LAKSONO, AGUNG WISNU (2025) ANALISIS SENTIMEN DAN GRAPH NETWORK: REAKSI PUBLIK TERHADAP PUTUSAN MAHKAMAH KONSTITUSI (MK) NOMOR. 60/PUU-XXII/2024. S1 thesis, Universitas Mercu Buana Jakarta - Menteng.
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Abstract
Putusan Mahkamah Konstitusi (MK) No. 60/PUU-XXII/2024 tentang penghapusan ambang batas pencalonan kepala daerah memicu berbagai respons publik di media sosial. Penelitian ini bertujuan untuk menganalisis reaksi publik menggunakan pendekatan Analisis Sentimen dan Graph Network Analysis. Data diperoleh melalui web crawling dan API streaming, kemudian diproses melalui tahapan preprocessing, klasifikasi sentimen, dan pemetaan jaringan kata. Algoritma Naïve Bayes menghasilkan akurasi sebesar 90%, dengan f1-score tertinggi pada sentimen positif (95%) namun sangat rendah pada netral (7%), menunjukkan ketimpangan klasifikasi antar kelas. Model Support Vector Machine (SVM) menunjukkan pola serupa dengan akurasi 90%, recall 100% untuk kelas positif, dan hanya 5% untuk kelas netral. Hal ini mengindikasikan dominasi klasifikasi terhadap sentimen positif. Sementara itu, visualisasi graph network mengidentifikasi kata-kata seperti mahkamah, konstitusi, pilkada, partai, dan putus sebagai simpul sentral, menunjukkan fokus utama publik pada aspek hukum dan politik. Klaster semantik seperti syarat, aturan, dan ambang turut memperlihatkan dinamika opini publik terhadap teknis pencalonan. Gabungan metode ini memberikan pemahaman yang utuh secara kuantitatif dan struktural terhadap wacana digital terkait Putusan MK 60/PUU-XXII/2024. The Constitutional Court (MK) Decision No. 60/PUU-XXII/2024 regarding the removal of the regional head election threshold sparked various public responses on social media. This study aims to analyze public reactions using Sentiment Analysis and Graph Network Analysis approaches. Data were collected through web crawling and API streaming with relevant keywords, followed by preprocessing, sentiment classification, and word network mapping. The Naïve Bayes algorithm achieved an accuracy of 90%, with the highest f1-score in the positive sentiment class (95%) and a very low score in the neutral class (7%), indicating a class imbalance issue. The Support Vector Machine (SVM) model showed similar results, with 90% accuracy, 100% recall for positive sentiment, and only 5% recall for neutral sentiment, highlighting the model’s tendency to overclassify positive sentiments. Meanwhile, the graph network visualization identified key nodes such as constitutional, court, election, party, and decision, reflecting the public's main focus on legal and political issues. Semantic clusters like requirements, rules, and threshold also emerged, showing public discourse on technical candidacy matters. The combination of these methods provides a comprehensive understanding of digital public opinion, both quantitatively through sentiment classification and structurally through word network relations.
| Item Type: | Thesis (S1) |
|---|---|
| NIM/NIDN Creators: | 41521120023 |
| Uncontrolled Keywords: | Reaksi publik, media sosial, analisis sentimen, graph network, Naïve Bayes, SVM Public reaction, social media, sentiment analysis, graph network, Naïve Bayes |
| 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 |
| Divisions: | Fakultas Ilmu Komputer > Informatika |
| Depositing User: | Maulana Arif Hidayat |
| Date Deposited: | 05 Aug 2025 01:54 |
| Last Modified: | 05 Aug 2025 01:54 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/96551 |
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