PENERAPAN ALGORITMA C4.5 DALAM MENGUKUR TINGKAT KEPUASAN NASABAH PADA PT BANK MUAMALAT INDONESIA KCU MEDAN BARU BERBASIS WEB
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the field of service providers, sharia banking and conventional banking have differences in their characteristics which lie in the practice of running business operations, where operations are based on sharia principles, and this principle is the main attraction for customers to utilize sharia bank services. Quality of service is a key factor that will become a competitive advantage in today's banking world. This happens because the bank as a service company has the characteristic of being easy to imitate a product that has been marketed. The measurement method for determining customer satisfaction at Bank Muamalat is by applying data mining, where customer data that makes transactions will be inputted into the system and then processed using the C4.5 method with predetermined criteria. Data mining is a process of finding meaningful relationships, patterns and trends by examining large sets of data stored in storage using pattern recognition techniques. According to Algorithm C4.5 is an algorithm used to form a decision tree.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it