Sistem Pendukung Keputusan Menentukan Pemilihan Lokasi untuk Cabang Baru Toko Liv Beauty Cosmetic menggunakan Metode TOPSIS
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
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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