{
      "department": "FAKULTAS SAINS DAN TEKNOLOGI",
      "subjects": [
        "005.12."
      ],
      "eprintid": 79613,
      "thesis_type": "skripsi",
      "date": "2026-08-28",
      "userid": 12460,
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            "placement": 1,
            "eprintid": 79613,
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                  "filename": "21106050023_BAB-I_IV-atau-V_DAFTAR-PUSTAKA.pdf"
                }
            ],
            "content": "published",
            "rev_number": 3,
            "uri": "http:\/\/digilib.uin-suka.ac.id\/id\/document\/1087695",
            "main": "21106050023_BAB-I_IV-atau-V_DAFTAR-PUSTAKA.pdf",
            "mime_type": "application\/pdf",
            "docid": 1087695,
            "format": "text",
            "security": "public",
            "pos": 1,
            "formatdesc": "RANCANG BANGUN VS CODE EXTENSION UNTUK IDENTIFIKASI KODE YANG BERISIKO MENGHAMBAT MAINTAINABILITY MENGGUNAKAN SOFTWARE METRICS DAN LARGE LANGUAGE MODEL"
          },
          {
            "language": "id",
            "placement": 2,
            "eprintid": 79613,
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                  "filename": "21106050023_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\/1087696",
            "main": "21106050023_BAB-II_sampai_SEBELUM-BAB-TERAKHIR.pdf",
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            "pos": 2,
            "formatdesc": "RANCANG BANGUN VS CODE EXTENSION UNTUK IDENTIFIKASI KODE YANG BERISIKO MENGHAMBAT MAINTAINABILITY MENGGUNAKAN SOFTWARE METRICS DAN LARGE LANGUAGE MODEL"
          }
      ],
      "rev_number": 10,
      "creators": [
        {
          "name": {
            "lineage": null,
            "given": "NIM.: 21106050023",
            "honourific": null,
            "family": "Ekasari Amalia"
          }
        }
      ],
      "dir": "disk0\/00\/07\/96\/13",
      "keywords": "Visual Studio Code, Extension, Maintainability, Software Metrics,\r\nJavaScript, Large Language Model",
      "lastmod": "2026-10-30 04:10:19",
      "ispublished": "pub",
      "metadata_visibility": "show",
      "date_type": "published",
      "eprint_status": "archive",
      "status_changed": "2026-10-30 04:10:19",
      "datestamp": "2026-10-30 04:10:19",
      "uri": "http:\/\/digilib.uin-suka.ac.id\/id\/eprint\/79613",
      "thesis_name": "other",
      "note": "Muhammad Galih Wonoseto, M.T.",
      "full_text_status": "restricted",
      "contact_email": "muh.khabib@uin-suka.ac.id",
      "divisions": [
        "Informatika(S1)"
      ],
      "abstract": "Maintainability is a software quality attribute related to the ease of understanding,\r\nrepairing, modifying, and developing code. This study aims to develop the\r\nMaintainability Risk Analyzer as a VS Code Extension to identify maintainability\r\nrisks in JavaScript code using software metrics and provide contextual explanations\r\nthrough a Large Language Model (LLM). The system was developed using Extreme\r\nProgramming (XP), consisting of planning, design, coding, and testing. Static\r\nanalysis utilizes the Abstract Syntax Tree (AST) to obtain Halstead Volume,\r\nCyclomatic Complexity, and Lines of Code (LOC), which are used to calculate the\r\nVisual Studio Maintainability Index (MIVS) and determine the risk level. The\r\nanalysis results are displayed directly within the development environment through\r\nDiagnostic, Highlight, Hover, and Show Summary. The Explain Maintainability\r\nRisk feature provides contextual explanations of risk factors and LLM-based\r\nrefactoring suggestions. The implementation and testing results show that the\r\nextension can perform maintainability analysis on JavaScript code, display risk\r\nlevels and the corresponding metrics, and provide explanations and improvement\r\nsuggestions through the LLM-based feature. User evaluation of the LLM-based\r\nfeature using the Extended TAM among 101 valid respondents resulted in an overall\r\nmean score of 3.49, which falls into the very high category.",
      "type": "thesis",
      "title": "RANCANG BANGUN VS CODE EXTENSION UNTUK IDENTIFIKASI KODE YANG BERISIKO MENGHAMBAT MAINTAINABILITY MENGGUNAKAN SOFTWARE METRICS DAN LARGE LANGUAGE MODEL",
      "institution": "UIN SUNAN KALIJAGA YOGYAKARTA",
      "pages": 118
    }