JARINGAN SARAF TIRUAN MEMPREDIKSI PENJUALAN MAKANAN DAN MINUMAN DENGAN MENGGUNAKAN METODE BACKPROPAGATION (STUDI KASUS : PONDOK JATI RESTO BINJAI
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
Pondok Jati Resto (PJR) is a cafe that provides a variety of foods and beverages that are sold to attract customers or potential customers. The number of food and beverages that have been sold, of course, PJR has data on sales of food and beverages. So far, sales data have only been seen from sales reports. It is of course very unfortunate that other data, for example, such as ordered food and beverage menus, can be used as an evaluation material for food and beverage needs that are often in demand. Food and drink is one of the most needed needs by humans. There are many types of food and drink that are made to fulfill the desire to try a food and drink. Apart from being at home, food and beverages can also be obtained at shops, stalls, restaurants, cafes and so on. The increasing number of population levels and the increasing popularity of the food and beverage business, of course, there are more and more food and beverage sellers circulating in several areas, one of which is Cafe Pondok Jati Resto. The application of artificial neural networks to predict the amount of food and beverages using Matlab software using the Backpropagation method can be applied in predicting the number of food and beverage sales. Based on the analysis process that has been carried out under the artificial neural network system using the Backpropagation method, it can identify data on the number of food and beverage sales, with test results or predictions of the average number of foods per year 20, 5 drinks and 19 snacks.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| 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