eprintid: 79613
rev_number: 10
eprint_status: archive
userid: 12460
dir: disk0/00/07/96/13
datestamp: 2026-10-30 04:10:19
lastmod: 2026-10-30 04:10:19
status_changed: 2026-10-30 04:10:19
type: thesis
metadata_visibility: show
contact_email: muh.khabib@uin-suka.ac.id
creators_name: Ekasari Amalia, NIM.: 21106050023
title: RANCANG BANGUN VS CODE EXTENSION UNTUK IDENTIFIKASI KODE YANG BERISIKO MENGHAMBAT MAINTAINABILITY MENGGUNAKAN SOFTWARE METRICS DAN LARGE LANGUAGE MODEL
ispublished: pub
subjects: 005.12.
divisions: Informatika(S1)
full_text_status: restricted
keywords: Visual Studio Code, Extension, Maintainability, Software Metrics,
JavaScript, Large Language Model
note: Muhammad Galih Wonoseto, M.T.
abstract: Maintainability is a software quality attribute related to the ease of understanding,
repairing, modifying, and developing code. This study aims to develop the
Maintainability Risk Analyzer as a VS Code Extension to identify maintainability
risks in JavaScript code using software metrics and provide contextual explanations
through a Large Language Model (LLM). The system was developed using Extreme
Programming (XP), consisting of planning, design, coding, and testing. Static
analysis utilizes the Abstract Syntax Tree (AST) to obtain Halstead Volume,
Cyclomatic Complexity, and Lines of Code (LOC), which are used to calculate the
Visual Studio Maintainability Index (MIVS) and determine the risk level. The
analysis results are displayed directly within the development environment through
Diagnostic, Highlight, Hover, and Show Summary. The Explain Maintainability
Risk feature provides contextual explanations of risk factors and LLM-based
refactoring suggestions. The implementation and testing results show that the
extension can perform maintainability analysis on JavaScript code, display risk
levels and the corresponding metrics, and provide explanations and improvement
suggestions through the LLM-based feature. User evaluation of the LLM-based
feature using the Extended TAM among 101 valid respondents resulted in an overall
mean score of 3.49, which falls into the very high category.
date: 2026-08-28
date_type: published
pages: 118
institution: UIN SUNAN KALIJAGA YOGYAKARTA
department: FAKULTAS SAINS DAN TEKNOLOGI
thesis_type: skripsi
thesis_name: other
citation:   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.   
document_url: https://digilib.uin-suka.ac.id/id/eprint/79613/1/21106050023_BAB-I_IV-atau-V_DAFTAR-PUSTAKA.pdf
document_url: https://digilib.uin-suka.ac.id/id/eprint/79613/2/21106050023_BAB-II_sampai_SEBELUM-BAB-TERAKHIR.pdf