Desain Model Artificial Intelligence Untuk Peningkatan Customer Experience & Penjualan Tenaga Listrik Melalui Penambahan Fitur Virtual Customer Support Pada Aplikasi PLN Mobile
Bibliographic record
Abstract
This study focuses on efforts to improve Customer Experience by incorporating Artificial Intelligence (AI)-based features into the PLN Mobile application. Given the challenges where many customers remain unaware of the variety of PLN products, the process of becoming a customer, and the boundaries of customer and PLN responsibilities, this research proposes the application of AI to improve customer interactions with PLN. The primary objective is to create an effective AI Chat Bot model for Customer Support, capable of efficiently serving both customers and non-customers. The methodology employed is the DM3 (Data-Driven Decision Management) approach, encompassing stages of data collection, pre-processing, AI model development, testing, launching, and further development. The primary focus is on integrating Natural Language Processing and Machine Learning technologies to enrich customer interactions. The results indicate that implementing an AI Chat Bot in PLN Mobile can enhance Customer Experience, facilitate communication, and gather valuable data for customer service activities. This Chat Bot feature not only improves service quality but also provides new information for PT PLN (persero) from customer activities, ultimately aiding in the increase of electricity sales. Keywords: Customer Experience, Artificial Intelligence, 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".