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        <dc:title>RANCANG BANGUN VS CODE EXTENSION UNTUK IDENTIFIKASI KODE YANG BERISIKO MENGHAMBAT MAINTAINABILITY MENGGUNAKAN SOFTWARE METRICS DAN LARGE LANGUAGE MODEL</dc:title>
        <dc:creator>Ekasari Amalia, NIM.: 21106050023</dc:creator>
        <dc:subject>005.12 Software System Analysis and Design/Sistem Analisa dan Desain Perangkat Lunak</dc:subject>
        <dc:description>Maintainability is a software quality attribute related to the ease of understanding,&#13;
repairing, modifying, and developing code. This study aims to develop the&#13;
Maintainability Risk Analyzer as a VS Code Extension to identify maintainability&#13;
risks in JavaScript code using software metrics and provide contextual explanations&#13;
through a Large Language Model (LLM). The system was developed using Extreme&#13;
Programming (XP), consisting of planning, design, coding, and testing. Static&#13;
analysis utilizes the Abstract Syntax Tree (AST) to obtain Halstead Volume,&#13;
Cyclomatic Complexity, and Lines of Code (LOC), which are used to calculate the&#13;
Visual Studio Maintainability Index (MIVS) and determine the risk level. The&#13;
analysis results are displayed directly within the development environment through&#13;
Diagnostic, Highlight, Hover, and Show Summary. The Explain Maintainability&#13;
Risk feature provides contextual explanations of risk factors and LLM-based&#13;
refactoring suggestions. The implementation and testing results show that the&#13;
extension can perform maintainability analysis on JavaScript code, display risk&#13;
levels and the corresponding metrics, and provide explanations and improvement&#13;
suggestions through the LLM-based feature. User evaluation of the LLM-based&#13;
feature using the Extended TAM among 101 valid respondents resulted in an overall&#13;
mean score of 3.49, which falls into the very high category.</dc:description>
        <dc:date>2026-08-28</dc:date>
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        <dc:identifier>https://digilib.uin-suka.ac.id/id/eprint/79613/2/21106050023_BAB-II_sampai_SEBELUM-BAB-TERAKHIR.pdf</dc:identifier>
        <dc:identifier>  Ekasari Amalia, NIM.: 21106050023  (2026) RANCANG BANGUN VS CODE EXTENSION UNTUK IDENTIFIKASI KODE YANG BERISIKO MENGHAMBAT MAINTAINABILITY MENGGUNAKAN SOFTWARE METRICS DAN LARGE LANGUAGE MODEL.  Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.   </dc:identifier></oai_dc:dc>
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