Analisis Sentimen pada Tweet Tentang UU Cipta Kerja Menggunakan Algoritma Support Vector Machine dan Particle
Swarm Optimization
Laporan Tugas Akhir
Diajukan Untuk Memenuhi Persyaratan Guna Meraih Gelar Sarjana Informatika Universitas Muhammadiyah Malang
Trifebi Shina Sabrila 201710370311042
Bidang Minat Data Science
PROGRAM STUDI INFORMATIKA FAKULTAS TEKNIK
UNIVERSITAS MUHAMMADIYAH MALANG
2021
vii
KATA PENGANTAR
Dengan memanjatkan puji syukur kehadirat Allah SWT. Atas limpahan rahmat dan hidayah-NYA sehingga peneliti dapat menyelesaikan tugas akhir yang berjudul :
“ANALISIS SENTIMEN PADA TWEET TENTANG UU CIPTA KERJA MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE
DAN PARTICLE SWARM OPTIMIZATION”
Di dalam tulisan ini disajikan pokok-pokok bahasan yang meliputi latar belakang, metode penelitian, dan hasil dan pembahasan yang telah diperoleh dari penelitian yang dilakukan dan telah disimpulkan berdasarkan hasil yang telah diperoleh oleh peneliti.
Peneliti menyadari sepenuhnya bahwa dalam penelitian tugas akhir ini masih banyak kekurangan dan keterbatasan. Oleh karena itu peneliti mengharapkan saran yang membangun agar tulisan ini bermanfaat bagi perkembangan ilmu pengetahuan.
Malang, 25 Mei 2021
Penulis
viii
DAFTAR ISI
LEMBAR PERSETUJUAN... i
LEMBAR PENGESAHAN ... ii
LEMBAR PERNYATAAN ... iii
ABSTRAK ... iv
ABSTRACT ... v
LEMBAR PERSEMBAHAN ... vi
KATA PENGANTAR ... vii
DAFTAR ISI ... viii
DAFTAR GAMBAR ... x
DAFTAR TABEL ... xi
BAB I ... 1
1.1 Latar Belakang... 1
1.2 Rumusan Masalah ... 3
1.3 Tujuan Penelitian ... 3
1.4 Batasan Masalah ... 3
BAB II ... 5
2.1 Penelitian Terdahulu ... 5
2.2 UU Cipta Kerja ... 6
2.3 Twitter ... 6
2.4 Text Mining ... 7
2.5 Analisis Sentimen ... 7
2.6 Preprocessing Data ... 8
2.6.1 Normalisasi ... 8
2.6.2 Case Folding... 8
2.6.3 Cleansing ... 8
2.6.4 Tokenization ... 8
2.6.5 Stopword Removal ... 8
2.6.6 Stemming ... 9
2.6.7 Algoritma Nazief-Adriani ... 9
2.7 Term Frequency-Inverse Document Frequency ... 9
2.8 Particle Swarm Optimization ... 9
ix
2.9 Support Vector Machine... 11
2.10 Confusion Matrix... 11
BAB III... 13
3.1 Pengumpulan Data... 13
3.2 Pelabelan Data ... 14
3.3 Data Preprocessing ... 15
3.4 Term Weighting... 18
3.5 Klasifikasi ... 18
3.5.1 Support Vector Machine ... 19
3.5.2 Particle Swarm Optimization ... 19
3.6 Evaluasi ... 20
3.7 Skenario Pengujian ... 21
BAB IV ... 22
4.1 Kebutuhan Sistem ... 22
4.2 Crawling Data ... 22
4.3 Preprocessing ... 24
4.4 Term Frequency-Inverse Document Frequency ... 26
4.5 Particle Swarm Optimization ... 27
4.6 Support Vector Machine... 29
4.7 Evaluasi ... 33
4.7.1 Klasifikasi Menggunakan Support Vector Machine ... 33
4.7.2 Klasifikasi Menggunakan Support Vector Machine dengan Particle Swarm Optimization ... 33
4.7.3 Perbandingan Hasil Evaluasi Klasifikasi ... 37
BAB V ... 40
5.1 Kesimpulan ... 40
5.2 Saran ... 40
DAFTAR PUSTAKA ... 42
x
DAFTAR GAMBAR
Gambar 1. Alur Kerja Particle Swarm Optimization ... 10
Gambar 2. Contoh Confusion Matrix... 11
Gambar 3. Alur Diagram Metode Penelitian ... 13
Gambar 4. Alur Model Klasifikasi SVM-PSO ... 20
Gambar 5. Tampilan Halaman Key dan Token ... 22
Gambar 6. Source Code Crawling Data Twitter API ... 23
Gambar 7. Source Code Crawling Data Snscrape ... 23
Gambar 8. Source Code Proses Case Folding ... 24
Gambar 9. Source Code Proses Cleansing ... 25
Gambar 10. Source Code Proses Tokenization ... 25
Gambar 11. Source Code Proses Stopword Removal ... 26
Gambar 12. Source Code Proses Stemming ... 26
Gambar 13. Source Code Proses TF-IDF ... 27
Gambar 14. Source Code Import Library ... 27
Gambar 15. Source Code Pembuatan Class PSO... 28
Gambar 16. Source Code Fungsi Optimizing dan Inisialisasi Parameter PSO ... 28
Gambar 17. Source Code Import Library ... 30
Gambar 18. Source Code Menentukan Variabel Klasifikasi ... 30
Gambar 19. Source Code Pengujian dengan K-Fold ... 31
Gambar 20. Source Code Model Klasifikasi SVM ... 31
Gambar 21. Source Code Confusion Matrix, Akurasi, Presisi, dan Recall ... 32
xi
DAFTAR TABEL
Tabel 1. Penelitian Sebelumnya ... 5
Tabel 2. Data yang Berhasil Dicrawling ... 14
Tabel 3. Contoh Data yang Telah Diberi Label ... 14
Tabel 4. Hasil Proses Normalisasi ... 15
Tabel 5. Hasil Proses Case Folding... 15
Tabel 6. Hasil Proses Cleansing ... 16
Tabel 7. Hasil Proses Tokenization ... 16
Tabel 8. Hasil Proses Stopword Removal ... 17
Tabel 9. Hasil Proses Stemming ... 17
Tabel 10. Contoh Hasil Pembobotan TF-IDF ... 18
Tabel 11. Confusion Matrix Skenario Pertama ... 33
Tabel 12. Confusion Matrix Skenario Kedua ... 34
Tabel 13. Confusion Matrix Skenario Ketiga ... 34
Tabel 14. Confusion Matrix Skenario Keempat ... 35
Tabel 15. Confusion Matrix Skenario Kelima ... 35
Tabel 16. Confusion Matrix Skenario Keenam ... 36
Tabel 17. Confusion Matrix Skenario Ketujuh ... 37
Tabel 18. Confusion Matrix Skenario Kedelapan ... 37
Tabel 19. Perbandingan Hasil Evaluasi ... 38
42
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