<mets:mets OBJID="eprint_79629" LABEL="Eprints Item" xsi:schemaLocation="http://www.loc.gov/METS/ http://www.loc.gov/standards/mets/mets.xsd http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-3.xsd" xmlns:mets="http://www.loc.gov/METS/" xmlns:mods="http://www.loc.gov/mods/v3" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><mets:metsHdr CREATEDATE="2026-11-13T13:18:14Z"><mets:agent ROLE="CUSTODIAN" TYPE="ORGANIZATION"><mets:name>Institutional Repository UIN Sunan Kalijaga Yogyakarta</mets:name></mets:agent></mets:metsHdr><mets:dmdSec ID="DMD_eprint_79629_mods"><mets:mdWrap MDTYPE="MODS"><mets:xmlData><mods:titleInfo><mods:title>RANCANG BANGUN EKSTENSI VISUAL STUDIO CODE UNTUK OTOMATISASI PENGEMBALIAN FOKUS PADA AI-ASSISTED PROGRAMMING</mods:title></mods:titleInfo><mods:name type="personal"><mods:namePart type="given">NIM.: 22106050045</mods:namePart><mods:namePart type="family">Akhmad Ikhsan Sayyid Al Haq</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:abstract>The use of artificial intelligence-based programming assistants such as GitHub Copilot Chat allows developers to switch to other applications while waiting for the AI response to finish processing. This condition requires developers to manually switch windows to return to Visual Studio Code once the response is available. This research aims to design and build a Visual Studio Code extension named copilot-focus that automates the window focus return after the GitHub Copilot Chat activity is completed. The extension development was conducted using the Extreme Programming (XP) methodology through three iterations. The constructed system consists of a periodic local log file monitoring mechanism, log data parsing using regular expressions, Copilot activity completion detection, and a platform-tailored focus return service utilizing the Win32 API on Windows and AppleScript via osascript on macOS. Supporting features such as visual notifications, audio notifications, and configuration settings were also implemented. Testing was conducted using the Black-Box Testing method based on four scenarios derived from the acceptance criteria, performed both internally and by involving 13 independent testers. The test results demonstrated a functional success rate of 92%, wherein the extension successfully returned the window focus without manual user intervention. The independent evaluation also validated the execution of a protective fallback mechanism (in the form of a blinking Taskbar icon on Windows) when the operating system blocked forced focus switching, and confirmed the necessity of manual accessibility permissions on macOS. Nevertheless, in some trials, a condition was found where log monitoring did not run immediately upon initial initialization, thus requiring an extension reload or restarting Visual Studio Code.</mods:abstract><mods:classification authority="lcc">005.12 Software System Analysis and Design/Sistem Analisa dan Desain Perangkat Lunak</mods:classification><mods:originInfo><mods:dateIssued encoding="iso8061">2026-08-31</mods:dateIssued></mods:originInfo><mods:originInfo><mods:publisher>UIN SUNAN KALIJAGA YOGYAKARTA;FAKULTAS SAINS DAN TEKNOLOGI</mods:publisher></mods:originInfo><mods:genre>Thesis</mods:genre></mets:xmlData></mets:mdWrap></mets:dmdSec><mets:amdSec ID="TMD_eprint_79629"><mets:rightsMD ID="rights_eprint_79629_mods"><mets:mdWrap MDTYPE="MODS"><mets:xmlData><mods:useAndReproduction>
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