Analisis Opini Pengguna Aplikasi New PLN Mobile Menggunakan Text Mining
Bibliographic record
Abstract
Media sosial pada saat ini menjadi suatu media yang sangat populer untuk menyampaikan opini oleh masyarakat di Indonesia. Melalui media sosial pengguna dapat dengan mudah mengungkapkan pengalamannya terhadap suatu produk, salah satunya adalah aplikasi New PLN Mobile dari PT PLN (Persero). Aplikasi tersebut menjadi platform digital untuk memenuhi berbagai kebutuhan pelanggan terkait pelayanan ketenagalistrikan. Salah satu metode yang dapat dipakai untuk menganalisa opini pengguna adalah menggunakan text mining dengan pendekatan word cloud, network explorer, jenis emosi, dan analisis sentimen. Hasil penelitian menunjukkan bahwa analisis word cloud memberikan frekuensi komentar terkait keberadaan aplikasi, pengalaman pelanggan, fitur baru, informasi pelanggan, hingga interaksi dan komunikasi pada media sosial. Selanjutnya analisis network explorer menunjukkan bahwa kata yang saling berkaitan adalah “aplikasi pln mobile” dan “kemudahan layanan gangguan”. Analisa jenis emosi mengekspresikan sebagian besar pelanggan terkejut (surprise) dengan adanya aplikasi aplikasi New PLN Mobile. Analisis sentimen menunjukkan bahwa sebagian besar kluster pelanggan menunjukkan sentimen yang sangat positif terhadap keberadaan aplikasi New PLN Mobile.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
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".