A Decision Support System To Determine The Location Of A New Sales Branch At Star East Shop With The Smart Method
Bibliographic record
Abstract
In a business world that is always dynamic and full of competition, business people must always think of ways to continue to survive and if possible develop their business scale in order to meet these business needs, there are many ways that can be taken, one of which is by conducting data analysis. . Bintang Timur shop is one of the businesses in the field of iron and building material shops in the city of stabat. Bintang Timur shop was founded in 2013 which continues to grow at this time. Equipped with the desire to meet the needs of users in the field of iron and building, Bintang Timur store continues to grow by adding various products. The profits obtained in this shop are also used to develop the business, one of which is the addition of goods sold. Based on the analysis that has been done, there are several obstacles faced by this eastern star shop. One of them is the process of finding a new sales branch location at the Bintang Timur store. In this study, a Decision Support System (DSS) will be built using the Simple Multiple Attribute Rating Technique (SMART) method which is a multi-criteria decision making technique based on each alternative consisting of of a number of criteria that have a value and each criterion. Based on the calculation results of the SMART method above, Tebasan (A1) is a new branch location at the Bintang Timur Store in Stabat City which is feasible with a value of 0.763.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".