Leveraging Temporal Feature Expansion for Enhanced Prediction of Naive Bayes and Random Forest Classification on SWSR - Dalam bentuk pengganti sidang - Artikel Jurnal
TRISULA DARMAWAN
Informasi Umum
Kode
25.04.400
Klasifikasi
000 - General Works
Jenis
Karya Ilmiah - Skripsi (S1) - Reference
Subjek
Data Science
Dilihat
80 kali
Informasi Lainnya
Abstraksi
Based on data from the Central Statistics Agency in the first semester of 2023, Central Java is one of the provinces in Indonesia with a percentage of poor people exceeding the national average rate. From these data, it can be understood that Central Java needs more attention to reduce poverty, including through effective data management of the Social Welfare Service Recipients (SWSR) database so that it can be the basis for developing social welfare service programs. Therefore, this research uses Naïve Bayes and Random Forest algorithms and combines them with a temporal feature expansion method that allows machine learning models to capture time-based patterns in the data so that the model can predict the classification of SWSR distribution in all districts/cities in Central Java for the next few years. The use of the time-based feature expansion method in machine learning classification has the advantage of identifying factors that affect future classification predictions, in contrast to time series or LSTM
Koleksi & Sirkulasi
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Pengarang
| Nama | TRISULA DARMAWAN |
| Jenis | Perorangan |
| Penyunting | Sri Suryani Prasetyowati, Yuliant Sibaroni |
| Penerjemah |
Penerbit
| Nama | Universitas Telkom, S1 Informatika |
| Kota | Bandung |
| Tahun | 2025 |
Sirkulasi
| Harga sewa | IDR 0,00 |
| Denda harian | IDR 0,00 |
| Jenis | Non-Sirkulasi |
