ANALISIS SENTIMEN TERHADAP PROGRAM INDONESIA PINTAR (PIP) PADA APLIKASI X DENGAN MENGGUNAKAN ALGORITMA KNEAREST NEIGHBROS (KNN) DAN SUPPORT VECTOR MACHINE (SVM)

SAPUTRA, AHMAD MAHFUDH ALWI (2023) ANALISIS SENTIMEN TERHADAP PROGRAM INDONESIA PINTAR (PIP) PADA APLIKASI X DENGAN MENGGUNAKAN ALGORITMA KNEAREST NEIGHBROS (KNN) DAN SUPPORT VECTOR MACHINE (SVM). S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

The Indonesia Smart Program (PIP) is a key government initiative designed to expand access to education for students from underprivileged families by providing financial assistance. This research focuses on analyzing public sentiment regarding the implementation of PIP using data collected from the social media platform X (formerly Twitter). The data underwent cleaning and pre-processing before being analyzed using machine learning algorithms: K-Nearest Neighbors (KNN) and Support Vector Machine (SVM). KNN was used to classify sentiments based on proximity to training data, while SVM aimed to identify more complex sentiment patterns with higher accuracy. The study aims to uncover public perceptions of PIP and determine which aspects receive the most attention, whether positive or negative. The results are expected to provide valuable insights for policymakers in improving the effectiveness and public acceptance of the program. Keywords: Indonesia Smart Program, Sentiment Analysis, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Social Media X Program Indonesia Pintar (PIP) merupakan salah satu program prioritas pemerintah Indonesia yang bertujuan untuk memperluas akses pendidikan bagi siswa dari keluarga kurang mampu melalui pemberian bantuan dana pendidikan. Penelitian ini berfokus pada analisis sentimen masyarakat terhadap pelaksanaan PIP dengan memanfaatkan data dari media sosial X (dahulu Twitter). Data yang dikumpulkan dianalisis menggunakan algoritma machine learning yaitu K-Nearest Neighbors (KNN) dan Support Vector Machine (SVM). Setelah melalui proses pembersihan dan pre-processing, data diklasifikasikan menjadi sentimen positif dan negatif. KNN digunakan untuk klasifikasi berdasarkan kedekatan data, sementara SVM digunakan untuk mendeteksi pola sentimen yang lebih kompleks dan akurat. Hasil analisis ini diharapkan dapat memberikan wawasan mengenai persepsi publik terhadap PIP serta aspek-aspek yang paling banyak mendapat tanggapan. Temuan penelitian ini dapat menjadi dasar evaluasi bagi pemerintah dalam merumuskan kebijakan lanjutan yang lebih tepat sasaran dan diterima masyarakat. Kata kunci: Program Indonesia Pintar, Analisis Sentimen, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Media Sosial X

Item Type:	Thesis (S1)
Call Number CD:	FIK/INFO. 26 001
NIM/NIDN Creators:	41521010043
Uncontrolled Keywords:	Program Indonesia Pintar, Analisis Sentimen, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Media Sosial X
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 > 006.7 Multimedia Systems/Sistem-sistem Multimedia > 006.75 Social Multimedia/Multimedia Social 300 Social Science/Ilmu-ilmu Sosial > 300. Social Science/Ilmu-ilmu Sosial > 303 Social Process/Proses Sosial > 303.3 Coordination and Control/Koordinasi dan Kontrol > 303.38 Public Opinion/Opini Publik 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:	28 Jan 2026 01:33
Last Modified:	28 Jan 2026 01:33
URI:	http://repository.mercubuana.ac.id/id/eprint/100748

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