(KLIK - S4) - Al Assyam [2023-08-25]

Hasan, Firman Noor (2023) (KLIK - S4) - Al Assyam [2023-08-25]. Analisis Sentimen Twitter Terhadap Perpindahan Ibu Kota Negara Ke IKN Nusantara Menggunakan Orange Data Mining, 4 (1). pp. 341-349. ISSN 2723-3898

Preview	Text 1. Cover.pdf Download (57kB) | Preview
Preview	Text 2. Editorial Team.pdf Download (498kB) | Preview
Preview	Text 3. Daftar Isi.pdf Download (11MB) | Preview
Preview	Text 4. Artikel.pdf Download (662kB) | Preview
Preview	Text 5. Bukti Hasil Turnitin.pdf Download (2MB) | Preview
Preview	Text 6. LOA - Assyams, Hasan.pdf Download (213kB) | Preview

Abstract

This study uses text mining which involves changing unstructured text to be structured and can be processed by a computer. In order to recognize important new patterns and ideas, several analytical techniques are used, including the text clustering method, Naive Bayes, and Support Vector Machines (SVM). Text Clustering analysis technique, which involves cluster analysis of text-based documents, can assist in categorizing and understanding unstructured text data using machine learning technology and Natural Language Processing (NLP) used in this process. This study aims to evaluate the community's response to the relocation of the national capital to Kalimantan. after going through the cleansing process, namely cleaning punctuation and characters, Transform Case, namely changing letters to lowercase, Tokenization is the process of dividing text sentences or paragraphs into certain parts, Stopwords Reducing the index in the text by removing some verbs, adjectives and other adverbs . The results of the analysis will be displayed in the form of a word cloud with words dominated by Indonesian and then Indonesian and distribution tables. The researcher collects 100 data via Twitter to become a dataset. The results of sentiment analysis with the Naive Bayes Classifier algorithm obtained results, namely 6 forms of emotion which were dominated by surprise (80%) and joy (50%), sadness (15% Sadness), fear (Fear) 10%, disgust (Disgust). ) 0% , angry (Anger) 0%.

Item Type:	Article
Subjects:	T Technology > T Technology (General)
Divisions:	Fakultas Teknik
Depositing User:	Mr Firman Noor Hasan
Date Deposited:	11 Oct 2023 03:17
Last Modified:	11 Oct 2023 03:17
URI:	http://repository.uhamka.ac.id/id/eprint/29406

Actions (login required)

View Item