{
      "department": "FAKULTAS SAINS DAN TEKNOLOGI",
      "subjects": [
        515.6
      ],
      "eprintid": 78703,
      "thesis_type": "skripsi",
      "date": "2026-08-03",
      "userid": 12460,
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            "formatdesc": "OPTIMALISASI REGRESI BINOMIAL NEGATIF DENGAN REGULARISASI LASSO MENGGUNAKAN K-FOLD CROSS VALIDATION (STUDI KASUS : JUMLAH KASUS DBD DI JAWA TENGAH TAHUN 2024)"
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            "content": "published",
            "rev_number": 3,
            "uri": "http:\/\/digilib.uin-suka.ac.id\/id\/document\/1078971",
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      "rev_number": 10,
      "creators": [
        {
          "name": {
            "lineage": null,
            "given": "NIM.: 22106010081",
            "honourific": null,
            "family": "Robit Ma’rufi Sabila"
          }
        }
      ],
      "dir": "disk0\/00\/07\/87\/03",
      "keywords": "Binomial Negatif, Lasso, K-Fold Cross Validation, Demam Berdarah (DBD)",
      "lastmod": "2026-10-06 03:30:14",
      "ispublished": "pub",
      "metadata_visibility": "show",
      "date_type": "published",
      "eprint_status": "archive",
      "status_changed": "2026-10-06 03:30:14",
      "datestamp": "2026-10-06 03:30:14",
      "uri": "http:\/\/digilib.uin-suka.ac.id\/id\/eprint\/78703",
      "thesis_name": "other",
      "note": "Aulia Khifah Futhona, M.Sc. dan Sri Utami Zuliana, S.Si., M.Sc., Ph.D.",
      "full_text_status": "restricted",
      "contact_email": "muh.khabib@uin-suka.ac.id",
      "divisions": [
        "jur_mat"
      ],
      "abstract": "Regression is one of the statistical methods used to analyze the relationship\r\nbetween dependent and independent variables. For count data, the Poisson\r\nregression model is commonly used. Poisson regression assumes equidispersion,\r\nwhere the mean and variance of the dependent variable are equal. However, in\r\npractice, Poisson regression often encounters overdispersion, where the variance\r\nis greater than the mean, making Poisson regression less appropriate. One method\r\nfor addressing overdispersion is Negative Binomial regression, which has a\r\ndispersion parameter that can accommodate overdispersion in the data. In addition\r\nto overdispersion, multicollinearity, characterized by high correlations among\r\nindependent variables, can cause regression coefficients to become unstable. The\r\nuse of high-dimensional data can also increase the risk of overfitting. Therefore,\r\na variable selection method is needed to produce a simpler and more efficient\r\nmodel. To address these issues, Lasso regularization is applied by imposing an L1\r\npenalty on the regression coefficients, thereby producing more stable parameter\r\nestimates while simultaneously performing variable selection. This study uses the\r\nnumber of Dengue Hemorrhagic Fever (DHF) cases in Central Java in 2024 as the\r\ndependent variable and 12 independent variables related to health, sanitation, and\r\nsocioeconomic factors. This study aims to apply Negative Binomial regression\r\nwith Lasso regularization using K-Fold Cross Validation. The results show that the\r\noptimal λ value of 0.4247 selects one variable, namely the Number of Community\r\nHealth Centers (JSKP), resulting in a more parsimonious and efficient model.",
      "type": "thesis",
      "title": "OPTIMALISASI REGRESI BINOMIAL NEGATIF DENGAN REGULARISASI LASSO MENGGUNAKAN K-FOLD CROSS VALIDATION (STUDI KASUS : JUMLAH KASUS DBD DI JAWA TENGAH TAHUN 2024)",
      "institution": "UIN SUNAN KALIJAGA YOGYAKARTA",
      "pages": 123
    }