ANALISIS SENTIMENT MASYARAKAT TERHADAP KEBIJAKAN TABUNGAN PERUMAHAN RAKYAT ( TAPERA) DI APLIKASI X MENGGUNAKAN ALGORITMA MACHINE LEARNING

RIENALDY, RIENALDY (2025) ANALISIS SENTIMENT MASYARAKAT TERHADAP KEBIJAKAN TABUNGAN PERUMAHAN RAKYAT ( TAPERA) DI APLIKASI X MENGGUNAKAN ALGORITMA MACHINE LEARNING. S1 thesis, Universitas Mercu Buana Jakarta.

	Text (HAL COVER) 01 Cover.pdf Download (473kB)
	Text (BAB I) 02 Bab 1.pdf Restricted to Registered users only Download (113kB)
	Text (BAB II) 03 Bab 2.pdf Restricted to Registered users only Download (363kB)
	Text (BAB III) 04 Bab 3.pdf Restricted to Registered users only Download (114kB)
	Text (BAB IV) 05 Bab 4.pdf Restricted to Registered users only Download (904kB)
	Text (BAB V) 06 Bab 5.pdf Restricted to Registered users only Download (30kB)
	Text (DAFTAR PUSTAKA) 07 Daftar Pustaka.pdf Restricted to Registered users only Download (122kB)
	Text (LAMPIRAN) 08 Lampiran.pdf Restricted to Registered users only Download (787kB)

Abstract

The Public Housing Savings Program (TAPERA) designed by the government aims to increase people's access to adequate housing. However, the implementation of this policy has given rise to various public opinions which are important to explain in order to understand public sentiment. This research aims to trigger public sentiment towards the TAPERA policy using data taken from the X application (Twitter). Apart from that, this research also compares the performance of the Support Vector Machine (SVM) and Logistic Regression algorithms in sentiment classification, as well as identifying the main keywords that influence public opinion. Data is collected from the X application (Twitter) using crawling and processed through preprocessing stages such as cleaning, tokenization, and TFIDF. The analysis was carried out using the SVM and Logistic Regression algorithms. Regression provides better results than previous research in analyzing public sentiment. This research is expected to provide valuable insight regarding public perceptions of the TAPERA policy, as well as provide recommendations to the government to increase the effectiveness of this policy. In addition, this research also shows how machine learning algorithms can be utilized for social media-based sentiment analysis Keyword: TAPERA, Analisis Sentimen, Support Vector Machine, Logistic Regression. Program Tabungan Perumahan Rakyat (TAPERA) yang dirancang oleh pemerintah bertujuan untuk meningkatkan akses masyarakat terhadap perumahan yang layak. Meski demikian, pelaksanaan kebijakan ini memunculkan beragam opini publik yang penting untuk dianalisis guna memahami sentimen masyarakat. Penelitian ini bertujuan untuk mengevaluasi sentimen masyarakat terhadap kebijakan TAPERA menggunakan data yang diambil dari aplikasi X (Twitter). Selain itu, penelitian ini juga membandingkan kinerja algoritma Support Vector Machine (SVM) dan Logistic Regression dalam klasifikasi sentimen, serta mengidentifikasi kata kunci utama yang memengaruhi opini publik. Data dikumpulkan dari aplikasi X (Twitter) menggunakan crawling dan diproses melalui tahapan preprocessing seperti cleansing, tokenization, dan TF-IDF. Analisis dilakukan dengan algoritma SVM dan Logistic Regression memberikan hasil yang lebih baik dibandingkan penelitian sebelumnya dalam menganalisis sentiment masyarakat. Penelitian ini diharapkan memberikan wawasan yang berharga terkait persepsi masyarakat terhadap kebijakan TAPERA, sekaligus memberikan rekomendasi kepada pemerintah untuk meningkatkan efektivitas kebijakan tersebut. Selain itu, penelitian ini juga menunjukkan bagaimana algoritma pembelajaran mesin dapat dimanfaatkan untuk analisis sentimen berbasis media sosial. Kata kunci: TAPERA, Analisis Sentimen, Support Vector Machine, Logistic Regression.

Item Type:	Thesis (S1)
Call Number CD:	FIK/INFO. 25 150
NIM/NIDN Creators:	41521010166
Uncontrolled Keywords:	TAPERA, Analisis Sentimen, Support Vector Machine, Logistic Regression.
Subjects:	000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 006 Special Computer Methods/Metode Komputer Tertentu > 006.3 Artificial Intelligence/Kecerdasan Buatan > 006.31 Machine Learning/Pembelajaran Mesin 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 006 Special Computer Methods/Metode Komputer Tertentu > 006.3 Artificial Intelligence/Kecerdasan Buatan > 006.35 Natural Language Processing/Pengolahan Bahasa Alami 500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 510 Mathematics/Matematika > 518 Numerical Analysis/Analisis Numerik, Analisa Numerik > 518.1 Algorithms/Algoritma
Divisions:	Fakultas Ilmu Komputer > Informatika
Depositing User:	khalimah
Date Deposited:	15 Aug 2025 04:21
Last Modified:	15 Aug 2025 04:21
URI:	http://repository.mercubuana.ac.id/id/eprint/96813

Actions (login required)

View Item