ARTANA, STEFANY (2026) ANALISIS SENTIMEN NETIZEN TERHADAP KRITIK KEBIJAKAN PEMBAYARAN QRIS MENGGUNAKAN METODE HYBRID CNN-LSTM. S1 thesis, Universitas Mercu Buana Jakarta.
|
Text (HAL COVER)
Cover.pdf Download (661kB) | Preview |
|
![[img]](https://repository.mercubuana.ac.id/style/images/fileicons/text.png) |
Text (BAB I)
BAB 1.pdf Restricted to Registered users only Download (89kB) |
|
|
Text (BAB II)
BAB 2.pdf Restricted to Registered users only Download (173kB) |
|
|
Text (BAB III)
BAB 3.pdf Restricted to Registered users only Download (160kB) |
|
|
Text (BAB IV)
BAB 4.pdf Restricted to Registered users only Download (466kB) |
|
|
Text (BAB V)
BAB 5.pdf Restricted to Registered users only Download (27kB) |
|
|
Text (DAFTAR PUSTAKA)
Daftar Pustaka.pdf Restricted to Registered users only Download (140kB) |
|
|
Text (LAMPIRAN)
Lampiran.pdf Restricted to Registered users only Download (263kB) |
Abstract
The development of digital payment systems in Indonesia has progressed significantly with the introduction of the Quick Response Code Indonesian Standard (QRIS) initiated by Bank Indonesia. QRIS aims to unify various digital payment services into a standardized system to enhance transaction efficiency and financial inclusion. However, this policy has attracted international attention, including criticism from the President of the United States, which sparked diverse responses from Indonesian netizens on social media. This study aims to analyse and classify netizens' sentiments towards the criticism using a deep learningbased approach, combining Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). Data was collected from public comments on two YouTube videos explicitly discussing this issue. Subsequently, text pre-processing, vector representation with Keras Embedding Layer, and training of the hybrid CNN-LSTM model were conducted. Performance evaluation was carried out using accuracy, precision, recall, F1-score, and confusion matrix metrics. The results of this study not only provide an overview of the distribution of public sentiment (positive, neutral, negative) but also demonstrate the effectiveness of the hybrid CNN-LSTM model in processing sequential Indonesian-language data. This research is expected to make a tangible contribution to the development of NLP-based sentiment analysis systems and serve as a reference for understanding public opinion on national strategic policies in the digital age. Keywords: QRIS, Sentiment Analysis, Deep Learning, CNN, LSTM. Perkembangan sistem pembayaran digital di Indonesia mengalami kemajuan pesat dengan hadirnya Quick Response Code Indonesian Standard (QRIS) yang diinisiasi oleh Bank Indonesia. QRIS bertujuan untuk menyatukan berbagai layanan pembayaran dalam satu sistem terstandarisasi guna meningkatkan efisiensi transaksi dan inklusi keuangan. Namun, kebijakan ini menuai sorotan internasional, termasuk kritik dari Presiden Amerika Serikat, yang memicu beragam tanggapan dari masyarakat Indonesia di media sosial. Penelitian ini bertujuan untuk menganalisis dan mengklasifikasikan sentimen netizen terhadap kritik tersebut menggunakan pendekatan berbasis deep learning, yakni gabungan Convolutional Neural Network (CNN) dan Long Short-Term Memory (LSTM). Data dikumpulkan melalui komentar publik pada dua video YouTube yang membahas isu ini secara eksplisit. Selanjutnya, dilakukan proses pra-pemrosesan teks, representasi vektor menggunakan Keras Embedding Layer, serta pelatihan model hybrid CNN-LSTM. Evaluasi kinerja dilakukan menggunakan metrik akurasi, presisi, recall, F1-score, dan confusion matrix. Hasil dari penelitian ini tidak hanya memberikan gambaran distribusi sentimen publik (positif, netral, negatif), tetapi juga menunjukkan efektivitas model hybrid CNN-LSTM dalam mengolah data berbahasa Indonesia yang bersifat sekuensial. Penelitian ini diharapkan dapat memberikan kontribusi nyata dalam pengembangan sistem analisis sentimen berbasis NLP serta menjadi referensi dalam memahami opini publik terhadap kebijakan strategis nasional di era digital. Kata kunci: QRIS, Analisis Sentimen, Deep Learning, CNN, LSTM.
| Item Type: | Thesis (S1) |
|---|---|
| NIM/NIDN Creators: | 41522010079 |
| Uncontrolled Keywords: | QRIS, Analisis Sentimen, Deep Learning, CNN, LSTM. |
| 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 |
| Divisions: | Fakultas Ilmu Komputer > Informatika |
| Depositing User: | khalimah |
| Date Deposited: | 26 Feb 2026 06:50 |
| Last Modified: | 26 Feb 2026 06:50 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/101187 |
Actions (login required)
 |
View Item |
