ANALISIS SENTIMEN INSTAGRAM DAN FACEBOOK TERHADAP KONDISI LALU LINTAS DI KOTA PALEMBANG MENGGUNAKAN RECURRENT NEURAL NETWORK

AFKARI, MUHAMMAD ARBI AL and Oklilas, Ahmad Fali (2025) ANALISIS SENTIMEN INSTAGRAM DAN FACEBOOK TERHADAP KONDISI LALU LINTAS DI KOTA PALEMBANG MENGGUNAKAN RECURRENT NEURAL NETWORK. Undergraduate thesis, Sriwijaya University.

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Abstract

Penelitian ini bertujuan membangun model klasifikasi kondisi lalu lintas menggunakan data dari sosial media (Instagram dan Facebook) serta data ETLE dengan arsitektur Recurrent Neural Network. Dari total 1.251 data sosial media yang dikumpulkan, sebanyak 719 data dipilih berdasarkan kemunculan kata kunci “macet”, “sedang”, dan “lancar”. Data ini kemudian dibagi menjadi data latih (529) dan data uji (190), serta diproses melalui tahapan preprocessing seperti cleaning, case folding, stemming, tokenization, stopword removal, dan normalisasi. Hasil klasifikasi menunjukkan akurasi tinggi pada data sosial media (96%), namun akurasi kategori “lancar” sangat rendah (18,75%) yang menunjukkan bias terhadap kategori “macet”. Sementara itu, hasil klasifikasi pada data ETLE menunjukkan akurasi stabil sebesar 93% tanpa indikasi bias yang signifikan. Evaluasi melalui pencocokan antar dua sumber data menunjukkan tingkat kesesuaian sebesar 82,25%, mencerminkan performa model yang cukup baik dalam kondisi nyata. Penelitian ini menyimpulkan bahwa model cukup andal, namun perlu perbaikan untuk meningkatkan sensitivitas terhadap kategori minoritas.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Computer System, Sentiment Analyis, Recurren Neural Network
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) > TA329-348 Engineering mathematics. Engineering analysis > TA347.F5C4665 Finite Element Method, Computer System Engineering Mathematics,
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Muhammad Arbi Al Afkari
Date Deposited:	22 Jul 2025 08:09
Last Modified:	22 Jul 2025 08:09
URI:	http://repository.unsri.ac.id/id/eprint/179387

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