ANALISIS KUALITAS LAYANAN, CITRA PERUSAHAAN SERTA PERAN INTERNET BANKING BAGI KEPUASAN NASABAH (STUDI KASUS PADA BANK BRI UNIT TOMBATU)
Bibliographic record
Abstract
Di era yang semakin maju ini, banyak orang yang mulai menggunakan teknologi untuk berinteraksi salah satunya bertransaksi. Bertansaksi dalam perbankan kini dilakukan dengan mudah salah satunya menggunakan smartphone. Oleh sebab itu, perusahaan harus mampu bersaing dengan meningkatkan kualitas layanan untuk reputasi citra perusahaan yang baik di mata nasabah dengan dukungan peran Internet Banking demi terwujudnya kepuasan nasabah. Tujuan penelitian ini adalah untuk mengetahui Kualitas Layanan, Citra Perusahaan serta peran Internet Banking bagi kepuasan nasabah Bank BRI Unit Tombatu. Metode yang digunakan dalam penelitian ini adalah metode kuantitatif. Pengumpulan data dilakukan diantaranya kuesioner serta studi kepustakaan. Objek dalam penelitian ini Bank BRI Unit Tombatu, yang diambil sebanyak 100 responden. Teknik sampling yang digunakan yaitu teknik purposive sampling. Hasil dari penelitian ini berdasarkan hasil uji F diketahui artinya variabel kualitas layanan, citra perusahaan dan Internet Banking secara simultan berpengaruh signifikan terhadap keputusan pembelian. Berdasarkan hasil olah data, Variabel kualitas layanan secara parsial berpengaruh positif dan signifikan terhadap keputusan pembelian konsumen, kemudian Variabel Citra perusahaan secara parsial berpengaruh negative dan tidak signifikan terhadap kepuasan nasabah dan yang terakhir variabel Internet Banking secara parsial berpengaruh positif dan signifikan terhadap kepuasan nasabah. Kata Kunci: kualitas layanan, citra perusahaan, internet banking, kepuasan nasabah
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".