Sistem Pendukung Keputusan Menentukan Pemilihan Lokasi untuk Cabang Baru Toko Liv Beauty Cosmetic menggunakan Metode TOPSIS
Bibliographic record
Abstract
Toko Liv Beauty is one of the business players in the beauty sector that is developing in North Sumatra, specifically in the West Binjai sub-district, Binjai City. As a store that provides various beauty products, this research aims to assist Toko Liv Beauty in determining a strategic location for opening a new branch using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The TOPSIS method was chosen for its ability to analyze alternatives based on positive and negative ideal solutions objectively. A case study was conducted at three potential locations in Binjai: Binjai City, Binjai South, and Binjai North, considering five main criteria: population density, ease of transportation access, number of competitors, rental costs, and building area. The analysis process involves normalizing the decision matrix, calculating weighted values, identifying ideal solutions, and determining alternative preferences. The analysis results show that the location with the highest preference is Binjai North (1), followed by Binjai South (0.5885) and Binjai City (0). Thus, Binjai North is recommended as a strategic location for opening a new branch of Toko Liv Beauty. The implementation of the TOPSIS method in this research is expected to contribute to more effective data-driven decision-making for the business development of Toko Liv Beauty.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".