ANNUR, AHMAD FAIZAL AMRI (2025) HYBRID RECOMMENDER SYSTEM INVESTASI REKSA DANA DENGAN DEMOGRAPHIC FILTERING DAN FUND PERFORMANCE METRICS. S1 thesis, Universitas Mercu Buana - Menteng.
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Abstract
Sistem rekomendasi investasi reksa dana dapat menjadi peran penting dalam membantu investor membuat keputusan yang lebih tepat berdasarkan demografi investor, profil risiko, dan riwayat kinerja reksa dana. Penelitian ini mengusulkan sebuah Hybrid Recommender System yang menggabungkan pendekatan Demographic Filtering dan Fund Performance Metrics untuk memberikan rekomendasi reksa dana yang sesuai dengan demografi dan profil risiko investor. Dalam penelitian ini, data investor dikelompokan berdasarkan tingkat pendapatan, kelompok usia, dan profil risiko. Sementara itu data kinerja reksa dana diukur menggunakan Compound Annual Growth Rate (CAGR) untuk jangka waktu 3 tahun dan Compound Monthly Growth Rate (CMGR) untuk kinerja bulanan selama 12 bulan terakhir. Berdasarkan hasil pengujian, performa paling optimal dan seimbang ketika reksa dana yang direkomendasikan adalah 2 dari 20 reksa dana dengan nilai precision 0.5038, recall 0.9878, dan f1-score 0.6673. Ini berarti bahwa 98.78% data pembelian reksa dana oleh investor sesuai dengan hasil rekomendasi, walaupun hanya 50.38% reksa dana yang direkomendasikan dibeli oleh investor atau bisa dibaca sebagai investor membeli setidaknya 1 dari 2 reksa dana yang direkomendasikan. Mutual fund investment recommendation systems play a crucial role in assisting investors in making more informed decisions based on investor demographics, risk profiles, and mutual fund performance history. This study proposes a Hybrid Recommender System that combines Demographic Filtering and Fund Performance Metrics to provide mutual fund recommendations aligned with investors' demographics and risk profiles. In this research, investor data is grouped based on income levels, age groups, and risk profiles. Meanwhile, mutual fund performance is measured using the Compound Annual Growth Rate (CAGR) for a 3-year period and the Compound Monthly Growth Rate (CMGR) for monthly performance over the last 12 months. Based on the test results, the most optimal and balanced performance was achieved when the recommended mutual funds were 2 out of 20 funds, with a precision of 0.5038, recall of 0.9878, and F1-score of 0.6673. This indicates that 98.78% of mutual fund purchases made by investors matched the recommendations, even though only 50.38% of the recommended funds were purchased by investors, meaning that investors bought at least one of the two recommended mutual funds.
| Item Type: | Thesis (S1) |
|---|---|
| NIM/NIDN Creators: | 41519110176 |
| Uncontrolled Keywords: | Hybrid Recommender, Demographic Filtering, Fund Performance Metrics, Simple Additive Weighting, Reksa Dana Hybrid Recommender, Demographic Filtering, Fund Performance Metrics, Simple Additive Weighting, Mutual Fund |
| 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: | FHADHILAH SHAFA ARISTA |
| Date Deposited: | 25 Feb 2025 03:04 |
| Last Modified: | 25 Feb 2025 03:04 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/94403 |
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