Pengelompokan Data Keluhan Masyarakat Terhadap Fasilitas Umum diKota Binjai Menggunakan Metode Clustering
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
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.000 | 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