SISTEM ANALISIS SENTIMEN KOMENTAR TIKTOK TENTANG PROGRAM MAKAN BERGIZI GRATIS BERBASIS WEB MENGGUNAKAN DEEP LEARNING ARSITEKTUR BIDIRECTIONAL LONG SHORT-TERM MEMORY (BILSTM)

RIZADI, DAVINDRA GIVANO KEVIN (2026) SISTEM ANALISIS SENTIMEN KOMENTAR TIKTOK TENTANG PROGRAM MAKAN BERGIZI GRATIS BERBASIS WEB MENGGUNAKAN DEEP LEARNING ARSITEKTUR BIDIRECTIONAL LONG SHORT-TERM MEMORY (BILSTM). S1 thesis, Universitas PGRI Madiun.

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Abstract

The Free Nutritious Meal Program has become one of the most discussed topics on TikTok, making sentiment analysis necessary to identify public opinion. This study aims to develop a web-based sentiment analysis system using the Bidirectional Long Short-Term Memory (BiLSTM) method to classify comments into positive, negative, and neutral categories. The system was developed using the Waterfall method with React.js, FastAPI, TensorFlow, and Playwright. The model achieved an accuracy of 95.88%, while Black Box Testing showed that all system functions operated as expected. In addition, the System Usability Scale (SUS) evaluation obtained an average score of 83.25, indicating that the system has excellent usability. Therefore, the proposed system can assist users in analyzing public opinion quickly, accurately, and efficiently.

Item Type:	Thesis/Skripsi/Tugas Akhir (S1)
Kata Kunci:	Analisis Sentimen, TikTok, Program Makan Bergizi Gratis, Bidirectional Long Short-Term Memory (BiLSTM). Sentiment Analysis, TikTok, Free Nutritious Meal Program, Bidirectional Long Short-Term Memory (BiLSTM).
Subjects:	Q Science > Q Science (General) T Technology > T Technology (General) Z Bibliography. Library Science. Information Resources > ZA Information resources Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources
Divisions:	Fakultas Teknik > Teknik Informatika
Depositing User:	KEVIN GIVANO DAVINDRA
Date Deposited:	17 Oct 2026 02:55
Last Modified:	17 Oct 2026 02:55
URI:	http://eprint.unipma.ac.id/id/eprint/10329

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