Pengelompokan Data Keluhan Masyarakat Terhadap Fasilitas Umum diKota Binjai Menggunakan Metode Clustering
Bibliographic record
Abstract
Public Facilities in Binjai City are infrastructure that is provided free of charge that can be enjoyed by the community and is one of the vacation spots that does not need to spend a lot of money, but there are several infrastructure facilities that are not maintained, dirty and have damage from minor to the most severe, even infrastructure, so that it greatly affects the comfort of the community. In the process of maintaining public facilities in Binjai City in accordance with the Binjai City Regional Regulation Letter Number 1 of 2024 concerning public facilities used for public purposes, including for educational, health, worship, socio-cultural, sports and recreational activities (Hamzah, 2024). The Environmental Service of Binjai City really needs input from the community to continue to help maintain and care for the facilities provided so that the agency can handle and respond to community complaints such as a lot of garbage, dirty, rusty, muddy facilities and others as well as input reported by the community on the cleanliness of public facilities in Binjai City. Therefore, the agency needs a system using the clustering method that can manage community complaint data to be used as information that can assist the agency in taking quick action to deal with the problem of community complaints about public facilities in Binjai City. Based on the research conducted on the case experiment above from testing 20 data, there are 3 groups, namely group 1 there are 5 data and group 2 there are 9 data, and group 3 there are 6 data which can be known that in cluster 2 the group of public complaints about public facilities in Binjai City with public facilities (X) Studion Field, with complaints (Y) Becek, Banyak Sampah, & Berkarat, with Advice (Z) Repair & Maintain Cleanliness.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".