<mets:mets OBJID="eprint_11237" 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="2026-10-31T00:45:17Z"><mets:agent ROLE="CUSTODIAN" TYPE="ORGANIZATION"><mets:name>Repository Universitas Pakuan</mets:name></mets:agent></mets:metsHdr><mets:dmdSec ID="DMD_eprint_11237_mods"><mets:mdWrap MDTYPE="MODS"><mets:xmlData><mods:titleInfo><mods:title>PROTOTIPE SISTEM PEMADAM API OTOMATIS&#13;
BERBASIS YOLOV8 DAN MEKANISME PAN-TILT UNTUK&#13;
LOKALISASI PRESISI SECARA REAL-TIME</mods:title></mods:titleInfo><mods:name type="personal"><mods:namePart type="given">Farhan Triyono</mods:namePart><mods:namePart type="family">Aji</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:abstract>PROTOTIPE SISTEM PEMADAM API OTOMATIS&#13;
BERBASIS YOLOV8 DAN MEKANISME PAN-TILT UNTUK&#13;
LOKALISASI PRESISI SECARA REAL-TIME&#13;
Farhan Triyono Aji¹, Soewarto Hardhienata², Teguh Pujanegara³*&#13;
¹'²'³ Program Studi Ilmu Komputer, Fakultas Matematika dan Ilmu Pengetahuan Alam,&#13;
Universitas Pakuan&#13;
*Email: farhantriyono.065120005@unpak.ac.id&#13;
ABSTRAK&#13;
Kebakaran merupakan bencana yang menimbulkan kerugian materiil maupun jiwa. Sistem&#13;
pemadam otomatis konvensional seperti sprinkler memiliki keterbatasan karena baru aktif saat&#13;
suhu mencapai ambang tertentu (57°C–74°C) dan menyemprotkan air secara menyeluruh tanpa&#13;
melokalisasi sumber api secara presisi. Penelitian ini mengusulkan prototipe sistem pemadam&#13;
api otomatis yang mengintegrasikan algoritma deep learning YOLOv8 dengan mekanisme&#13;
pan-tilt berbasis Raspberry Pi secara real-time. Model YOLOv8n dilatih menggunakan 100&#13;
sampel data citra api yang teranotasi. Kamera webcam beresolusi 640x480px digunakan&#13;
sebagai masukan visual. Koordinat centroid dari bounding box hasil prediksi dikonversi menjadi&#13;
sudut pergerakan servo pan-tilt untuk mengarahkan nozzle secara langsung ke titik api. Hasil&#13;
evaluasi pelatihan YOLOv8 mencapai nilai mAP50 sebesar 0,92 dan mAP50-95 sebesar 0,668.&#13;
Pengujian pada lingkungan nyata menunjukkan sistem mampu mendeteksi api dengan&#13;
confidence score rata-rata 0,77 (77%). Tingkat kesalahan rata-rata lokalisasi posisi api adalah&#13;
11,20 mm. Sistem secara keseluruhan berhasil memadamkan api secara real-time dengan&#13;
tingkat keberhasilan sebesar 87,5%. Keterbatasan pemadaman (12,5%) utamanya terjadi pada&#13;
area sudut ruangan akibat pembulatan kepresisian sudut servo. Penggunaan matriks kalibrasi&#13;
terbukti meningkatkan presisi pengarahan nozzle.&#13;
Kata Kunci: YOLOv8, Pan-Tilt, Pemadam Api Otomatis, Raspberry Pi, Computer Vision,&#13;
Lokalisasi Presis</mods:abstract><mods:classification authority="lcc">Ilmu Komputer</mods:classification><mods:originInfo><mods:dateIssued encoding="iso8061">2026-07-12</mods:dateIssued></mods:originInfo><mods:originInfo><mods:publisher>Universitas Pakuan;Fakultas Matematika dan Pengetahuan Alam</mods:publisher></mods:originInfo><mods:genre>Thesis</mods:genre></mets:xmlData></mets:mdWrap></mets:dmdSec><mets:amdSec ID="TMD_eprint_11237"><mets:rightsMD ID="rights_eprint_11237_mods"><mets:mdWrap MDTYPE="MODS"><mets:xmlData><mods:useAndReproduction>
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