Diagnosis Gangguan Permasalahan Layanan Telkom menggunakan Metode Dempster Shafer
Bibliographic record
Abstract
The increasing complexity of public demand for telecommunication services, particularly internet services, has pushed PT. Telkom, as one of the state-owned enterprises (SOEs), to continuously enhance the quality of its services. One of its flagship products, Indihome, offers faster internet connectivity compared to dial-up services. However, Indihome has been frequently criticized by customers due to service disruptions. This indicates a need for developing effective strategies to address customer complaints. The primary issue faced by the public is the lack of knowledge regarding service disruptions, leading to difficulties in explaining the problem to technicians for repair. This research aims to develop a Telkom service disruption diagnosis system that can assist the public in identifying issues early without direct consultation with an expert. The system is developed using an expert system method, where information about service disruptions is processed to generate accurate diagnoses. With this system, customers can identify the type of disruption and provide clearer information to Telkom technicians. The research findings indicate that the most common disruptions are caused by faulty adapters or modems and disconnected configurations, with a density value of 54.49%. This system is expected to improve Telkom’s public service quality, minimize customer complaints, and expedite the repair process for Indihome services.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".