Clustering of PLN ULP Binjai Timur Customer Complaints using the K-Means Method
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
The large number of customer complaints received daily by PLN ULP Binjai Timur presents a challenge in providing responsive and accurate service. Irregularities in recording and grouping complaints mean that the available information is less than optimal for supporting decision-making. This study aims to group customer complaints based on similar characteristics for easier analysis. The method used is the K-Means algorithm, a clustering technique in data mining that divides data into several groups based on their proximity to the cluster center (centroid). The analysis was conducted through the Knowledge Discovery in Database (KDD) stages, which include data selection, transformation, and algorithm implementation using MATLAB software. The three main variables used in the grouping process were complaint type, complaint submission medium, and customer address. The implementation results in three main complaint clusters with distinct patterns, providing PLN with insight into the most frequently encountered problems, areas with high complaint rates, and the most frequently used reporting medium. These findings provide an important foundation for PLN in setting treatment priorities, improving service quality, and strengthening customer relationships. The application of the K-Means algorithm has proven effective as a systematic and practical solution for managing complex and large amounts of complaint data.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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