PENERAPAN DATA MINING PENGELOMPOKAN DATA PENGGUNA AIR BERSIH BERDASARKAN KELUHANNYA MENGGUNAKAN METODE CLUSTERING PADA PDAM LANGKAT
Bibliographic record
Abstract
Permasalahan pelanggan memang sangat kompleks, oleh karena itu harus ditangani secara baik, jelas, dan tuntas. Pelayanan yang baik dari suatu perusahaan dapat menunjukan profesionalisme perusahaan itu sendiri, artinya keseriusan, kepastian waktu, ketepatan waktu dan hasil kerja yang dapat dipertanggung jawabkan dalam menyelesaikan semua permasalahan dapat membuktikan kualitas suatu perusahaan. Clustering merupakan proses partisi satu set objek data ke dalam himpunan bagian yang disebut dengan cluster. Objek yang di dalam cluster memiliki kemiripan karakteristik antar satu sama lainnya dan berbeda dengan cluster yang lain. Clustering sangat berguna dan bisa menemukan group atau kelompok yang tidak dikenal dalam data. Dari 2056 data keluhan pelanggan iperoleh hasil Cluter 1 yaitu 12, 5, 5, pada cluster 2 yaitu 4, 5, 5 dan cluster 3 yaitu 8, 2, 2. Dengan jumlah anggota cluster 1 883 anggota, cluster 2 635 anggota dan cluster 3 yaitu 538 anggota. Dari hasil cluster Matlab tersebut terdapat kesamaan hasil yaitu jenis keluhan pada cluster 1 dengan cluster 2 yaitu kode 5 jenis keluhan pipa bocor dengan peanganan kerusakan menyambung pipa air (gibout join).
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.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".