{
      "department": "FAKULTAS SAINS DAN TEKNOLOGI",
      "subjects": [
        515.6
      ],
      "eprintid": 76851,
      "thesis_type": "skripsi",
      "date": "2026-05-20",
      "userid": 12460,
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            "placement": 1,
            "eprintid": 76851,
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                  "filename": "22106010089_BAB-I_IV-atau-V_DAFTAR-PUSTAKA.pdf"
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            ],
            "content": "published",
            "rev_number": 3,
            "uri": "http:\/\/digilib.uin-suka.ac.id\/id\/document\/1057285",
            "main": "22106010089_BAB-I_IV-atau-V_DAFTAR-PUSTAKA.pdf",
            "mime_type": "application\/pdf",
            "docid": 1057285,
            "format": "text",
            "security": "public",
            "pos": 1,
            "formatdesc": "OPTIMISASI ANALISIS REGRESI LOGISTIK LASSO MENGGUNAKAN BAYESIAN INFORMATION CRITERION (BIC) (STUDI KASUS : FAKTOR PENYEBAB PERCERAIAN DI INDONESIA TAHUN 2024)"
          },
          {
            "language": "id",
            "placement": 2,
            "eprintid": 76851,
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                  "uri": "http:\/\/digilib.uin-suka.ac.id\/id\/file\/1832194",
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                  "filename": "22106010089_BAB-II_sampai_SEBELUM-BAB-TERAKHIR.pdf"
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            ],
            "content": "published",
            "rev_number": 3,
            "uri": "http:\/\/digilib.uin-suka.ac.id\/id\/document\/1057286",
            "main": "22106010089_BAB-II_sampai_SEBELUM-BAB-TERAKHIR.pdf",
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            "pos": 2,
            "formatdesc": "OPTIMISASI ANALISIS REGRESI LOGISTIK LASSO MENGGUNAKAN BAYESIAN INFORMATION CRITERION (BIC) (STUDI KASUS : FAKTOR PENYEBAB PERCERAIAN DI INDONESIA TAHUN 2024)"
          }
      ],
      "rev_number": 10,
      "creators": [
        {
          "name": {
            "lineage": null,
            "given": "NIM.: 22106010089",
            "honourific": null,
            "family": "Mutiara Nur Amalina"
          }
        }
      ],
      "dir": "disk0\/00\/07\/68\/51",
      "keywords": "Multikolinearitas, Regresi Logistik, LASSO, BIC, Perceraian",
      "lastmod": "2026-06-22 02:09:07",
      "ispublished": "pub",
      "metadata_visibility": "show",
      "date_type": "published",
      "eprint_status": "archive",
      "status_changed": "2026-06-22 02:09:07",
      "datestamp": "2026-06-22 02:09:07",
      "uri": "http:\/\/digilib.uin-suka.ac.id\/id\/eprint\/76851",
      "thesis_name": "other",
      "note": "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": "Divorce is a social issue influenced by various contributing factors and shows\r\ndifferent patterns across provinces. This study aims to classify divorce levels in Indonesia\r\nin 2024 using LASSO logistic regression with optimal parameter selection\r\nbased on the Bayesian Information Criterion (BIC). The data used in this study are\r\nsecondary data from the Statistics Indonesia (BPS), covering 34 provinces in Indonesia.\r\nThe response variable was categorized into two groups, namely low and\r\nhigh divorce levels, based on the divorce rate per 1,000 population. Meanwhile, the\r\npredictor variables consisted of 13 factors causing divorce. The analysis began with\r\nthe development of an initial logistic regression model, followed by multicollinearity\r\ntesting, variable standardization, the application of the LASSO method, and the\r\nselection of the optimal λ value using BIC. The results showed that the initial logistic\r\nregression model experienced multicollinearity, as all variables had VIF values\r\ngreater than 10. Through the LASSO-BIC method, the optimal value obtained was\r\nλBIC = 0.003914, with a minimum BIC value of 26.77. The final model retained\r\nsix variables, namely gambling, abandonment by one spouse, imprisonment of one\r\nspouse, polygamy, physical disability\/illness, and continuous disputes or conflicts.\r\nCompared with the initial model, LASSO-BIC produced a simpler model, reducing\r\nthe number of parameters from 14 to 7 and decreasing the BIC value from 66.055\r\nto 26.770.",
      "type": "thesis",
      "title": "OPTIMISASI ANALISIS REGRESI LOGISTIK LASSO MENGGUNAKAN BAYESIAN INFORMATION CRITERION (BIC) (STUDI KASUS : FAKTOR PENYEBAB PERCERAIAN DI INDONESIA TAHUN 2024)",
      "institution": "UIN SUNAN KALIJAGA YOGYAKARTA",
      "pages": 117
    }