to analyze multi-seed robustness, while the fixed 71-image test set was used for CPU-only inference evaluation. YOLO26n achieved the highest validation mAP@50-95 and fitness with the lowest variability, and also achieved the lowest CPU pipeline latency and highest throughput on the test set. These findings show that YOLO26n offers the best trade-off between accuracy and efficiency across the evaluated dataset and CPU-only inference setting. The reported throughput reflects low-frame-rate image-based inference, not real-time video performance. This study provides a reproducible benchmark protocol that combines fixed data splitting, grayscale preprocessing, data integrity checking, multi-seed robustness analysis, and CPU inference profiling.
Fuzzy Logic-Based Classification of Crescent Moon Images Using Contrast and Thickness Yudhiakto Pramudya; Kartika Firdausy; Adi Jufriansah; Okimustava Okimustava; Itsnaini Irvina Khoirunnisa; Bayu Krisna Murti; Rihmah Alifah Hidayah; Murinto Murinto; Muhammad Maulidan
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 2 (2026): April
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i2.14964

Abstract

Accurate determination of the crescent moon (hilal) is crucial for establishing the start of lunar months in the Islamic calendar; however, observations are frequently hindered by daylight conditions, atmospheric disturbances, and subjective visual interpretation. This research proposes a fuzzy logic-based classification system to evaluate crescent moon images using contrast and arc thickness as input parameters, providing a transparent, rule-based alternative to black-box machine learning models for hilal visibility assessment. Images were collected on four distinct observation dates (May 28, 2025, August 5, 2024, September 16, 2023, and May 9, 2021) under varying atmospheric conditions and crescent appearances. Each image underwent pre-processing to extract quantitative measures of arc contrast and thickness, which were subsequently fuzzified using triangular and trapezoidal membership functions. A fuzzy inference system employing expert-defined rules was then used to compute a visibility score for each observation. The resulting visibility scores of 0.4691, 0.4604, 0.4689, and 0.4154, respectively, placed all four observations within the “partially visible” category. These findings demonstrate the system's capability to manage observational ambiguity in daylight conditions, showing potential for reliable classification while still requiring validation on larger datasets and clear non-visibility cases, and offering a transparent and interpretable framework to support more consistent and standardized hilal classification for calendrical purposes.
Co-Authors Adi Jufriansah Itsnaini Irvina Khoirunnisa Kartika Firdausy Muhammad Maulidan Murinto Murinto Okimustava Okimustava Rihmah Alifah Hidayah Yudhiakto Pramudya