Classification-Based on Crescent Elongation Data Using Multinomial Logistic Regression

Classification-Based on Crescent Elongation Data Using Multinomial Logistic Regression | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Classification-Based on Crescent Elongation Data Using Multinomial Logistic Regression Tomy Ivan Sugiharto, Mokhamad Amin Hariyadi, Totok Chamidy, Irwan Budi Santoso, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7588742/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract The determination of the beginning of the Hijri month through the observation of the new moon is an important process that requires scientific support to account for decision-making. This study aims to develop a model for the classification of hilal visibility using Multinomial Logistic Regression (MLR) by utilizing observation data from the Meteorology, Climatology, and Geophysics Agency (BMKG) for the 2021–2025 period. Using data that includes astronomical and geometric parameters such as the Solar azimuth, the Moon's azimuth, the height of the Sun, the height of the Moon, the difference in azimuth, and the elongation. The label assigns the visibility of the new moon into three categories, namely Dark (elongation < 5°), Faint (5° ≤ elongation < 12°), and Light (elongation ≥ 12°). Obtain a total of 2210 lines of existing datasets, then divide the dataset into different training scenarios, namely 60/40, 70/30, and 80/20, with a standardization process before modeling. Optimized the model with the hyperparameter setting of the regularization constant C in the range of 0.001 to 100, using the saga solver and L2 penalty. The test results show that the model's performance improves as the C value increases. Obtained the best configuration at a 60/40 split with C = 100, resulting in an F1-Score of 99.10%, near-perfect accuracy and recall, and a ROC-AUC value close to 1. These findings show that MLR is effective in classifying elongation-based hilal visibility, as well as potentially being used as a basis for technical decision-making in determining the beginning of the Hijri month. However, this study still requires additional validation, such as stratified k-fold assays and cross-site or year testing, to ensure the model's generalization capabilities and avoid potential data leakage. hilal visibility elongation classification multinomial logistic regression Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 21 Oct, 2025 Reviewers invited by journal 18 Sep, 2025 Editor assigned by journal 15 Sep, 2025 Submission checks completed at journal 13 Sep, 2025 First submitted to journal 11 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7588742","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":521856485,"identity":"ef10208f-4b66-4bcb-a502-9f085affc5f9","order_by":0,"name":"Tomy Ivan Sugiharto","email":"","orcid":"","institution":"Universitas Islam Negeri Maulana Malik Ibrahim Malang","correspondingAuthor":false,"prefix":"","firstName":"Tomy","middleName":"Ivan","lastName":"Sugiharto","suffix":""},{"id":521856487,"identity":"d720ca20-7aa4-423b-9886-31a943b0ddd5","order_by":1,"name":"Mokhamad Amin 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This study aims to develop a model for the classification of hilal visibility using Multinomial Logistic Regression (MLR) by utilizing observation data from the Meteorology, Climatology, and Geophysics Agency (BMKG) for the 2021–2025 period. Using data that includes astronomical and geometric parameters such as the Solar azimuth, the Moon's azimuth, the height of the Sun, the height of the Moon, the difference in azimuth, and the elongation. The label assigns the visibility of the new moon into three categories, namely Dark (elongation \u0026lt; 5°), Faint (5° ≤ elongation \u0026lt; 12°), and Light (elongation ≥ 12°). Obtain a total of 2210 lines of existing datasets, then divide the dataset into different training scenarios, namely 60/40, 70/30, and 80/20, with a standardization process before modeling. Optimized the model with the hyperparameter setting of the regularization constant C in the range of 0.001 to 100, using the saga solver and L2 penalty. The test results show that the model's performance improves as the C value increases. Obtained the best configuration at a 60/40 split with C = 100, resulting in an F1-Score of 99.10%, near-perfect accuracy and recall, and a ROC-AUC value close to 1. These findings show that MLR is effective in classifying elongation-based hilal visibility, as well as potentially being used as a basis for technical decision-making in determining the beginning of the Hijri month. However, this study still requires additional validation, such as stratified k-fold assays and cross-site or year testing, to ensure the model's generalization capabilities and avoid potential data leakage.\u003c/p\u003e","manuscriptTitle":"Classification-Based on Crescent Elongation Data Using Multinomial Logistic Regression","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-30 13:49:52","doi":"10.21203/rs.3.rs-7588742/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"120412664544370136972910734078689127268","date":"2025-10-21T05:51:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-18T21:34:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-15T09:23:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-13T07:23:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"Pure and Applied Geophysics","date":"2025-09-11T07:05:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"pure-and-applied-geophysics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"paag","sideBox":"Learn more about [Pure and Applied Geophysics](https://www.springer.com/journal/24)","snPcode":"24","submissionUrl":"https://submission.nature.com/new-submission/24/3","title":"Pure and Applied Geophysics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"85648e2b-0114-4db3-8fbe-a581f855b3f5","owner":[],"postedDate":"September 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-30T13:49:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-30 13:49:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7588742","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7588742","identity":"rs-7588742","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}