Analytic Predictive of Crescent Sighting Using Astronomical Data-Based Multinomial Logistic Regression in Indonesia
DOI:
https://doi.org/10.70609/g-tech.v9i4.8246Keywords:
Classification, Elongation, Hilal visibility, Multinomial Logistic RegressionAbstract
This research aims to develop and validate a sophisticated crescent visibility classification model in Indonesia. Multinomial Logistic Regression (MLR) was chosen for its capability to provide clear model interpretation through coefficient analysis. Utilizing comprehensive observational data (2021-2025) from Indonesia's Meteorology, Climatology, and Geophysics Agency (BMKG), the study comprised 2210 data points. The model classifies visibility into three categories (Dark, Faint, and Bright) based on defined elongation thresholds. The final predictor variables used were azimuth difference, moon altitude, and elongation. Analysis of the optimal model's (Model A3) coefficients revealed azimuth difference and elongation as the most dominant predictors, marked by exceptionally large positive coefficients (12.050 and 12.018, respectively) for classifying the 'Faint' category. After data preprocessing and systematic optimization ('saga' solver, L2 penalty), the optimal model (A3, C=100) demonstrated exceptional performance with an outstanding F1-Score of 99.10%. These findings strongly validate MLR's effectiveness for elongation-based crescent visibility classification and highlight its substantial potential as a reliable foundation for objective decision-making.
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Copyright (c) 2025 Tomy Ivan Sugiharto, Mokhamad Amin Hariyadi, Totok Chamidy, Irwan Budi Santoso, Cahyo Crysdian, Ahmad Zarkoni, Ma'muri Ma'muri, Syahreni Syahreni
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