Analisis Sentimen Masyarakat Terhadap Hasil Quick Count Pilpres 2024 Pada Media Sosial Instagram Menggunakan Metode Support Vector Machine (Svm)

Setiawan, Yohanes Don Bosco (2024) Analisis Sentimen Masyarakat Terhadap Hasil Quick Count Pilpres 2024 Pada Media Sosial Instagram Menggunakan Metode Support Vector Machine (Svm). S1 Sistem Informasi thesis, STMIK Widya Cipta Dharma.

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Abstract

Yohanes Don Bosco Setiawan, 2024, Analisis Sentimen Masyarakat Terhadap Hasil Quick Count Pemilihan Presiden 2024 Pada Media Sosial Instagram Menggunakan Metode Support Vector Machine (SVM) di Komisi Pemilihan Umum Kota Samarinda. Skripsi jurusan Sistem Informasi, Sekolah Tinggi Manajemen Informatika dan Komputer Widya Cipta Dharma, Pembimbing Utama Bapak Kusno Harianto, S.Kom., M.Kom, Pembimbing Pendamping Ibu Amelia Yusnita, S.Kom., M.Kom. Kata kunci: Analisis Sentimen, Quick Count, Pilpres 2024, Media Sosial, Instagram, Support Vector Machine (SVM) Penelitian ini bertujuan untuk menganalisis sentimen masyarakat terhadap hasil quick count Pemilihan Presiden (Pilpres) 2024 pada media sosial Instagram menggunakan metode Support Vector Machine (SVM). Media sosial, khususnya Instagram, menjadi platform penting untuk mengamati reaksi masyarakat terhadap berbagai isu, termasuk politik. Dalam penelitian ini, data komentar Instagram dikumpulkan dan dianalisis untuk mengidentifikasi sentimen positif, negatif, dan netral. Proses analisis meliputi pengumpulan data, pre-processing, pelatihan model menggunakan SVM, Pemodelan dengan SVM, Confusion Matrix dan Visualisasi Hasil penelitian, yang dilakukan di Komisi Pemilihan Umum Kota Samarinda, menunjukkan bahwa metode SVM mampu mengklasifikasikan sentimen dengan tingkat akurasi sebesar 81%, presisi sebesar 79%, dan recall sebesar 80%. Penelitian ini diharapkan dapat memberikan kontribusi dalam pengembangan metode analisis sentimen yang lebih canggih dan efektif serta menjadi landasan bagi penelitian lebih lanjut dalam memahami dinamika opini publik dalam konteks politik modern yang semakin kompleks. ============================================================ Yohanes Don Bosco Setiawan, 2024, Sentiment Analysis of Public Reactions to Quick Count Results in the 2024 Presidential Election on Instagram Using Support Vector Machine (SVM) Method at the General Election Commission of Samarinda City. Bachelor's Thesis, Information Systems Department, Widya Cipta Dharma School of Management and Computer Science, Primary Supervisor Mr. Kusno Harianto, S.Kom., M.Kom, Assistant Supervisor Ms. Amelia Yusnita, S.Kom., M.Kom. Keywords: Sentiment Analysis, Quick Count, 2024 Presidential Election, Social Media, Instagram, Support Vector Machine (SVM) This research aims to analyze public sentiment regarding the quick count results of the 2024 Presidential Election on Instagram using the Support Vector Machine (SVM) method. Social media, particularly Instagram, has become an important platform for observing public reactions to various issues, including politics. In this study, Instagram comment data was collected and analyzed to identify positive, negative, and neutral sentiments. The analysis process includes data collection, pre-processing, model training using SVM, SVM modeling, Confusion Matrix, and Visualization. The research, conducted at the General Election Commission of Samarinda City, indicates that the SVM method was able to classify sentiment with an accuracy rate of 81%, precision of 79%, and recall of 80%. This study is expected to contribute to the development of more advanced and effective sentiment analysis methods and serve as a foundation for further research in understanding the dynamics of public opinion in the increasingly complex context of modern politics.

Item Type: Thesis (S1 Sistem Informasi)
Additional Information: Pembimbing 1 : Kusno Harianto, S.Kom., M.Kom Pembimbing 2 : Amelia Yusnita, S.Kom., M.Kom
Uncontrolled Keywords: Analisis Sentimen, Quick Count, Pilpres 2024, Media Sosial, Instagram, Support Vector Machine (SVM)
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Sistem Informasi
Depositing User: Yohanes Don Bosco Setiawan
Date Deposited: 07 Aug 2024 02:13
Last Modified: 07 Aug 2024 02:13
URI: http://repository.wicida.ac.id/id/eprint/5546

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