PENGARUH KESTABILAN EMOSI DAN LITERASI KEUANGAN TERHADAP PERILAKU KONSUMTIF MASYARAKAT RW 03 KELURAHAN TANJUNG BARAT
Bibliographic record
Abstract
ABSTRAK Tujuan Penelitian ini untuk mengetahahui Pengaruh Kestabilan emosi dan Literasi Keuangan Terhadap Perilaku Konsumtif Masyarakat RW 03 Kelurahan Tanjung Barat, Jakarta Selatan. Pada Metodologi Penelitian ini menggunakan jenis metode Field research yaitu metode study lapangan dengan mengadakan kunjungan langsung dan menyebar angket , juga menggunakan metode library research yaitu metode study pustaka melalui membaca jurnal ,skripsi dan buku-buku perpustakaan, sifat data penelitian b e r up a kualitatif. Jumlah Populasi sebanyak 1320 warga RW 03 Kelurahan Tanjung Barat, Jakarta, sedankan sampelnya menggunakan rumus slovin sebanyak 93 orang. Teknik pengumpulan data dalam penelitian ini menggunakan kuesioner yang diolah dengan menggunakan bantuan program SPSS versi 26. Analisis data menggunakan uji klasik berupa uji Normalitas, uji Multikolineritas, uji Heteroskedasitas, dan uji Ko Integrasi, Sedangkan uji statistiknya berupa analisis regresi linier berganda, uji korelasi , uji koefisien determinasi, Uji Hipotesis dan uji F. Hasil Penelitian menunjukkansecara parsial adalah adanya Pengaruh Kesetabilan emosi terhadap Prilaku Konsumtip,dan juga adanya pengaruh Literasi Keuangan terhadap Perilaku Konsumtif.Dan secara simultan adanya pengaruh Kesetabilan emosi dan Literasi Keuangan terhadap Prilaku Konsumtip.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".